Papers by Liu Yang

ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction (2026.findings-acl)

Challenge:	Existing studies on hate speech detection have failed to answer this question.
Approach:	They propose a dataset for testing the behaviors of automated content moderators against content policies.
Outcome:	The proposed dataset includes hateful and non-hateful examples matching the 41 community standards guideline policies of Facebook.

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Wenda Liu, Song Zhigang, Shuai Nie, Guangyao Liu, Lisung Chen, Binyu Yang, Yaran Chen, Peng Zhou, Hongzhen Wang, Yuchen Liu, Wenyue Hu, Jiaming Xu, Runyu Shi, Ying Huang

Progra: Progress-Aware Reinforcement Learning for Multi-Turn Function Calling (2026.findings-acl)

Challenge:	ProUIE improves universal information extraction (UIE) without external information . many LLM-based methods rely on extra schema cues, external resources or complex alignment and verification pipelines .
Approach:	They propose a Macro-to-Micro progressive learning approach that improves UIE without external information.
Outcome:	ProUIE outperforms instruction-tuned baselines on average for NER and RE while using a smaller backbone.

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Huacan Chai, Zijie Cao, Maolin Ran, Yingxuan Yang, Jianghao Lin, Xin Peng, Hairui Wang, Renjie Ding, Ziyu Wan, Muning Wen, Weiwen Liu, Weinan Zhang, Fei Huang, Ying Wen

Challenge:	Existing methods for multi-turn function calling are limited by redundancy and lack explicit integration of progress awareness into training.
Approach:	They propose a framework that explicitly integrates progress awareness into LLM training for multi-turn function calling.
Outcome:	Empirical results show that Progra outperforms existing methods on two public benchmarks.

ETAS: Zero-Shot Transformer Architecture Search via Network Trainability and Expressivity (2024.findings-acl)

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Jiechao Yang, Yong Liu

L2Dir: Integrating L_2-Norm and Directional Alignment for Unsupervised Contrastive Representation Learning in Multimodal Retrieval (2026.acl-long)

Challenge:	Existing Transformer Architecture Search methods are limited to computer vision and natural language processing tasks.
Approach:	They propose a Transformer Architecture Search proxy that measures trainability and expressivity of Transformer networks separately and integrates it into an effective regularized evolution framework to demonstrate its efficacy.
Outcome:	The proposed proxy can achieve higher correlation with the true performance of Transformer networks on computer vision and natural language processing tasks.

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Tianyu Zong, Rui Dai, Hongzhu Yi, Yuanxiang Wang, Zhenghao Zhang, Zhenyu Guan, Yujia Yang, Bingkang Shi, Yueyang Ding, Xiangxiang Chu, Kaikui Liu, Jungang Xu

Want To Reduce Labeling Cost? GPT-3 Can Help (2021.findings-emnlp)

Challenge:	Existing approaches to multimodal representation learning focus on directional alignment and embedding magnitudes (L2-norm) however, these methods often fail to account for the intrinsic role of L2-norm in the contrastive process.
Approach:	They propose a plug-and-play framework that optimizes L2-norm alignment and Directional consistency jointly.
Outcome:	The proposed framework achieves consistent and significant performance gains over established baselines across 95 tasks using UniIR and VLM2Vec-V2 frameworks.

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Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, Michael Zeng

OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction (2025.acl-long)

Challenge:	Data annotation is labor-intensive and time-consuming for many NLP tasks.
Approach:	They propose to use GPT-3 to train models which are deployed for inference . they propose to combine pseudo labels from GPT3 with human labels .
Outcome:	The proposed method can be generalizable to many practical applications.

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Haonan Zhang, Run Luo, Xiong Liu, Yuchuan Wu, Ting-En Lin, Pengpeng Zeng, Qiang Qu, Feiteng Fang, Min Yang, Lianli Gao, Jingkuan Song, Fei Huang, Yongbin Li

UTMath: A Benchmark for Math Evaluation with Unit Test (2025.findings-emnlp)

Challenge:	Existing methods focus on replicating dialogues in textual form, neglecting the role’s voice traits as a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios.
Approach:	They propose a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency.
Outcome:	The proposed model exhibits role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses.

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Bo Yang, Qingping Yang, Yingwei Ma, Runtao Liu

ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation (2022.emnlp-main)

Challenge:	Prevailing benchmarks for mathematical reasoning include MATH and AIME . predicated on single-instantiation problems with fixed numbers, these models leave generalization on isomorphic problem variants untested.
Approach:	They propose a mathematical reasoning benchmark that quantifies solution accuracy and solution space generality.
Outcome:	The proposed model solves 1,053 problems spanning 9 mathematical domains . the best-performing model solved only 32.57% of the problems .

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Shuohang Wang, Ruochen Xu, Yang Liu, Chenguang Zhu, Michael Zeng

Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs (2025.emnlp-main)

Challenge:	Existing datasets only annotate a binary label for each sentence pair. Existing models only annnotate binary labels for each phrase pair.
Approach:	They propose a novel binary paraphrase classification task that annotates the degree of paraphrase between sentences and a new annotation schema that labels the minimum spans of tokens in a sentence that don't have the corresponding paraphrases in the other sentence.
Outcome:	The proposed dataset can be used to train an automatic scorer for language generation evaluation.

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Jun Bai, Minghao Tong, Yang Liu, Zixia Jia, Zilong Zheng

SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems (2026.acl-long)

Challenge:	Context faithfulness is essential for reliable reasoning in context-dependent scenarios.
Approach:	They propose a method that identifies and fine-tunes context-faithful experts . they propose 'context-faither fine- tuning' which selectively fine- tunes them .
Outcome:	The proposed method identifies experts with specialization in context utilization and improves context grounding.

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Yuzhe Zhang, Feiran Liu, Yi Shan, Xinyi Huang, Xin Yang, Yueqi Zhu, Xuxin Cheng, Cao Liu, Ke Zeng, Terry Jingchen Zhang, Wenyuan Jiang

Challenge:	Existing benchmarks conflate coordination ability with role-based priors.
Approach:	They propose a role-free benchmark for evaluating free-form collaboration under information silos.
Outcome:	The proposed benchmark systematically probes coordination capabilities under information silos using 54 configurations and 3 frontier LLMs.

MoNMT: Modularly Leveraging Monolingual and Bilingual Knowledge for Neural Machine Translation (2024.lrec-main)

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Jianhui Pang, Baosong Yang, Derek F. Wong, Dayiheng Liu, Xiangpeng Wei, Jun Xie, Lidia S. Chao

GLoCIM: Global-view Long Chain Interest Modeling for news recommendation (2025.coling-main)

Challenge:	Existing models for multi-domain translation tasks only use monolingual data, whereas bilingual data is indispensable for improving the models.
Approach:	They propose a modular strategy that facilitates the cooperation of monolingual and bilingual knowledge in translation tasks by avoiding catastrophic forgetting.
Outcome:	The proposed model exhibits superior generalization and robustness over the conventional approach.

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Zhen Yang, Wenhui Wang, Tao Qi, Peng Zhang, TianYun Zhang, Ru Zhang, Jianyi Liu, Yongfeng Huang

VoxpopuliTTS: a large-scale multilingual TTS corpus for zero-shot speech generation (2025.coling-main)

Challenge:	Recent efforts to extract local subgraph information from click graphs have hindered collaboratively utilizing global click graph information.
Approach:	They propose a global-view long chain interests model that models a click graph with neighbor interest to enhance news recommendation.
Outcome:	The proposed method surpasses baseline methods on two real-world datasets.

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Wenrui Liu, Jionghao Bai, Xize Cheng, Jialong Zuo, Ziyue Jiang, Shengpeng Ji, Minghui Fang, Xiaoda Yang, Qian Yang, Zhou Zhao

PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents (2024.eacl-long)

Challenge:	Existing multilingual TTS datasets are limited in speech generation fields due to lack of quality data.
Approach:	They propose to use 30,000 hours of high-quality speech data across 3 languages . they filter out low-quality text-text pairs and concatenate short transcripts .
Outcome:	The proposed dataset comprises 30,000 hours of high-quality speech data, across 3 languages with multiple speakers and styles, suitable for various speech tasks such as TTS and ASR.

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Simeng Sun, Yang Liu, Shuohang Wang, Dan Iter, Chenguang Zhu, Mohit Iyyer

Assessing Multilingual Fairness in Pre-trained Multimodal Representations (2022.findings-acl)

Challenge:	Using chain-of-thought prompting, large language models perform better on complex reasoning tasks.
Approach:	They propose a prompting framework that decomposes a question into a sequence of actions and executes them over the document to obtain the answer.
Outcome:	The proposed framework outperforms zero-shot and chain-of-thought prompting on a QuALITY dataset . it proposes a plan based on actions mined from a training set and executes it step by step .

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Jialu Wang, Yang Liu, Xin Wang

Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs (2022.naacl-main)

Challenge:	Recent pre-trained multimodal models have shown exceptional capabilities towards connecting images and natural language.
Approach:	They propose two new fairness notions for pre-trained multimodal models that consider language as the fairness recipient.
Outcome:	The proposed models can be generalized to multilingualism by cross-lingual alignment . the results show that the models are individually fair across languages .

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Songlin Yang, Wei Liu, Kewei Tu

Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning (2024.findings-acl)

Challenge:	Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) are widely used structured models.
Approach:	They use tensor rank decomposition to reduce computational complexities for a subset of FGGs subsuming HMMs and PCFGs.
Outcome:	The proposed model performs better on HMM modeling and unsupervised PCFG parsing than previous work.

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Yang Wu, Chenghao Wang, Ece Gumusel, Xiaozhong Liu

GQLBench: A Large-Scale Cross-Domain, Cross-Dialect Benchmark for NL2GQL (2026.acl-long)

Challenge:	Recent years have witnessed a substantial increase in the demand for legal services, especially for individuals with modest means.
Approach:	They propose a diagnostic legal large language model which uses adaptive lawyer-like diagnostic questions to collect additional case information and then provides high-quality feedback.
Outcome:	The proposed model surpasses classical LLMs by providing outstanding performance and a remarkable user experience in the legal domain.

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Yanning Su, Yuhang Zhou, Yang Fang, Sen Liu, Guangnan Ye, Hongfeng Chai

A Dialogue-based Information Extraction System for Medical Insurance Assessment (2021.findings-acl)

Challenge:	despite growing interest in NL2GQL, benchmarking progress has been constrained by the lack of resources that are simultaneously large-scale, cross-domain, and cross-dialect.
Approach:	They propose a framework that integrates NL2SQL-to-NL2GQL conversion with graph-native data generation.
Outcome:	The proposed framework supports execution-based evaluation on Cypher and ISO-GQL, covering hundreds of graph databases and over 20k natural language questions for each dialect.

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Shuang Peng, Mengdi Zhou, Minghui Yang, Haitao Mi, Shaosheng Cao, Zujie Wen, Teng Xu, Hongbin Wang, Lei Liu

Language Models as Inductive Reasoners (2024.eacl-long)

Challenge:	a new system that integrates advanced NLP technologies for medical insurance assessment is proposed . the average time cost of the procedure is reduced from 55 minutes to 35 minutes .
Approach:	They propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment.
Outcome:	The proposed system reduces the time cost of the procedure from 55 minutes to 35 minutes and saves 30% human resources cost compared with the previous offline procedure.

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Zonglin Yang, Li Dong, Xinya Du, Hao Cheng, Erik Cambria, Xiaodong Liu, Jianfeng Gao, Furu Wei

DSP: Discriminative Soft Prompts for Zero-Shot Entity and Relation Extraction (2023.findings-acl)

Challenge:	Inductive reasoning is a core component of human intelligence.
Approach:	They propose a task to induce natural language rules from natural language facts using natural language as representation for knowledge instead of formal language.
Outcome:	The proposed task surpasses baselines in both automatic and human evaluations.

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Bo Lv, Xin Liu, Shaojie Dai, Nayu Liu, Fan Yang, Ping Luo, Yue Yu

CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-long)

Challenge:	Prompt-based methods have shown their efficacy in transferring general knowledge within pre-trained language models (PLMs) however, when applied to zero-shot entity and relation extraction, they struggle with the limited coverage of verbalizers to labels and the slow inference speed.
Approach:	They propose a method which reformulates zero-shot tasks into token discrimination tasks without having to construct verbalizers.
Outcome:	The proposed method outperforms baselines on two zero-shot entity recognition datasets with higher inference speed and achieves 7.5% improvement over previous state-of-the-art models on Wiki-ZSL and FewRel.

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Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao

A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos (2025.findings-acl)

Challenge:	Cross-modal retrieval tasks are used to retrieve data from one modality or another based on a query from another modality.
Approach:	They propose a generative cross-modal retrieval framework based on coarse-to-fine semantic modeling . they propose combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation.
Outcome:	The proposed framework achieves excellent performance and efficiency in multimodal retrieval tasks.

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Yang Yao, Xuan Tong, Ruofan Wang, Yixu Wang, Lujundong Li, Liang Liu, Yan Teng, Yingchun Wang

Reinforcement Learning for Diffusion LLMs via Energy-Based Gibbs Alignment (2026.acl-long)

Challenge:	Large Reasoning Models (LRMs) have advanced beyond traditional Large Language Models, yet they pose heightened safety risks.
Approach:	They propose a first jailbreak attack targeting Large Reasoning Models . they exploit a Chaos Machine component to transform attack prompts with diverse one-to-one mappings based on the reasoning chain .
Outcome:	The proposed attack exploits the unique vulnerabilities of LRMs by integrating a Chaos Machine. success rates of the mousetrap attack are as high as 96%, 86% and 98% respectively.

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Yijia Fan, Jing Yang, Mingyu Liu, Kaitong Cai, Jian Wang, Keze Wang, Jusheng Zhang

VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models (2024.emnlp-main)

Challenge:	Diffusion Large Language Models (dLLMs) offer parallel decoding and bidirectional context modeling . aligning dLLms with reinforcement learning (RL) remains a challenge .
Approach:	They propose a variational framework that reformulates RL for dLLMs as a distribution matching problem.
Outcome:	The proposed framework reformulates RL for dLLMs as a distribution matching problem.

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Yifei Liu, Jicheng Wen, Yang Wang, Shengyu Ye, Li Lyna Zhang, Ting Cao, Cheng Li, Mao Yang

Composable Text Controls in Latent Space with ODEs (2023.emnlp-main)

Challenge:	Recent research has focused on pushing weight-only quantization to extremely low-bit due to numerical representation limitations.
Approach:	They propose a vector-based quantization approach that pushes LLMs to extremely low-bit . they propose scalar-based weight quantization that reduces memory requirements and optimizes storage costs .
Outcome:	The proposed method reduces model quantization perplexity by 0.01-0.34 on LLaMA-2, 0.38-0.68 on mistral-7B, 4.41-7.34, on llaMA-3 on QA tasks on average.

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Guangyi Liu, Zeyu Feng, Yuan Gao, Zichao Yang, Xiaodan Liang, Junwei Bao, Xiaodong He, Shuguang Cui, Zhen Li, Zhiting Hu

Scaling Laws for Code: Every Programming Language Matters (2026.findings-acl)

Challenge:	Existing approaches to composable text operations often require plug-and-play . a single LM can perform arbitrary text operation composition in the latent space .
Approach:	They propose an efficient approach for composable text operations in the latent space of text . they connect pretrained LMs to the laten space and adapt them to the space .
Outcome:	The proposed approach improves on existing methods in the latent space of text.

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Jian Yang, Shuyue Guo, Linzheng Chai, Wei Zhang, Aishan Liu, Chuan Hao, Zhoujun Li, Xin Zhao, Xianglong Liu, Weifeng Lv, Bryan Dai

CriticBench: Benchmarking LLMs for Critique-Correct Reasoning (2024.findings-acl)

Challenge:	Existing studies focus on language-agnostic settings, neglecting the inherently multilingual nature of modern software development.
Approach:	They propose a proportion-dependent scaling law that prioritizes high-utility languages . they propose PLs to have varying effects during pre-training that affect model performance .
Outcome:	The proposed scaling law is based on 1000+ experiments across multiple languages and models.

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Zicheng Lin, Zhibin Gou, Tian Liang, Ruilin Luo, Haowei Liu, Yujiu Yang

Model Composition for Multimodal Large Language Models (2024.acl-long)

Challenge:	CriticBench is a benchmark designed to assess LLMs’ abilities to critique and refine their reasoning across a variety of tasks.
Approach:	They propose a benchmark to assess LLMs' ability to critique and correct reasoning across a variety of tasks.
Outcome:	The proposed benchmark examines the performance of 17 large language models in generation, critique, and correction reasoning.

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Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu

Fantastic Expressions and Where to Find Them: Chinese Simile Generation with Multiple Constraints (2023.acl-long)

Challenge:	Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities.
Approach:	They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters.
Outcome:	The proposed model retains the modal understanding capabilities of each original model.

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Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Xiangpeng Wei, Zhengyuan Liu, Jun Xie

Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization (2026.findings-acl)

Challenge:	Existing attempts to generate similes as context-free tasks are not suitable for simile generation . however, simile generated under such settings might be undesirable, we argue .
Approach:	They propose a model to generate a simile with multiple simile elements . they propose to use a vehicle retrieval module to obtain the explicable comparison .
Outcome:	The proposed model can generate a simile with multiple simile elements, e.g., context and vehicle.

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Jiantong Jiang, Peiyu Yang, Rui Zhang, Feng Liu

A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification (P18-2)

Challenge:	Large language models (LLMs) are a powerful tool for high-performance inference serving.
Approach:	They focus on system-aware KV infrastructure for serving LLMs . they analyze cross-behavior co-design affinity and behavior-objective links .
Outcome:	The proposed key-value (KV) cache is crucial for low-latency, high-throughput LLM inference serving.

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Zeyang Lei, Yujiu Yang, Min Yang, Yi Liu

Task-Driven and Experience-Based Question Answering Corpus for In-Home Robot Application in the House3D Virtual Environment (2022.lrec-1)

Challenge:	Existing sentiment classification approaches do not fully exploit sentiment linguistic knowledge.
Approach:	They propose a Multi-sentiment-resource Enhanced Attention Network to integrate sentiment linguistic knowledge into the deep neural network via attention mechanisms.
Outcome:	The proposed network captures sentiments from different representation sub-spaces, and is superior to strong competitors.

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Zhuoqun Xu, Liubo Ouyang, Yang Liu

HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)

Challenge:	Question answering is an important part of natural language processing (NLP)
Approach:	They propose to use TEQA to investigate the ability of agent task experience understanding for the long-term household task.
Outcome:	The proposed corpus aims to investigate the ability of task experience understanding of agents for the daily question answering scenario on the ALFRED dataset.

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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li

Do Large Language Models excel in Complex Logical Reasoning with Formal Language? (2025.emnlp-main)

Challenge:	Existing top-k attention methods struggle to strike a balance between efficiency and accuracy.
Approach:	They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention.
Outcome:	The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy.

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Jin Jiang, Jianing Wang, Yuchen Yan, Yang Liu, Jianhua Zhu, Mengdi Zhang, Liangcai Gao

LLMs as Lab Engineers: A Benchmark for Analytical Method Lifecycle Management (2026.findings-acl)

Challenge:	Existing studies on LLMs have focused on formal language, but evaluations of their performance are limited.
Approach:	They propose to use a formal language to evaluate LLMs across logical reasoning problems using formal languages.
Outcome:	The proposed model outperforms Instruct models in three dimensions, taxonomy of tasks, and format of trajectories, and achieves the best generalization performance across other languages.

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Xiaoyi Chen, Mahsa Monshizadeh, Chaoqi Zhang, Jianjun Lang, Yang Wu, Genevieve Mortensen, Xiaozhong Liu, Haixu Tang

Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts (2024.findings-acl)

Challenge:	General-purpose commercial models outperform domain-specialized ones, while RAG and reasoning significantly improve performance.
Approach:	They propose a benchmark to evaluate LLMs' capabilities in analytical chemistry scenarios.
Outcome:	The proposed framework outperforms existing benchmarks focused on factual knowledge and provides practical guidance for analytical chemistry challenges.

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Ganesh Jawahar, Haichuan Yang, Yunyang Xiong, Zechun Liu, Dilin Wang, Fei Sun, Meng Li, Aasish Pappu, Barlas Oguz, Muhammad Abdul-Mageed, Laks Lakshmanan, Raghuraman Krishnamoorthi, Vikas Chandra

PUNR: Pre-training with User Behavior Modeling for News Recommendation (2023.findings-emnlp)

Challenge:	Neural architecture search (NAS) uses weight-sharing supernets to generate diverse subnetworks without retraining.
Approach:	They propose a weight-sharing supernet that leverages mixture-of-experts to enhance supernet model expressiveness with minimal training overhead.
Outcome:	The proposed method achieves state-of-the-art (SoTA) performance in NAS for fast machine translation models, surpassing NAS-BERT and AutoDistil across various model sizes.

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Guangyuan Ma, Hongtao Liu, Xing W, Wanhui Qian, Zhepeng Lv, Qing Yang, Songlin Hu

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models (2025.findings-emnlp)

Challenge:	Existing news recommendation methods use pre-trained language models to produce news vectors and user vectors.
Approach:	They propose an unsupervised pre-training paradigm with two tasks for user behavior modeling.
Outcome:	The proposed model improves on the real-world news benchmark.

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Yuhan Kang, Yang Shi, Mei Wen, Jun He, Jianchao Yang, Zeyu Xue, Jing Feng, Xinwang Liu

Neuron-Level Differentiation of Memorization and Generalization in Large Language Models (2025.emnlp-main)

Challenge:	a novel post-training pruning method relies on the Hessian matrix to perform pruning . current pruning methods are computationally intensive and lack performance due to second-order derivative calculations.
Approach:	They propose a Hessian-free weight pruning method that reduces computational burden . they use an Exponentially Weighted Moving Average technique to bypass weight sorting .
Outcome:	The proposed method achieves hardware-efficient model compression by eliminating computational intensive calculations.

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Ko-Wei Huang, Yi-Fu Fu, Ching-Yu Tsai, Yu-Chieh Tu, Tzu-ling Cheng, Cheng-Yu Lin, Yi-Ting Yang, Heng-Yi Liu, Keng-Te Liao, Da-Cheng Juan, Shou-De Lin

USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026.acl-long)

Challenge:	Existing models exhibit memorization and generalization behaviors in ways that are not easily interpretable or controllable.
Approach:	They propose to use a GPT-2 and LLaMA-3.2 model to identify distinct neuron subsets responsible for each behavior to steer the model toward memorization or generalization.
Outcome:	The proposed models show that inference-time interventions on these neurons can steer the model’s behavior toward memorization or generalization.

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Baolin Zheng, Guanlin Chen, Qingyang Teng, Hongqiong Zhong, Yingshui Tan, Zhendong Liu, Weixun Wang, Jiaheng Liu, Jian Yang, Huiyun Jing, Jincheng Wei, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang

Lookahead Q-Cache: Achieving More Consistent KV Cache Eviction via Pseudo Query (2025.emnlp-main)

Challenge:	Existing safety benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale and the oversight of complex modality combinations.
Approach:	They propose a framework that covers 61 risk categories across four modality interactions to address this gap.
Outcome:	The proposed framework covers 61 risk categories across four distinct modality interactions.

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Yixuan Wang, Shiyu Ji, Yijun Liu, Yuzhuang Xu, Yang Xu, Qingfu Zhu, Wanxiang Che

Challenge:	Existing KV cache eviction methods prune tokens using prefilling-stage attention scores, causing inconsistency with actual inference queries.
Approach:	They propose a lookahead q-cache framework that generates low-cost pseudo lookaheaded queries to better approximate the true decoding-stage queries.
Outcome:	The proposed framework outperforms existing methods on LongBench and Needle-in-a-Haystack benchmarks and can be flexibly combined to yield further improvements.

Reinforced Efficient Reasoning via Semantically Diverse Exploration (2026.acl-long)

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Ziqi Zhao, Zhaochun Ren, Jiahong Zou, Liu Yang, Zhiwei Xu, Xuri Ge, Zhumin Chen, Xinyu Ma, Daiting Shi, Shuaiqiang Wang, Dawei Yin, Xin Xin

Shared-Private Bilingual Word Embeddings for Neural Machine Translation (P19-1)

Challenge:	Existing methods for reinforcement learning with verifiable rewards suffer from limited exploration diversity and inefficient reasoning.
Approach:	They propose a method that rewards concise and correct reasoning while penalizing unnecessarily long reasoning chains.
Outcome:	Extensive experiments on Qwen and Llama models validate the effectiveness and efficiency of ROSE.

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Xuebo Liu, Derek F. Wong, Yang Liu, Lidia S. Chao, Tong Xiao, Jingbo Zhu

Beyond Function-Level Search: Repository-Aware Dual-Encoder Code Retrieval with Adversarial Verification (2025.findings-emnlp)

Challenge:	Word embedding is central to neural machine translation, but indirectly interfaces with other layers, making them comparatively isolated.
Approach:	They propose a shared-private bilingual word embedding which gives a closer relationship between the source and target embedders and reduces the number of model parameters.
Outcome:	The proposed model improves on 5 language pairs belonging to 6 different language families and written in 5 different alphabets and significantly reduces model parameters.

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Aofan Liu, Song Shiyuan, Haoxuan Li, Cehao Yang, Yiyan Qi

GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models (2026.acl-long)

Challenge:	escalating complexity of modern codebases has intensified the need for code retrieval systems capable of interpreting cross-component change intents.
Approach:	RepoAlignBench is a benchmark designed to evaluate repository-level code retrieval . the benchmark proposes an adversarial reflection-augmented dual-tower architecture .
Outcome:	The proposed framework achieves 12.2% Top-5 Accuracy and 7.1% Recall improvements over state-of-the-art benchmarks.

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Zhiwen Ruan, Yichao Du, Jianjie Zheng, Longyue Wang, Yun Chen, Peng Li, Jinsong Su, Yang Liu, Guanhua Chen

TellWhisper: Tell Whisper Who Speaks When (2026.acl-long)

Challenge:	Existing adapter-based transfer methods treat instruction-tuned models as passive targets . direct fine-tuning can disrupt this delicate balance and lead to instability or performance degradation.
Approach:	They propose a framework that incorporates instruction-level guidance into task adaptation.
Outcome:	The proposed framework outperforms direct fine-tuning and representative transfer-based baselines while maintaining robust generalization and favorable test-time scaling behavior.

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Yifan Hu, Peiji Yang, Zhisheng Wang, Yicheng Zhong, Rui Liu

RoadMapper: A Multi-Agent System for Roadmap Generation of Solving Complex Research Problems (2026.findings-acl)

Challenge:	Existing approaches decouple temporal modeling and speaker modeling when addressing 'when' and 'who' . a new framework that couples temporal structure with speaker dynamics is proposed to address these limitations .
Approach:	They propose a framework that couples temporal and speaker identity within the speech encoder . they propose TS-RoPE, a time-speaker rotary positional encoding that partitions Query/Key channels into temporal, speaker subspaces and applies region-specific rotations to align "when" and "who" cues in selfattention.
Outcome:	The proposed framework couples temporal structure with speaker dynamics in speech encoder . it uses frame-level speaker activity to estimate speaker-activity estimates .

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Jiacheng Liu, Zichen Tang, Zhongjun Yang, Xinyi Hu, Xueyuan Lin, Linwei Jia, Ruofei Bai, Rongjin Li, Shiyao Peng, Haocheng Gao, Haihong E

Personality-Guided Code Generation Using Large Language Models (2025.acl-long)

Challenge:	Existing tools to generate structured content for research tasks are limited in their ability to generate high-quality roadmaps.
Approach:	They propose a benchmark to evaluate the ability of large language models (LLMs) to generate high-quality roadmaps for solving complex research problems.
Outcome:	The proposed system can improve LLMs’ ability for roadmap generation while saving 84% of the time required by human experts.

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Yaoqi Guo, Zhenpeng Chen, Jie M. Zhang, Yang Liu, Yun Ma

Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation (2021.acl-long)

Challenge:	Existing studies have shown that personality-guided code generation improves software development outcomes when individuals are assigned tasks that match their personality types.
Approach:	They evaluate how emulating personality traits appropriate to the coding tasks affects LLM performance by using seven widely adopted LLMs.
Outcome:	The proposed approach improves pass rates in 23 out of 28 LLM-dataset combinations, while emulating personality traits can be easily integrated with other prompting strategies to further boost performance.

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Xin Liu, Baosong Yang, Dayiheng Liu, Haibo Zhang, Weihua Luo, Min Zhang, Haiying Zhang, Jinsong Su

HTMuon: Improving Muon via Heavy-Tailed Spectral Correction (2026.findings-acl)

Challenge:	Existing methods to pretrain language models are limited by one-size-fits-all vocabulary . embeddings of mismatch tokens can be efficiently initialized in downstream tasks .
Approach:	They propose to extend pretrain-finetune pipeline with an embedding transfer step . plug-and-play embeddable generator is introduced to generate any input token .
Outcome:	The proposed approach allows for more efficient and better performed NLG models.

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Tianyu Pang, Yujie Fang, Zihang Liu, Shenyang Deng, Lei Hsiung, Shuhua Yu, Yaoqing Yang

ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models (2026.acl-long)

Challenge:	Muon’s orthogonalized update rule suppresses the emergence of heavy-tailed weight spectra and over-emphasizes the training along noise-dominated directions.
Approach:	They propose to preserve Muon's ability to capture parameter interdependencies while producing heavier-tailed updates and inducing heavier-tail weight spectra.
Outcome:	The proposed algorithm suppresses the emergence of heavy-tailed weight spectra and over-emphasizes training along noise-dominated directions.

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Jincheng Liu, Sijun He, Jingjing Wu, Xiangsen Wang, Yang Chen, Zhaoqi Kuang, Siqi Bao, Yuan Yao

HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System (2022.coling-1)

Challenge:	Existing evaluation frameworks focus on isolated question-answering tasks that may not capture the essential aspects of strategic reasoning.
Approach:	They evaluate 13 large language models across over 800 games in chess . they use a chessian-based framework to test strategic reasoning and pattern recognition .
Outcome:	The proposed framework improves performance and basic understanding of large language models.

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Zhanyu Ma, Jian Ye, Xurui Yang, Jianfeng Liu

SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities (2022.acl-long)

Challenge:	Existing methods to train task-oriented dialogue systems in monolingual datasets are expensive to build.
Approach:	They propose a hierarchical framework to classify intents in high-level and slot filling in low-level . they incorporate sentence-level alignment among different languages to enhance intent detection .
Outcome:	The proposed framework achieves the performance on a public task-oriented dialog dataset.

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Hsiang-Sheng Tsai, Heng-Jui Chang, Wen-Chin Huang, Zili Huang, Kushal Lakhotia, Shu-wen Yang, Shuyan Dong, Andy Liu, Cheng-I Lai, Jiatong Shi, Xuankai Chang, Phil Hall, Hsuan-Jui Chen, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-yi Lee

Research Replication Prediction Using Weakly Supervised Learning (2020.findings-emnlp)

Challenge:	Existing evaluation methods for transfer learning are limited in speech research . authors show that pre-trained models transfer well across multiple tasks .
Approach:	They propose a benchmark to evaluate pre-trained models by increasing task diversity and difficulty over SUPERB.
Outcome:	The proposed benchmark increases task diversity and difficulty over SUPERB-SG.

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Tianyi Luo, Xingyu Li, Hainan Wang, Yang Liu

ReviewRL: Towards Automated Scientific Review with RL (2025.emnlp-main)

Challenge:	Existing methods to predict scientific claims’ replicability use only hand-extracted statistics features without utilizing research papers’ text information.
Approach:	They propose two weakly supervised learning approaches that use automatically extracted text information of research papers to improve the prediction accuracy of research replication using both labeled and unlabeled datasets.
Outcome:	The proposed methods achieve an accuracy of 75.76% over real-world datasets.

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Sihang Zeng, Kai Tian, Kaiyan Zhang, Yuru Wang, Junqi Gao, Runze Liu, Sa Yang, Jingxuan Li, Xinwei Long, Jiaheng Ma, Biqing Qi, Bowen Zhou

Challenge:	Existing automated review systems struggle with factual accuracy, rating consistency, and analytical depth.
Approach:	They propose a framework for generating comprehensive and factually grounded scientific paper reviews using supervised fine-tuning and reinforcement learning.
Outcome:	The proposed framework outperforms existing methods on ICLR 2025 papers.

Do LLMs Catch Their Own Mistakes? A Comprehensive Benchmark for Reflective Tool Use LLMs (2026.findings-acl)

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Zheyuan Liu, Liqiang Xiao, Yang Li, Hyokun Yun, Lihong Li, Chao Zhang, Meng Jiang

Enabling Stroke-Level Structural Analysis of Hieroglyphic Scripts without Language-Specific Priors (2026.findings-acl)

Challenge:	Existing benchmarks primarily evaluate planning and execution success, overlooking the self-reflective dimension of tool use.
Approach:	They propose a benchmark to assess LLMs’ self-reflective reasoning in tool-augmented multi-turn dialogues.
Outcome:	The proposed benchmark covers 10 domains with 88 distinct APIs and 968 annotated dialogues, systematically injecting diverse error types arising from both user and assistant behavior.

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Fuwen Luo, Zihao Wan, Ziyue Wang, Yaluo Liu, Pau Tong Lin Xu, Xuanjia Qiao, Xiaolong Wang, Peng Li, Yang Liu

Challenge:	Existing structural analysis methods for hieroglyphic scripts are script-specific and labor-intensive.
Approach:	They propose a hieroglyphic Stroke Analyzer framework that captures character-internal structures and semantics without handcrafted data.
Outcome:	The proposed framework captures character-internal structures and semantics without priors . it can be used to generalize hieroglyphic scripts across languages .

LUNE: Efficient LLM Unlearning via LoRA Fine-Tuning with Negative Examples (2026.findings-acl)

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Yezi Liu, Hanning Chen, Wenjun Huang, Yang Ni, Mohsen Imani

Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes (2022.coling-1)

Challenge:	Large Language Models encode vast factual knowledge, yet their inability to selectively forget specific information hinders privacy protection, bias mitigation, and post-deployment correction.
Approach:	They propose a LoRA-based negative-only unlearning framework that updates only low-rank adapters while freezing the backbone.
Outcome:	The proposed framework reduces computational cost by about an order of magnitude compared to full fine-tuning and memory-editing methods.

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Bin Ji, Shasha Li, Shaoduo Gan, Jie Yu, Jun Ma, Huijun Liu, Jing Yang

Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature Enhancement (2024.findings-emnlp)

Challenge:	Existing prototypical networks for named entity recognition suffer from label dependency and tightly distributed prototypes, thus causing misclassifications.
Approach:	They propose an Entity-level Prototypical Network enhanced by dispersedly distributed prototypes to build entity-level prototypes and distribute them dispersionally.
Outcome:	The proposed system outperforms the previous models on two evaluation tasks and the Few-NERD settings in terms of overall performance.

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Zhiyuan Chang, Mingyang Li, Junjie Wang, Yi Liu, Qing Wang, Yang Liu

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)

Challenge:	Text-to-Image Diffusion models generate high-quality images from textual descriptions, but they often produce images that do not fully align with the input prompts, resulting in semantic inconsistencies.
Approach:	They propose an automated repair approach to address catastrophic-neglect in T2I DMs.
Outcome:	The proposed model achieves 10.1%-16.3% higher Correct Rate in image generation compared to baselines.

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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang

ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models (2025.acl-long)

Challenge:	Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified.
Approach:	They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .
Outcome:	Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .

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Ziyue Wang, Chi Chen, Fuwen Luo, Yurui Dong, Yuanchi Zhang, Yuzhuang Xu, Xiaolong Wang, Peng Li, Yang Liu

Towards a Unified Multi-Dimensional Evaluator for Text Generation (2022.emnlp-main)

Challenge:	Existing benchmarks for evaluating MLLMs have not addressed active perception . a novel benchmark is proposed to evaluate active perception in ML models .
Approach:	They propose a benchmark to evaluate active perception in Multimodal Large Language Models . they restrict the perceptual field of a model and require it to actively zoom or shift it .
Outcome:	The proposed benchmark focuses on a specialized form of Visual Question Answering (VQA) that eases and quantifies the evaluation yet challenging for existing MLLMs.

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Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, Jiawei Han

WaveDetect: Robust Framework for Machine-Generated Text Detection via Wavelet Transform (2026.findings-acl)

Challenge:	Existing evaluation frameworks for natural language generation are dominated by similarity-based metrics.
Approach:	They propose a multi-dimensional evaluator for natural language generation that integrates multiple dimensions into one evaluer.
Outcome:	The proposed evaluator improves on three typical NLG tasks and improves with external knowledge.

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Zhichen Liu, Kaitong Qin, Linhan He, Yang Xu

Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents (2026.acl-long)

Challenge:	Existing methods for detecting LLM-generated texts falter when faced with adversarial perturbations, cross-domain shifts, and the rapid temporal evolution of the foundation model.
Approach:	They propose a framework that reformulates text detection as a signal processing task within the time-frequency domain.
Outcome:	The proposed framework achieves superior accuracy and robustness against sophisticated attacks and generalization across out-of-distribution topics.

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null Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, null Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei

Unified Contextual Query Rewriting (2023.acl-industry)

Challenge:	Existing benchmarks focus on simple image-text interactions, overlooking complex visual formats like charts.
Approach:	They propose a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis.
Outcome:	The proposed framework generates 4,738 question-answering pairs across 8 domains from real-world documents.

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Yingxue Zhou, Jie Hao, Mukund Rungta, Yang Liu, Eunah Cho, Xing Fan, Yanbin Lu, Vishal Vasudevan, Kellen Gillespie, Zeynab Raeesy

Paraphrasing

Challenge:	Large-scale conversational AI agents such as Alexa, Siri, and Google Assistant are becoming increasingly popular in real-world applications to assist users in daily life.
Approach:	They propose a unified contextual query rewriting model that unifies QR for friction reduction and contextual carryover . they leverage the text-to-text unified framework which uses independent tasks with weighted loss to account for task importance .
Outcome:	The proposed model reduces friction and contextual carryover by using multiple auxiliary tasks.

FFN Lens: How Transformers Divide Labor for Multilingual Tasks (2026.findings-acl)

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Jiatong Li, Hailong Cao, Yang Liu

Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search (2026.findings-acl)

Challenge:	Large Language Models (LLMs) exhibit strong performance on multilingual tasks, yet the process of constructing predictions in the target language remains under-explored.
Approach:	They propose a novel interpretability method focusing on the Feed-Forward Network (FFN) layers of Large Language Models.
Outcome:	The proposed interpretability method is based on the Feed-Forward Network (FFN) layer of Large Language Models.

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Yifei Zhang, Xu Yang, Xiao Yang, Bowen Xian, Qizheng Li, Shikai Fang, Jingyuan Li, Jian Wang, Minrui Xu, Yuge Zhang, Weiqing Liu, Jiang Bian

GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization (2024.emnlp-main)

Challenge:	LLM-based agents for machine learning engineering rely on tree search to rank candidates.
Approach:	They propose an LLM-based agent that operationalizes gradient-based optimization.
Outcome:	The proposed agent achieves a state-of-the-art 35.1% any-medal rate on MLE-Bench with a limited budget on a single GPU.

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Yangfan Ye, Xiachong Feng, Xiaocheng Feng, Weitao Ma, Libo Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin

WW-CSL: A New Dataset for Word-Based Wearable Chinese Sign Language Detection (2024.lrec-main)

Challenge:	Current studies focus on single-language or single-document tasks for news summarization . lack of a benchmark inhibits researchers from adequately studying this invaluable problem.
Approach:	They propose a novel task that unifies Multi-lingual, Cross-lingual and Multi-document Summarization into one task.
Outcome:	The proposed task encapsulates the real-world requirements all-in-one and is validated by extensive analysis.

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Fan Xu, Kai Liu, Yifeng Yang, Keyu Yan

OpenHuEval: Evaluating Large Language Model on Hungarian Specifics (2025.findings-acl)

Challenge:	Sign language is an effective non-verbal communication mode for the hearingimpaired people.
Approach:	They propose a three-form scheme to represent dynamic CSL gestures using a word-based dataset.
Outcome:	The proposed framework integrates the local sequential sensor data derived from the wearable-based CSL gestures with the global, fine-grained skeleton representations captured from video-based gestures simultaneously.

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Haote Yang, Xingjian Wei, Jiang Wu, Noémi Ligeti-Nagy, Jiaxing Sun, Yinfan Wang, Győző Zijian Yang, Junyuan Gao, Jingchao Wang, Bowen Jiang, Shasha Wang, Nanjun Yu, Zihao Zhang, Shixin Hong, Hongwei Liu, Wei Li, Songyang Zhang, Dahua Lin, Lijun Wu, Gábor Prószéky, Conghui He

Context-DPO: Aligning Language Models for Context-Faithfulness (2025.findings-acl)

Challenge:	Recent advances in Large Language Models (LLMs) represent significant strides toward artificial general intelligence (AGI).
Approach:	They introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics.
Outcome:	The framework reveals intrinsic patterns and mechanisms of LLMs in non-English languages, with Hungarian serving as an example.

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Baolong Bi, Shaohan Huang, Yiwei Wang, Tianchi Yang, Zihan Zhang, Haizhen Huang, Lingrui Mei, Junfeng Fang, Zehao Li, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang, Shenghua Liu

Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing (2020.acl-main)

Challenge:	Context-DPO is the first alignment method specifically designed to enhance contextfaithfulness for large language models.
Approach:	They propose a benchmark that simulates Retrieval-Augmented Generation scenarios with knowledge conflicts to evaluate context-faithfulness.
Outcome:	The proposed method improves LLMs' context-faithfulness by 35% to 280% over open-source models.

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Ruisheng Cao, Su Zhu, Chenyu Yang, Chen Liu, Rao Ma, Yanbin Zhao, Lu Chen, Kai Yu

Budget-Constrained Tool Learning with Planning (2024.findings-acl)

Challenge:	Existing semantic parsing frameworks rely on nontrivial human labor to generate canonical utterances.
Approach:	They propose a framework that uses an unsupervised paraphrase model to parse canonical utterances.
Outcome:	The proposed framework is effective and compatible with supervised training.

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Yuanhang Zheng, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu

Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification (2021.emnlp-main)

Challenge:	Existing methods for budget-constrained tool learning have been overlooked . et al., 2023b) compared tool learning with other methods to improve performance .
Approach:	They propose a method for budget-constrained tool learning by creating a preferable plan under the budget constraint before utilizing the tools.
Outcome:	The proposed method reduces the cost of tool learning and reaches competitive Pass Rate.

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Pengfei Cao, Yubo Chen, Yuqing Yang, Kang Liu, Jun Zhao

Challenge:	Existing studies focus on identifying event factuality at sentence level, which leads to conflicts between different mentions of the same event.
Approach:	They propose a document-level event factuality identification model that uses local uncertainty and global structure to model event factuality.
Outcome:	The proposed method outperforms existing models on two widely used datasets.

Synergizing Stylometrics with Semantics: Dual-Path Framework for LLM Detection and Attribution (2026.findings-acl)

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Xingyu Lu, Yumeng Ma, Xiang Zhou, Shengli Gan, Guiying Deng, Yang Wen, Yanbing Liu

Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning (2023.findings-emnlp)

Challenge:	Existing methods for identifying MGTs rely on statistical likelihood or deep embeddings.
Approach:	They propose a framework that extracts model-specific stylistic fingerprints across lexical, syntactic, and structural dimensions.
Outcome:	The proposed framework achieves a Macro-F1 score of 95.6% on the Wikipedia dataset.

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Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Mohammad Shoeybi, Ming-Yu Liu, Yuke Zhu, Bryan Catanzaro, Chaowei Xiao, Anima Anandkumar

Refining Source Representations with Relation Networks for Neural Machine Translation (C18-1)

Challenge:	Existing methods for image-to-text generation store all knowledge within parameters, thus requiring computational-expensive fine-tuning.
Approach:	They propose a Retrieval-augmented Visual Language Model that stores all the knowledge within parameters and can be used to retrieve it from the external database.
Outcome:	The proposed model significantly boosts performance for image-to-text generation tasks with 4x less parameters compared with baseline methods.

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Wen Zhang, Jiawei Hu, Yang Feng, Qun Liu

Multi-target Backdoor Attacks for Code Pre-trained Models (2023.acl-long)

Challenge:	Existing neural machine translation frameworks that forget distant information and disregard relationship between source and target words are not effective.
Approach:	They propose to use relation networks to learn better representations of the source . they propose to associate source words with each other to help retain their relationships .
Outcome:	Experiments show that the proposed approach outperforms the encoder-decoder framework on several datasets.

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Yanzhou Li, Shangqing Liu, Kangjie Chen, Xiaofei Xie, Tianwei Zhang, Yang Liu

What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation (2022.findings-acl)

Challenge:	Existing work for backdoor attacks on neural code models insert triggers into task-specific data for code-related downstream tasks, limiting the scope of attacks.
Approach:	They propose task-agnostic backdoor attacks for code pre-trained models . they use two learning strategies to implant backdoors into code understanding and generation models - Poisoned Seq2Seq learning and token representation learning .
Outcome:	The proposed model is pre-trained with two learning strategies to support the multi-target attack of downstream code understanding and generation tasks.

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Sarik Ghazarian, Behnam Hedayatnia, Alexandros Papangelis, Yang Liu, Dilek Hakkani-Tur

Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving (2025.emnlp-main)

Challenge:	Existing metrics for dialog evaluation are trained on human annotations, which is cumbersome to collect.
Approach:	They propose to use user sentiment and other information as proxy to measure the quality of previous dialogs.
Outcome:	The proposed model is comparable to models trained on human annotated data.

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Chuxue Cao, Mengze Li, Juntao Dai, Jinluan Yang, Zijian Zhao, Shengyu Zhang, Weijie Shi, Chengzhong Liu, Sirui Han, Yike Guo

LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts (2026.eacl-long)

Challenge:	Large language models (LLMs) have shown promising first-order logic (FOL) reasoning capabilities with applications in various areas, but their effectiveness in complex mathematical reasoning involving multi-step FOL deductions remains under-explored.
Approach:	They propose a self-adaptive solution that enhances the Diversity and REAsonability of LLMs’ generation strategies by introducing an Axiom-Driven Strategy Diversification mechanism and a Sub-Proposition Error Feedback to help LLM reflect on and correct their proofs.
Outcome:	The proposed model improves diversity and REAsonability of LLMs’ generation strategies by introducing an Axiom-Driven Strategy Diversification mechanism and a Sub-Proposition Error Feedback to help LLM reflect on and correct proofs.

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Yang Liu, Jiaye Yang, Weikang Li, Jiahui Liang, Yang Li, Lingyong Yan

ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning (2025.findings-emnlp)

Challenge:	LM-LEXICON is a definition modeling approach that integrates data clustering, semantic expert learning, and model merging.
Approach:	They propose a definition modeling approach that integrates data clustering, semantic expert learning, and model merging using a sparse mixture-of-experts architecture.
Outcome:	The proposed model outperforms existing methods on five widely used benchmarks and achieves a BLEU score of 7%.

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Yang Wu, Huayi Zhang, Yizheng Jiao, Lin Ma, Xiaozhong Liu, Jinhong Yu, Dongyu Zhang, Dezhi Yu, Wei Xu

Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood (2024.emnlp-main)

Challenge:	Prevailing methods for task-specific instruction tuning use similarity metrics to select training data . but instruction tuning loss often fails to exhibit a monotonic relationship with actual task performance .
Approach:	They propose a task-specific instruction tuning method that leverages pairwise preference loss as a reward signal.
Outcome:	The proposed method surpasses state-of-the-art methods for task-specific instruction tuning.

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Yang Xu, Yu Wang, Hao An, Zhichen Liu, Yongyuan Li

Psycholinguistics

Challenge:	Existing methods for detecting modelgenerated texts from human texts are limited by the fact that absolute likelihood values of texts are bound to certain linguistic and cognitive constraints.
Approach:	They propose to use relative likelihood values instead of absolute ones to extract useful features from the spectrum-view of likelihood for the human-model text detection task.
Outcome:	The proposed method can reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies.

IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web (2025.findings-acl)

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Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li

OptiCo: Adaptive Distributed Training Optimization via Collaborative Agent Reasoning (2026.acl-long)

Challenge:	Existing models have been introduced to improve image comprehension, but there is no robust benchmark for imagetoweb conversion.
Approach:	They propose a benchmark to assess imagetoweb conversion proficiency of large multimodal models . they propose to measure layout information of web pages by parsing the Document Object Model tree .
Outcome:	The proposed benchmark measures the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work.

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Sheng Chen, Tang Zhe, Weixing Zhang, Fei Yang, Yuanyuan. Wang, Tianlin Li, Yang Liu

PhotoChat: A Human-Human Dialogue Dataset With Photo Sharing Behavior For Joint Image-Text Modeling (2021.acl-long)

Challenge:	Existing distributed training frameworks are plagued by over-reliance on prior profiling and poor generalization across models/hardware.
Approach:	They propose a model-driven multi-agent framework that leverages Large Language Models to enable automatic and explainable distributed training strategy configuration.
Outcome:	The proposed framework outperforms expert-designed training strategies within 20 iterations.

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Xiaoxue Zang, Lijuan Liu, Maria Wang, Yang Song, Hao Zhang, Jindong Chen

BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover’s Distance (2020.emnlp-main)

Challenge:	PhotoChat contains 12k dialogues, each of which is paired with a user photo that is shared during the conversation.
Approach:	They propose to use PhotoChat to facilitate research on image-text modeling by combining a photo-sharing intent prediction task and a picture retrieval task to retrieve the most relevant photo according to the dialogue context.
Outcome:	The proposed tasks achieve 10.4% recall@1 and 58.1% F1 scores, indicating that the proposed dataset presents interesting yet challenging real-world problems.

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Jianquan Li, Xiaokang Liu, Honghong Zhao, Ruifeng Xu, Min Yang, Yaohong Jin

DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)

Challenge:	Pre-trained language models have been proposed and applied to many NLP tasks, yielding state-of-the-art performance, but high storage and computational costs obstruct them to be effectively deployed on resource-constrained devices and real-time applications.
Approach:	They propose a BERT distillation method which allows each intermediate student layer to learn from any intermediate teacher layers.
Outcome:	The proposed method can learn from different teacher layers adaptively for different NLP tasks.

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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang

CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning (2022.naacl-industry)

Challenge:	Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs).
Approach:	They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories.
Outcome:	The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks.

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Penghui Wei, Xuanhua Yang, ShaoGuo Liu, Liang Wang, Bo Zheng

Beyond "I Don’t Know": Evaluating LLM Self-Awareness in Discriminating Data and Model Uncertainty (2026.acl-long)

Challenge:	a paper focuses on automatically generating the text of an ad to capture user interest for achieving higher click-through rate.
Approach:	They propose a CTR-driven advertising text generation approach to generate ad texts based on user reviews.
Outcome:	The proposed approach outperforms existing approaches on industrial datasets and on large-scale unpaired reviews.

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Jingyi Ren, Ante Wang, Yunghwei Lai, Xiaolong Wang, Linlu Gong, Weitao Li, Weizhi Ma, Yang Liu

Challenge:	Prior studies treat refusal as a generic "I don't know" lack of distinction limits downstream action decisions like requesting clarification or invoking external tools.
Approach:	They propose a benchmark to evaluate explicit uncertainty attribution in large language models.
Outcome:	The proposed method improves uncertainty attribution while preserving answer accuracy.

Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation (2025.acl-long)

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Andong Chen, Yuchen Song, Kehai Chen, Xuefeng Bai, Muyun Yang, Liqiang Nie, Jie Liu, Tiejun Zhao, Min Zhang

DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)

Challenge:	Experimental results show that our model significantly outperforms existing multimodal MT and text-only MT.
Approach:	They propose a stable diffusion-based imagination network into a multimodal large language model to generate an image for each source sentence.
Outcome:	The proposed model outperforms existing multimodal and text-only MT and achieves an average improvement of 14 BLEU points on Multi30K and MSCOCO multimodal MT benchmarks.

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton

A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction (2025.coling-main)

Challenge:	commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools .
Approach:	They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression .
Outcome:	The proposed approach outperforms human experts in medical examinations on diverse datasets.

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Xinpeng Liu, Bing Xu, Muyun Yang, Hailong Cao, Conghui Zhu, Tiejun Zhao, Wenpeng Lu

Unveiling Project-Specific Bias in Neural Code Models (2024.lrec-main)

Challenge:	Existing approaches to address Grammatical Error Correction (GEC) tasks are based on large scale labeled data, which leads to extremely high data annotation costs.
Approach:	They propose a Chain-of-Task framework to reduce over-correction in large language models . they propose supervised fine-tuning strategy and an algorithm for automatic dataset annotation .
Outcome:	The proposed framework achieves state-of-the-art on both FCGEC (in-domain) and NaCGEC (out-of domain) test sets.

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Zhiming Li, Yanzhou Li, Tianlin Li, Mengnan Du, Bozhi Wu, Yushi Cao, Junzhe Jiang, Yang Liu

TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)

Challenge:	Large Language Models (LLMs) based neural code models struggle to generalize effectively to real-world inter-project out-of-distribution data.
Approach:	They propose a Cond-Idf measurement to measure the relatedness of a token with a label and its project-specificness.
Outcome:	The proposed framework improves both inter-project OOD generalization and adversarial robustness while not sacrificing accuracy on intra-project IID data.

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Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi

LoopCoder: Scaling Code Intelligence via Looped Language Models (2026.findings-acl)

Challenge:	TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications.
Approach:	They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Outcome:	The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions.

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Jian Yang, Wei Zhang, Shuyue Guo, Yizhi LI, Linzheng Chai, Zhengmao Ye, Shukai Liu, Yuyang Song, Jiajun Wu, Che Liu, Tianyu Zheng, Siwei Wu, Leo L, Xudong Ma, Chuan Hao, Ran Tao, Yan Xing, Jianzhou Wang, Mingjie Tang, Aishan Liu, Zhoujun Li, Xianglong Liu, Weifeng Lv, Bryan Dai

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)

Challenge:	Large language models have mastered syntax-level code generation, but complex algorithmic reasoning remains a challenge.
Approach:	They propose a recurrent inductive bias that aligns with the recursive nature of programming logic.
Outcome:	The proposed model achieves comparable performance to standard dense models with more parameters.

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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen

LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment (2026.findings-acl)

Challenge:	Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy.
Approach:	They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges.
Outcome:	The proposed framework reduces retrieval time while maintaining high model performance.

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Shipeng Li, Zhiqin Yang, Shikun Li, Xiaobo Xia, Hengyu Liu, Xinghua Zhang, Gaode Chen, Dong Fang, Ying Tai, Zhe Peng

Generating Structured Pseudo Labels for Noise-resistant Zero-shot Video Sentence Localization (2023.acl-long)

Challenge:	Reinforcement learning with verifiable rewards (RLVR) is a key technique for enhancing LLMs’ reasoning abilities, yet its data inefficiency remains a major bottleneck.
Approach:	They propose a gradient-alignment-based method which intelligently selects the learnable and representative training reasoning data for RLVR post-training.
Outcome:	Experiments on five reasoning benchmarks show that the proposed method significantly reduces training data requirements while improving performance.

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Minghang Zheng, Shaogang Gong, Hailin Jin, Yuxin Peng, Yang Liu

Evaluating Text Generation Quality Using Spectral Distances of Surprisal (2025.findings-emnlp)

Challenge:	Existing zero-shot pipelines generate event proposals and then generate a pseudo query for each event proposal.
Approach:	They propose a Structure-based Pseudo Label generation (SPL) that generates free-form interpretable pseudo queries before constructing query-dependent event proposals.
Outcome:	The proposed method learns with only video data without any annotation . it generates free-form interpretable pseudo queries before constructing query-dependent event proposals .

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Zhichen Liu, Yongyuan Li, Yang Xu, Yu Wang, Yingfang Yuan, Zuhao Yang

On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)

Challenge:	Existing metric fails to capture text surprisal, but FACE-2 produces stronger agreement with human preferences.
Approach:	They propose a new automatic evaluation metric for open-ended text generation . they propose metric that extracts the dynamic patterns (spectrum) of text surprisal .
Outcome:	The proposed metric outperforms existing methods in revealing the model scaling effect . it produces stronger agreement with human preferences from a large human-annotated dataset .

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Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun

Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions (2025.acl-long)

Challenge:	Existing research on inductive reasoning models emphasizes rule design without grounding them in specific scenarios.
Approach:	They propose to use LLMs to learn underlying patterns from limited examples in entirely new environments.
Outcome:	The proposed benchmark evaluates the inductive reasoning abilities of large language models in scientific settings.

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Hang Li, Tianlong Xu, Kaiqi Yang, Yucheng Chu, Yanling Chen, Yichi Song, Qingsong Wen, Hui Liu

Numerical Reasoning

Challenge:	Recent studies have demonstrated the potential of large language models (LLMs) for automatic error detection in math word problems (MWPs).
Approach:	They propose a framework that generates adaptive reference solutions using LLMs to enhance error detection by reducing conformity bias in MWPs.
Outcome:	The proposed framework mitigates the performance gap between conventional and alternative solutions in MWPs, especially when combined with reasoning-enhancing techniques like chain-of-thought prompting.

PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction (2021.acl-long)

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Shulin Liu, Tao Yang, Tianchi Yue, Feng Zhang, Di Wang

MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators (2022.acl-long)

Challenge:	Chinese spelling correction (CSC) is a task to detect and correct spelling errors in texts.
Approach:	They propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) which jointly learns how to understand language and correct spelling errors.
Outcome:	The proposed model outperforms state-of-the-art methods on widely used benchmarks and achieves superior performance against existing models.

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Zhixing Tan, Xiangwen Zhang, Shuo Wang, Yang Liu

Long Text and Multi-Table Summarization: Dataset and Method (2022.findings-emnlp)

Challenge:	Prompting has been shown to be a promising approach for applying pre-trained language models to perform downstream tasks.
Approach:	They propose a method that divides the translation process into three stages using pre-trained language models.
Outcome:	The proposed method significantly improves translation performance of pre-trained language models on three translation tasks.

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Shuaiqi Liu, Jiannong Cao, Ruosong Yang, Zhiyuan Wen

End-to-End Segmentation-based News Summarization (2022.findings-acl)

Challenge:	Existing document summarization methods focus on the text and filter out the non-textual content. Existing methods cannot meet the requirements of summarizing long text and multiple tables in each report.
Approach:	They propose a dataset for automatic document summarization that uses text and tabular data to produce a concise summary covering the input document's salient information.
Outcome:	The proposed method can produce a concise summary covering the input document's salient information.

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Yang Liu, Chenguang Zhu, Michael Zeng

MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)

Challenge:	Existing summarization systems only provide one genetic summary of the whole article, making it difficult for users to navigate the reading.
Approach:	They propose a task of segmenting a news article into multiple sections and generating the corresponding summary to each section.
Outcome:	The proposed model outperforms state-of-the-art models on a 27k news article dataset . it can jointly segment a document and produce the summary for each section .

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Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang

ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging (2026.acl-long)

Challenge:	Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain.
Approach:	They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process.
Outcome:	The proposed dataset evaluates the performance of unsupervised methods and advanced large language models.

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Junyao Yang, Chen Qian, Wen Shen, Yong Liu, Jing Shao, Dongrui Liu

G2: Guided Generation for Enhanced Output Diversity in LLMs (2025.emnlp-main)

Challenge:	Existing models with long chain-of-thought reasoning lack reasoning depth and domain-specific utility.
Approach:	They propose a model merging framework that integrates reasoning with domain-specific task models.
Outcome:	The proposed model merging framework outperforms state-of-the-art models while maintaining robust reasoning performance.

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Zhiwen Ruan, Yixia Li, Yefeng Liu, Yun Chen, Weihua Luo, Peng Li, Yang Liu, Guanhua Chen

Challenge:	Existing approaches to enhance output diversity but compromise quality of outputs.
Approach:	They propose a training-free plug-and-play method that enhances output diversity while preserving generation quality.
Outcome:	The proposed method enhances output diversity while maintaining an optimal balance between diversity and quality.

AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents (2025.findings-acl)

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Guhong Chen, Liyang Fan, Zihan Gong, Nan Xie, Zixuan Li, Ziqiang Liu, Chengming Li, Qiang Qu, Hamid Alinejad-Rokny, Shiwen Ni, Min Yang

Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learners (2024.acl-long)

Challenge:	Existing legal language models struggle with dynamic courtroom interactions, resulting in overfitting to standardized legal tasks.
Approach:	They propose a new adversarial evolutionary approach for agents that performs dynamic knowledge learning and evolution through structured adversarials in a simulated courtroom program.
Outcome:	The proposed approach outperforms existing LLM-based models in three critical dimensions: cognitive agility, professional knowledge, and logical rigor.

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Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang, Jinchuan Tian, Zhenhui Ye, Luping Liu, Zehan Wang, Ziyue Jiang, Xuankai Chang, Jiatong Shi, Chao Weng, Zhou Zhao, Dong Yu

ToolRM: Towards Agentic Tool-Use Reward Modeling (2026.findings-acl)

Challenge:	Large language models (LLMs) have been used for general-purpose interfaces across multiple tasks and languages.
Approach:	They propose to use large language models as a general-purpose interface across multiple tasks and languages.
Outcome:	The proposed model performs better on 200K hours of 6-language data for voice generation applications.

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Renhao Li, Jianhong Tu, Yang Su, Yantao Liu, Fei Huang, Hamid Alinejad-Rokny, Derek F. Wong, Junyang Lin, Min Yang

Challenge:	lack of reliable reward models for tool-use tasks has limited progress toward agentic AI . recent advances in agentic artificial intelligence are driven by tool-using capabilities of large language models.
Approach:	They propose a pipeline that constructs pairwise preference data using rule-based scoring and multidimensional sampling to build lightweight reward models.
Outcome:	The proposed model outperforms existing models on tool calling tasks with higher accuracy.

ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models (2024.emnlp-main)

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Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana

MAIN: Mutual Alignment Is Necessary for instruction tuning (2025.emnlp-main)

Challenge:	Currently, tool-augmented large language models (LLMs) only achieve total scores of 45.3 and 37.0, respectively, on a scale of 100.
Approach:	They propose a multi-level diagnostic process to assess the LLM's hallucinations through two perspectives: depth and breadth.
Outcome:	The proposed diagnostic process assesses the hallucinations of large language models through two perspectives: depth and breadth.

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Fanyi Yang, Jianfeng Liu, Xin Zhang, Haoyu Liu, Xixin Cao, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang

Masked Text-to-Audio Flow-Matching and Reward Feedback Optimization (2026.findings-acl)

Challenge:	Instruction tuning has enabled large language models to achieve remarkable performance, yet its success heavily depends on the availability of high-quality instruction-response pairs.
Approach:	They propose a mutual alignment framework which enforces coherence between instructions and responses through mutual constraints.
Outcome:	The proposed framework generalizes well across model architectures and sizes, achieving state-of-the-art performance on LLaMA, Mistral, and Qwen models across diverse benchmarks.

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Rongjie Huang, Dongchao Yang, Wenxiang Guo, Huadai Liu, Xize Cheng, Zehan Wang, Zhou Zhao, Xixin Wu, Helen M. Meng

ExplainaBoard: An Explainable Leaderboard for NLP (2021.acl-demo)

Challenge:	Experimental results show that Flow-matching generative models can scale training by increasing data, computational resources, and model size.
Approach:	They propose a flow-matching transformer with masked generative modeling for scaling text-to-audio inference-time prediction.
Outcome:	The proposed model scales inference-time computations by masking generation and re-predicting them through iterative decoding.

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Pengfei Liu, Jinlan Fu, Yang Xiao, Weizhe Yuan, Shuaichen Chang, Junqi Dai, Yixin Liu, Zihuiwen Ye, Graham Neubig

ExPUNations: Augmenting Puns with Keywords and Explanations (2022.emnlp-main)

Challenge:	Using leaderboards, researchers can track the performance of various systems on various NLP tasks.
Approach:	They propose a new conceptualization and implementation of NLP evaluation using a leaderboard.
Outcome:	The ExplainaBoard is an evaluation tool for natural language processing (NLP) it covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks.

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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Perspective Transition of Large Language Models for Solving Subjective Tasks (2025.findings-acl)

Challenge:	Puns add the challenge of fusing commonsense and world knowledge with the ability to interpret lexical-semantic ambiguity.
Approach:	They propose to augment existing datasets with detailed crowdsourced annotations of puns, keywords and fine-grained funniness ratings to challenge current models' ability to understand and generate humor.
Outcome:	The proposed tasks include explanation generation to aid with pun classification and keyword-conditioned pun generation to challenge state-of-the-art models' ability to understand and generate humor.

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Xiaolong Wang, Yuanchi Zhang, Ziyue Wang, Yuzhuang Xu, Fuwen Luo, Yile Wang, Peng Li, Yang Liu

Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)

Challenge:	Large language models (LLMs) have revolutionized the field of natural language processing . performance of LLMs on subjective tasks is limited, authors say .
Approach:	They propose a method that allows LLMs to select between direct, role, and third-person perspectives for best way to solve corresponding subjective problem.
Outcome:	The proposed method outperforms widely used single fixed perspective based methods on 12 subjective tasks.

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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu

PclGPT: A Large Language Model for Patronizing and Condescending Language Detection (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature.
Approach:	They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management.
Outcome:	The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench.

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Hongbo Wang, LiMingDa LiMingDa, Junyu Lu, Hebin Xia, Liang Yang, Bo Xu, Ruizhu Liu, Hongfei Lin

Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models (2026.findings-acl)

Challenge:	Patronizing and condescending language is an essential branch of toxic language . pre-trained language models perform poorly in detecting PCL due to its implicit toxicity traits .
Approach:	They propose a novel LLM benchmark for patronizing and condescending language . they use a dataset to analyze the toxicity of patronizing condescending languages .
Outcome:	The proposed model can detect patronizing and condescending language (PCL) the model can be used to analyze the toxicity of the language and to improve the detection.

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Hoang Phan, Xianjun Yang, Yuanshun Yao, Jingyu Zhang, Shengjie Bi, Xiaocheng Tang, Madian Khabsa, Lijuan Liu, Deren Lei

Evaluating and Aligning Human Economic Risk Preferences in LLMs (2025.emnlp-main)

Challenge:	Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning . however, the recipe introduces a significant risk of capability regression, where models forget foundational skills after prolonged training without employing regularization strategies.
Approach:	They propose a replay strategy with dynamic objective reweighting for general knowledge preservation using short-horizon signals of convergence and instability.
Outcome:	The proposed method preserves general capabilities and improves reasoning . it can be applied to existing RLVR pipelines without training additional models or tuning .

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Jiaxin Liu, Yixuan Tang, Yi Yang, Kar Yan Tam

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) are increasingly used in decision-making scenarios that involve risk assessment, yet their alignment with human economic rationality remains unclear.
Approach:	They propose an evaluation metric called Risk Disparity Score (RDS) and assess whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual’s persona.
Outcome:	The proposed evaluation metric assesses whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual’s persona.

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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen

RAG or Learning? Understanding the Limits of LLM Adaptation under Continuous Knowledge Drift in the Real World (2026.findings-acl)

Challenge:	Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval.
Approach:	They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs).
Outcome:	The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models.

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Hanbing Liu, Lang Cao, Yang Li

Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)

Challenge:	Existing methods to update or supplement large language models struggle under continuous knowledge drift.
Approach:	They propose a dynamic event benchmark and time-aware retrieval baseline that captures how knowledge evolves over time.
Outcome:	The proposed method enables systematic evaluation of model adaptation under continuous knowledge drift.

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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

Disentangled Information Bottleneck for Adversarial Text Defense (2025.emnlp-main)

Challenge:	Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation.
Approach:	They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task.
Outcome:	The proposed model improves inference speed by 2.3-2.7x times without compromising model performance.

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Yidan Xu, Xinghao Yang, Wei Liu, Bao-di Liu, Weifeng Liu

Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs (2026.findings-acl)

Challenge:	Existing studies have proven that these deep models are super vulnerable to adversarial examples, which are slightly modified inputs.
Approach:	They propose a novel text defense method that separates the robust and non-robust features with a disentangled two-line framework rather than the one-line compression network in IB.
Outcome:	The proposed method outperforms six baselines on four datasets with accuracy improvements ranging from 3.8% to 20.7%.

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Guangtao Lyu, Qi Liu, Chenghao Xu, Jiexi Yan, Muli Yang, Xueting Li, Fen Fang, Cheng Deng

CRPO: Confidence-Reward Driven Preference Optimization for Machine Translation (2025.findings-acl)

Challenge:	Existing training-free methods are vulnerable to the attention sink phenomenon . Existing methods include contrastive decoding and auxiliary expert models .
Approach:	They propose a training-free attention intervention that constructs a PAD map to identify semantically core visual regions and applies per-head Median Absolute Deviation Scaling to adaptively control the intervention strength.
Outcome:	The proposed intervention improves visual grounding and reduces hallucinations on multiple LVLMs and benchmarks.

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Guofeng Cui, Pichao Wang, Yang Liu, Zemian Ke, Zhu Liu, Vimal Bhat

EpiCoDe: Boosting Model Performance Beyond Training with Extrapolation and Contrastive Decoding (2025.findings-acl)

Challenge:	Large language models (LLMs) have shown great potential in natural language processing tasks, but their application to machine translation remains challenging due to pretraining on predominantly English-centric datasets.
Approach:	They propose a method that combines reward scores with model confidence to improve model selection for fine-tuning.
Outcome:	The proposed method outperforms existing methods in translation accuracy and data efficiency.

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Mingxu Tao, Jie Hu, Mingchuan Yang, Yunhuai Liu, Dongyan Zhao, Yansong Feng

Learn Like Humans: Use Meta-cognitive Reflection for Efficient Self-Improvement (2026.acl-long)

Challenge:	Existing methods to enhance performance of Large language models are limited due to the cost of training data and privacy concerns.
Approach:	They propose a method that enhances a finetuned model with its inferior version and adopts contrastive decoding to reduce predicted errors.
Outcome:	The proposed method outperforms existing methods in data-scarcity scenarios across three domains and shows that it is more robust and robust.

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Xinmeng Hou, Bohao Qu, Wuqi Wang, Peiliang Gong, Qing Guo, Yang Liu

Context as a Tool: Context Management for Long-Horizon SWE-Agents (2026.findings-acl)

Challenge:	Existing self-improving frameworks rely on inefficient, multi-turn recursive loops that incur high computational costs.
Approach:	They propose a framework that achieves efficient self-evolution within a single recurrence cycle.
Outcome:	The proposed framework outperforms state-of-the-art self-evolving systems while significantly reducing computational overhead.

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Shukai Liu, Bo Jiang, Jian Yang, Yizhi LI, Jinyang Guo, Xianglong Liu, Bryan Dai

DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines (2023.findings-emnlp)

Challenge:	Existing large language models rely on append-only context maintenance or passively triggered compression heuristics, leading to context explosion, semantic drift, and degraded reasoning in long-running interactions.
Approach:	They propose a new context management paradigm that elevates context maintenance to a callable tool . they propose 'cat' framework that injects context-management actions into complete interaction trajectories .
Outcome:	The proposed model outperforms ReAct-based agents and static compression baselines on SWE-Verified tests.

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Prakhar Gupta, Yang Liu, Di Jin, Behnam Hedayatnia, Spandana Gella, Sijia Liu, Patrick Lange, Julia Hirschberg, Dilek Hakkani-Tur

Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training (2026.acl-long)

Challenge:	Dialogue models are able to generate fluent and interesting responses, but they can be difficult to control and may produce non-engaging, unsafe results.
Approach:	They propose a framework for controlling dialogue model behavior using natural language rules, or guidelines, which provide information about the context they are applicable to and what should be included in the response.
Outcome:	The proposed framework is effective in three open-domain dialogue response generation tasks and is consistent with the developer's expectations and intent.

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Lei Liu, Hao Zhu, Xiaoyan Yang, Yue Shen, Zhixuan Chu, Jian Wang, Jinjie Gu, Kui Ren

Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training (2025.naacl-long)

Challenge:	Large language models (LLMs) have impressive capabilities across a wide range of domains, but their generalpurpose pre-training objectives often leave them illsuited for specialized applications such as healthcare.
Approach:	They propose a perplexity-aware data scaling law that establishes a predictive relationship between the perplexities of domain-specific data and the test loss.
Outcome:	Experiments on medical and general-domain benchmarks show that the proposed scaling law consistently identifies near-optimal training subsets with significantly reduced data consumption.

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Sun Ao, Weilin Zhao, Xu Han, Cheng Yang, Xinrong Zhang, Zhiyuan Liu, Chuan Shi, Maosong Sun

Enhancing Topic-to-Essay Generation with External Commonsense Knowledge (P19-1)

Challenge:	Current PP methods face severe bottlenecks, including pipeline bubbles and memory footprint.
Approach:	They propose a sequence-level one-forward-one-backward (1F1B) PP method for training LLMs on long sequences with high throughput and memory efficiency.
Outcome:	The proposed method achieves 1.14X training throughput with half memory footprint compared to baseline methods . it trains an LLM with 30B parameters on sequences up to 64k tokens using 64X NVIDIA A100 GPUs .

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Pengcheng Yang, Lei Li, Fuli Luo, Tianyu Liu, Xu Sun

Beware of Your Po! Measuring and Mitigating AI Safety Risks in Role-Play Fine-Tuning of LLMs (2025.acl-long)

Challenge:	Existing methods for topic-to-essay generation are insufficient for generating novel, diverse, and topic-consistent paragraph-level text with a set of topics.
Approach:	They propose to integrate commonsense from external knowledge base into the generator through dynamic memory mechanism and adversarial training to further improve topic-consistency.
Outcome:	The proposed task is more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation.

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Weixiang Zhao, Yulin Hu, Yang Deng, Jiahe Guo, Xingyu Sui, Xinyang Han, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)

Challenge:	Existing role-play fine-tuning techniques improve role adaptability but may degrade safety performance, especially for villainous characters.
Approach:	They propose safety-aware Role-Play Fine-Tuning (SaRFT) to balance role-playing capabilities and safety.
Outcome:	The proposed method outperforms state-of-the-art baselines under both LoRA and full-parameter fine-tuning settings.

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Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang

TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition (2025.acl-long)

Challenge:	Z-Code++ is a pre-trained language model optimized for abstractive text summarization.
Approach:	They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance.
Outcome:	The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings.

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Tianwei Lin, Jiang Liu, Wenqiao Zhang, Yang Dai, Haoyuan Li, Zhelun Yu, Wanggui He, Juncheng Li, Jiannan Guo, Hao Jiang, Siliang Tang, Yueting Zhuang

PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data (2025.findings-acl)

Challenge:	Existing methods for fine-tuning are resource-efficient, but performance often falls short . a new approach, TeamLoRA, integrates collaborative and competitive modules to improve performance.
Approach:	They propose to introduce task-specific LoRA as domain experts to improve learning efficiency . teamLoRA integrates collaborative and competition modules to improve model learning .
Outcome:	Experiments show that TeamLoRA improves performance in multi-task learning . teamLorea integrates collaborative and competitive modules to improve performance .

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Juntao Tan, Liangwei Yang, Zuxin Liu, Zhiwei Liu, Rithesh R N, Tulika Manoj Awalgaonkar, Jianguo Zhang, Weiran Yao, Ming Zhu, Shirley Kokane, Silvio Savarese, Huan Wang, Caiming Xiong, Shelby Heinecke

FPE2M2: Approaching Lossless and Efficient Quantization with Native Floating Point (2025.findings-acl)

Challenge:	Existing research lacks direct access to such data, making benchmarking difficult due to privacy concerns.
Approach:	They propose a synthetic data pipeline that generates realistic user profiles and private documents and a benchmark to evaluate models' ability to understand personal information.
Outcome:	The proposed pipeline generates realistic user profiles and private documents, enabling PersonaBench, a benchmark for evaluating models’ ability to understand personal information.

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Ke Yi, Jianwei Zhang, Zhiying Xu, Xinlong Yang, Yang Zhou, Minmin Sun, Zengke Liu, Tong Zhang, Junyang Lin, Jingren Zhou

A Web Scale Entity Extraction System (2021.findings-emnlp)

Challenge:	Auto-regressive decoding is a memory-bound job, meaning decoding performance is limited by the bandwidth rather than the computational capabilities of the GPU.
Approach:	They propose a framework that supports lossless weight-only quantization inference and validate it on Qwen and LLaMA Models.
Outcome:	The proposed framework achieves the highest efficiency with lossless accuracy on Qwen and LLaMA Models across various modalities.

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Xuanting Cai, Quanbin Ma, Jianyu Liu, Pan Li, Qi Zeng, Zhengkan Yang, Pushkar Tripathi

STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents (2024.findings-acl)

Challenge:	Existing systems for large-scale entity extraction are limited by the scale and variety of data available on internet platforms.
Approach:	They propose to build an entity extraction system for multiple document types at large scale using multi-modal Transformers.
Outcome:	The proposed system extracts multiple types of entities from multiple document types at large scale using multi-modal Transformers.

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Yue Chen, Chen Huang, Yang Deng, Wenqiang Lei, Dingnan Jin, Jia Liu, Tat-Seng Chua

Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration (2024.findings-acl)

Challenge:	Existing methods for addressing ambiguities in conversational search systems are one-size-fits-all and struggle to achieve effective domain transferability.
Approach:	They propose a method to provide search engines with strategies regarding when to ask clarification questions in a post-hoc manner.
Outcome:	The proposed method improves search performance 10% on four unseen domains.

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Kejuan Yang, Xiao Liu, Kaiwen Men, Aohan Zeng, Yuxiao Dong, Jie Tang

More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)

Challenge:	Existing methods for extending the maximum context lengths of language models are lacking a strong baseline for in-context few-shot classification and on more challenging Chain-of-Thought reasoning, such as HotpotQA, deteriorate question miscomprehension and false inference.
Approach:	They propose to harness window-wise attention and positional embedding techniques to extend the maximum context lengths of language models.
Outcome:	The proposed method is able to extend the maximum context lengths of language models, e.g., 2048 for LLaMA, by harnessing window-wise attention and positional embedding techniques.

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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun

An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4 (2025.findings-acl)

Challenge:	Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically .
Approach:	They propose to improve relation extraction methods to extract relational facts from text . they analyze existing methods and show promising directions towards more powerful RE .
Outcome:	The proposed methods can extract relational facts from text, but they are still lacking in the current field.

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Hui Huang, Xingyuan Bu, Hongli Zhou, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao

RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems (2026.acl-long)

Challenge:	Recent studies have fine-tuned judge models based on open-source LLMs to evaluate the quality of other LLM.
Approach:	They propose to use open-source LLMs to evaluate Large Language Models (LLMs) their empirical results show that the models underperform GPT-4 in several dimensions .
Outcome:	The proposed models outperform GPT-4 on several dimensions including generalizability, fairness and adaptability.

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Weicong Liu, Zixuan Yang, Yibo Zhao, Xiang Li

NOVA-63: Native Omni-lingual Versatile Assessments of 63 Disciplines (2025.emnlp-main)

Challenge:	LR-bench is a high-fidelity, up-to-date benchmark curated from 2024–2025 AI/NLP manuscripts with five-level self-assessed familiarity ratings collected via a large-scale email survey .
Approach:	They propose a reviewer-centric ranking framework that distills each reviewer’s recent publications into compact keyword-based profiles and fine-tunes an embedding model with weak preference supervision constructed from heuristic retrieval signals.
Outcome:	The proposed framework outperforms existing benchmarks and the CMU gold-standard dataset in the evaluation of AI/NLP manuscripts.

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Jinyang Zhang, Kexin Yang, Yu Wan, Muyang Ye, Baosong Yang, Fei Huang, Junyang Lin, Dayiheng Liu

Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments (2024.acl-long)

Challenge:	Existing multilingual benchmarks show severe drawbacks, such as overly translated content, the absence of difficulty control, and disciplinary imbalance, making the benchmarking process unreliable and showing low convincingness.
Approach:	They propose a multilingual benchmark that integrates LLM-assisted formatting, expert quality verification, and multi-level difficulty screening to provide a comprehensive, difficult multilingual assessment.
Outcome:	The proposed benchmark features 93,536 questions sourced from native speakers across 14 languages and 63 academic disciplines.

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Zhenrui Yue, Huimin Zeng, Lanyu Shang, Yifan Liu, Yang Zhang, Dong Wang

Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)

Challenge:	Existing methods to verify claim credibility rely on embedded knowledge or unreliable context.
Approach:	They propose retrieval augmented fact verification through the synthesis of contrasting arguments (RAFTS) they use an embedding model to identify informative demonstrations and in-context prompts to generate the prediction and explanation.
Outcome:	The proposed method outperforms existing methods with smaller LLMs or unreliable contexts.

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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang

RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation (2026.acl-long)

Challenge:	Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability.
Approach:	They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs .
Outcome:	The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks.

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Sunzhu Li, Jiale Zhao, Huimin Ren, Zhenlin Wei, Yang Zhou, Jingwen Yang, Shunyu Liu, Kaike Zhang, Chen Wei

Analyzing Chain-of-thought Prompting in Black-Box Large Language Models via Estimated V-information (2024.lrec-main)

Challenge:	Existing methods for generating open-ended rubrics suffer from scalability bottlenecks and coarse criteria resulting in a supervision ceiling effect.
Approach:	They propose a framework for automated Coarse-to-Fine Rubric Generation . their framework uses principle-guided synthesis, multi-model aggregation, difficulty evolution .
Outcome:	The proposed framework produces comprehensive and highly discriminative criteria capable of capturing the subtle nuances.

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Zecheng Wang, Chunshan Li, Zhao Yang, Qingbin Liu, Yanchao Hao, Xi Chen, Dianhui Chu, Dianbo Sui

GLM: General Language Model Pretraining with Autoregressive Blank Infilling (2022.acl-long)

Challenge:	Chain-of-Thought (CoT) prompting and large language models (LLMs) have shown great potential in improving performance on challenging reasoning tasks.
Approach:	They propose a new metric which extends the concept of pointwise V-information to black-box models and quantifies label-relevant new information introduced by CoT prompting.
Outcome:	The proposed metric extends the concept of pointwise V-information to black-box models, quantifying label-relevant new information introduced by CoT prompting beyond pre-existing label information.

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Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang

QAP: A Quantum-Inspired Adaptive-Priority-Learning Model for Multimodal Emotion Recognition (2023.findings-acl)

Challenge:	Existing pretraining frameworks do not perform well for all tasks of three main categories, such as natural language understanding (NLU), unconditional generation, and conditional generation.
Approach:	They propose a general language model based on autoregressive blank infilling to address this challenge.
Outcome:	The proposed model outperforms BERT, T5, and GPT on a wide range of tasks across NLU, conditional and unconditional generation tasks.

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Ziming Li, Yan Zhou, Yaxin Liu, Fuqing Zhu, Chuanpeng Yang, Songlin Hu

Uncertainty-Aware Cross-Modal Alignment for Hate Speech Detection (2024.lrec-main)

Challenge:	Experimental results show that multimodal emotion recognition is a state-of-the-art technique . textual, visual and acoustic modalities are involved in multimodal video emotion recognition .
Approach:	They propose a quantum-inspired adaptive-priority-learning model to address the challenges . they use quantum state to model modal features and Q-attention to integrate three modalities .
Outcome:	Experimental results show that QAP improves on previous models.

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Chuanpeng Yang, Fuqing Zhu, Yaxin Liu, Jizhong Han, Songlin Hu

FineCops-Ref: A new Dataset and Task for Fine-Grained Compositional Referring Expression Comprehension (2024.emnlp-main)

Challenge:	Existing methods for detecting hate speech ignore misalignment and uncertainty between modalities . social media platforms have become conduits for the rapid dissemination of hate speech .
Approach:	They propose an uncertainty-aware cross-modal alignment framework for hate speech detection that minimizes the misalignment of image and text in memes.
Outcome:	The proposed framework produces a competitive performance compared with existing methods.

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Junzhuo Liu, Xuzheng Yang, Weiwei Li, Peng Wang

Do LLMs Align Human Values Regarding Social Biases? Judging and Explaining Social Biases with LLMs (2025.findings-emnlp)

Challenge:	Referring Expression Comprehension (REC) is a cross-modal task that objectively evaluates the capabilities of language understanding, image comprehension, and language-to-image grounding.
Approach:	They propose to use a new reference expression comprehension (REC) dataset to evaluate the capabilities of language understanding, image comprehension, and language-to-image grounding.
Outcome:	The proposed model is able to reject scenarios where the target object is not visible in the image, a key aspect often overlooked in existing models and approaches.

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Yang Liu, Chenhui Chu

Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training (2026.acl-long)

Challenge:	Large language models can lead to undesired consequences when misaligned with human values . previous studies have shown misalignment of LLMs with human value using expert-designed or agent-based emulated bias scenarios .
Approach:	They investigate whether large language models (LLMs) are misaligned with human values . they find no significant differences in understanding of HVSB between LLMs .
Outcome:	The results show that large language models do not have lower misalignment rates and attack success rates . the study also shows that smaller language models have the ability to explain HVSB .

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Kailai Yang, Xiao Liu, Lei Ji, Hao Li, Xiao Liang, Zhiwei Liu, Yeyun Gong, Peng Cheng, Mao Yang

RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation (2026.acl-long)

Challenge:	Existing methods for reweighting data mixtures rely on manual designation with certain heuristics based on intuition or empirical results.
Approach:	They propose a model-based framework that learns to re-weight domains by reinforcement learning on large quantities of data mixing trajectories with corresponding feedback from an evaluation environment.
Outcome:	The proposed framework outperforms baselines in achieving balanced performance across source and target fields and domain spaces without retraining.

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Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo, Jingzhuo Wu, Leqi Zheng, Yiran Yang, Jianke Zhang, Qingbin Li, Shannan Yan, Changguo Jia, Junfei Wu, Zilei Wang, Qiang Liu, Liang Wang

DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles (2022.lrec-1)

Challenge:	Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains, but their ability to replicate complex, multi-panel visualizations remains largely unassessed.
Approach:	They propose a large-scale benchmark to evaluate chart generation from large- scale raw data and assess iterative code refinement in a multi-turn conversational setting.
Outcome:	The new benchmark evaluates 14 leading VLMs on real-world data and shows they struggle with complex plot structures and authentic data.

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Yuanchi Zhang, Yang Liu

NLEBench+NorGLM: A Comprehensive Empirical Analysis and Benchmark Dataset for Generative Language Models in Norwegian (2024.emnlp-main)

Challenge:	Existing methods to extract and attribute quotations from news data are difficult and require a lot of effort.
Approach:	They propose a corpus of 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media.
Outcome:	The proposed corpus contains 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media.

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Peng Liu, Lemei Zhang, Terje Farup, Even Lauvrak, Jon Ingvaldsen, Simen Eide, Jon Atle Gulla, Zhirong Yang

MTRec: Multi-Task Learning over BERT for News Recommendation (2022.findings-acl)

Challenge:	Norwegian is under-represented within the most impressive breakthroughs in NLP tasks.
Approach:	they investigate the impact of existing Norwegian language models on Norwegian generation tasks . they pre-trained 4 Norwegian Open Language Models from parameter scales and architectures .
Outcome:	The proposed benchmark evaluates the performance of language models on Norwegian generation tasks.

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Qiwei Bi, Jian Li, Lifeng Shang, Xin Jiang, Qun Liu, Hanfang Yang

Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)

Challenge:	Existing news recommendation methods learn news representations solely based on news titles. Existing methods only utilize title information and neglect other valuable news information such as categories and entities.
Approach:	They propose a multi-task method to incorporate multi-field information into BERT, which improves its news encoding capability.
Outcome:	Extensive experiments on the MIND news recommendation benchmark show the proposed method is effective.

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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing

HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization (2025.emnlp-main)

Challenge:	Texar is an open-source text generation toolkit that supports a broad set of text generation tasks.
Approach:	They introduce Texar, an open-source text generation toolkit that supports text generation tasks.
Outcome:	Texar supports machine translation, summarization, dialog, content manipulation, and more.

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Huaqin Zhao, Jiaxi Li, Yi Pan, Shizhe Liang, Xiaofeng Yang, Fei Dou, Tianming Liu, Jin Lu

Curriculum Pre-training for End-to-End Speech Translation (2020.acl-main)

Challenge:	Large language models (LLMs) face memory challenges due to the high cost of backpropagation.
Approach:	They propose a zeroth-order (ZO) optimization that matches memory usage to inference . they propose scalable and memory-efficient zeroth order (ZE) optimizer that integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping as a second-order pre-conditioner.
Outcome:	The proposed algorithm outperforms state-of-the-art methods with an average speedup of 20 over MeZO on RoBERTa-large and OPT-1.3B.

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Chengyi Wang, Yu Wu, Shujie Liu, Ming Zhou, Zhenglu Yang

Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making (2023.findings-acl)

Challenge:	End-to-end speech translation requires a powerful encoder to transcribe, understand and learn cross-lingual semantics simultaneously.
Approach:	They propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages.
Outcome:	The proposed method improves on En-De and En-Fr speech translation benchmarks.

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Xuanjie Fang, Sijie Cheng, Yang Liu, Wei Wang

DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease Questions with Scientific Literature (2024.findings-emnlp)

Challenge:	Pre-trained language models (PLMs) have shown strong potential in various downstream tasks.
Approach:	They propose to model adversarial attack task as a sequential decision-making problem where the whole attack process is sequential with two decision- making problems, i.e., word finder and word substitution.
Outcome:	The proposed approach achieves the highest attack success rate with a comparable modification rate and semantic similarity to attack fine-tuned BERT.

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Dawei Li, Shu Yang, Zhen Tan, Jae Baik, Sukwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen

Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration (2026.acl-long)

Challenge:	Recent advances in large language models have achieved promising performances across various applications, but the challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains.
Approach:	They propose a dynamic co-augmentation framework for the refinement of large language models and knowledge graphs in the context of Alzheimer's Disease.
Outcome:	The proposed framework can be used to study Alzheimer's Disease (AD) using LLMs and KGs.

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Yang Zhao, Yangou Ouyang, Xiao Ding, Hepeng Wang, Bibo Cai, Kai Xiong, Jinglong Gao, Zhouhao Sun, Li Du, Bing Qin, Ting Liu

TongGu: Mastering Classical Chinese Understanding with Knowledge-Grounded Large Language Models (2024.findings-emnlp)

Challenge:	Existing data arbitration strategies for large language model training rely on surface-level heuristics that fail to diagnose intrinsic learning needs.
Approach:	They propose a framework that arbitrates data based on its degree of cognitive conflict with the model's existing knowledge.
Outcome:	Extensive experiments on WebShop and ALFWorld show that PRISM outperforms state-of-the-art hybrid methods while reducing computational costs by up to 3.22 .

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Jiahuan Cao, Dezhi Peng, Peirong Zhang, Yongxin Shi, Yang Liu, Kai Ding, Lianwen Jin

BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capability in Natural Language Processing (NLP), but struggle with Classical Chinese Understanding (CCU) Existing models, including general-purpose and preliminary LLMs, lack the ability to address CCU in data-demanding and knowledge-intensive tasks.
Approach:	They propose to use a classical Chinese corpora-based instruction-tuning dataset to unlock the full CCU potential of LLMs.
Outcome:	The proposed model unlocks the full CCU potential of LLMs by preserving its foundational knowledge while maintaining redundancy-aware tuning (RAT) and CCU-RAG.

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Tianyuan Huang, Zepeng Zhu, Hangdi Xing, Zirui Shao, Zhi Yu, Chaoxiong Yang, Jiaxian He, Xiaozhong Liu, Jiajun Bu

Improving Back-Translation with Uncertainty-based Confidence Estimation (D19-1)

Challenge:	Existing Braille research focuses on isolated tasks while mixed-content Braille tasks face data scarcity and ambiguities.
Approach:	They propose a syntax tree-based augmentation method tailored for Braille data.
Outcome:	The proposed method improves Braille translation, formula-to-Braille conversion, and mixed-text translation.

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Shuo Wang, Yang Liu, Chao Wang, Huanbo Luan, Maosong Sun

Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation (2022.emnlp-main)

Challenge:	Despite the success of low-resource neural machine translation, there is a data scarcity problem in many languages . large-scale, high-quality, and widecoverage bilingual corpora do not exist for most language pairs .
Approach:	They propose to quantify confidence of NMT models based on model uncertainty . they propose to use uncertainty-based confidence measures to improve back-translation .
Outcome:	The proposed model outperforms conventional statistical machine translation (SMT) on Chinese-English and English-German translation tasks.

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Yang Yu, Fangzhao Wu, Chuhan Wu, Jingwei Yi, Qi Liu

Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation (2025.findings-emnlp)

Challenge:	Existing work fine tunes the PLM with the news recommendation task, which can cause a domain shift problem.
Approach:	They propose a self-supervised method to adapt general PLM to news domain with a contrastive matching task between news titles and news bodies.
Outcome:	The proposed method can improve both the effectiveness and efficiency of the large PLM-based news recommendation model while maintaining its performance.

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Bohao Yang, Kun Zhao, Dong Liu, Chen Tang, Liang Zhan, Chenghua Lin

Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation (2022.findings-acl)

Challenge:	Existing evaluation metrics struggle to evaluate adversarial negative examples . existing metrics struggle in handling adversarials, resulting in low correlations with human judgments.
Approach:	They propose a framework that integrates AMR and domain-specific language models for automatic open-domain dialogue evaluation.
Outcome:	The proposed evaluation framework achieves strong correlations with human judgments across multiple datasets.

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Tianchi Yue, Shulin Liu, Huihui Cai, Tao Yang, Shengkang Song, TingHao Yu

Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion (2025.findings-emnlp)

Challenge:	Chinese Grammatical Error Detection is a non-automatic method to detect grammatical errors in texts.
Approach:	They propose a Conditional Non-Autoregressive Error Generation model for Chinese grammatical errors that uses a masking and prediction method to generate a context-dependent error.
Outcome:	The proposed method achieves better performance than all compared data augmentation methods on the CGED-2018 and CGAD-2020 benchmarks.

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Yujian Liu, Jiabao Ji, Tong Yu, Ryan A. Rossi, Sungchul Kim, Handong Zhao, Ritwik Sinha, Yang Zhang, Shiyu Chang

NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2024.lrec-main)

Challenge:	Existing methods to integrate external information into a given table neglect the structured nature of the table.
Approach:	They propose a simple yet effective method to integrate external information into a given table by first building an augmenting table and then generating a SQL query over the two tables to answer the question.
Outcome:	The proposed method outperforms strong baselines on three table QA benchmarks.

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Fuhan Cai, Duo Liu, Zhongqiang Zhang, Ge Liu, Xiaozhe Yang, Xiangzhong Fang

HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition (2024.acl-long)

Challenge:	Hierarchical text classification (HTC) is a challenging task in natural language processing due to its complex taxonomic label hierarchy.
Approach:	They propose to use prompts to model hierarchical text classification (HTC) they propose to introduce conditional random fields and Global Pointer to establish hierarchic dependencies .
Outcome:	The proposed approach achieves state-of-the-art (SoTA) performance on three public datasets.

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Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang

CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents (2025.findings-acl)

Challenge:	Large language models (LLMs) are a promising alternative to expensive human evaluations.
Approach:	They propose a framework that iteratively aligns LLM-based evaluators with human preference . they decompose a given evaluation task into finer-grained criteria .
Outcome:	The proposed framework iteratively aligns LLM-based evaluators with human preference . it decomposes a given evaluation task into finer-grained criteria . the framework is efficient to train and more explainable than relying solely on prompts .

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Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Anjie Yang, Zhaoxuan Jin, Jianbo Deng, Philip Torr, Bernard Ghanem, Guohao Li

E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service (2022.lrec-1)

Challenge:	Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the complexity of constructing tasks and evaluators.
Approach:	They propose a cross-environment agent benchmark framework that integrates graph-based evaluation and task generation methods.
Outcome:	The proposed framework supports multiple devices and can be easily extended to any environment with a Python interface.

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Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He

KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion (2021.findings-acl)

Challenge:	Recent research has focused on developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations.
Approach:	They construct an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders.
Outcome:	The proposed dataset contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders.

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Jie Zhou, Shengding Hu, Xin Lv, Cheng Yang, Zhiyuan Liu, Wei Xu, Jie Jiang, Juanzi Li, Maosong Sun

Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search (2022.emnlp-industry)

Challenge:	Existing studies focus on partial aspects of knowledge abstraction, concretization, and completion (KACC).
Approach:	They propose a unified knowledge graph benchmark to improve existing benchmarks . they collect new datasets that contain larger concept graphs and cross-view links .
Outcome:	The proposed benchmark improves existing benchmarks in terms of dataset scale, task coverage, and difficulty.

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Ziyang Liu, Chaokun Wang, Hao Feng, Lingfei Wu, Liqun Yang

Attention Mechanism with Energy-Friendly Operations (2022.findings-acl)

Challenge:	Existing approaches to e-commerce relevance matching ignore bipartite graphs in logs . experimental results show that proposed method improves human relevance judgment .
Approach:	They propose an efficient knowledge distillation framework for e-commerce relevance matching to exploit the advantages of Transformer-style and classical relevance matching models.
Outcome:	The proposed method significantly improves human relevance judgment on large-scale real-world data.

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Yu Wan, Baosong Yang, Dayiheng Liu, Rong Xiao, Derek Wong, Haibo Zhang, Boxing Chen, Lidia Chao

Challenge:	Empirical results show that attention mechanism can be improved from the energy consumption aspects.
Approach:	They propose to replace multiplications with either selective operations or additions to reduce energy consumption.
Outcome:	The proposed model achieves competitable accuracy while saving 99% and 66% energy during alignment calculation and the whole attention procedure.

Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains (2025.findings-emnlp)

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Yang Wu, Raha Moraffah, Rujing Yao, Jinhong Yu, Zhimin Tao, Xiaozhong Liu

Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning (2026.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated an impressive level of general knowledge, but often struggle in highly specialized domains due to the lack of expert knowledge.
Approach:	They propose a framework to actively engage domain experts within a fixed budget to enhance domain-specific LLMs.
Outcome:	The proposed framework improves LLMs in highly specialized domains while adhering to budget constraints.

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Xuanyu Lei, Chenliang Li, Yuning Wu, Kaiming Liu, Weizhou Shen, Peng Li, Ming Yan, Fei Huang, Ya-Qin Zhang, Yang Liu

Challenge:	Recent advances in Large Language Models (LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited.
Approach:	They propose an Adaptive Curriculum Reinforcement Learning framework to advance long-form writing capabilities beyond SFT.
Outcome:	Experiments on 7B-scale writer models show that Writing-RL improves long-form writing performance over strong SFT baselines.

SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks (2026.acl-long)

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Tianyi Wang, Yixia Li, Long Li, Yibiao Chen, Shaohan Huang, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

Iterative Dual Domain Adaptation for Neural Machine Translation (D19-1)

Challenge:	Proximal Policy Optimization (PPO) is central to aligning Large Language Models with verifiable rewards.
Approach:	They propose a scalable algorithm that harmonizes sample efficiency with stability of outcome-based updates.
Outcome:	The proposed algorithm outperforms standard PPO and matches the performance of computation-heavy group-based methods.

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Jiali Zeng, Yang Liu, Jinsong Su, Yubing Ge, Yaojie Lu, Yongjing Yin, Jiebo Luo

Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models (2026.findings-acl)

Challenge:	Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our proposed framework.
Approach:	They propose an iterative dual domain adaptation framework for neural machine translation that uses multiple corpora to perform bidirectional translation knowledge transfer.
Outcome:	Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of the proposed framework.

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Zhining Liu, Tianyi Wang, Xiao Lin, Penghao Ouyang, Gaotang Li, Ze Yang, Hui Liu, Sumit Keswani, Vishwa Pardeshi, Huijun Zhao, Wei Fan, Hanghang Tong

Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling (D19-1)

Challenge:	Vision-Language Models (VLMs) have advanced multimodal learning, driving progress in cross-modal reasoning.
Approach:	They propose to examine moral robustness of vision-language models by analyzing their moral stances under multimodal perturbations.
Outcome:	The proposed model-agnostic multimodal perturbations expose VLMs to a variety of moral vulnerabilities, including a sycophancy trade-off where stronger instruction-following models are more susceptible to persuasion.

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Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin

Enhancing Lexical Relation Mining with Structured Sememe Knowledge (2026.acl-long)

Challenge:	Existing techniques for relevance and semantic matching cannot be easily adapted to the other.
Approach:	They propose a model that incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness.
Outcome:	The proposed model incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness.

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Hansi Wang, Qiliang Liang, Yue Wang, Yang Liu

Yang Zhao, Li Du, Xiao Ding, Yangou Ouyang, Hepeng Wang, Kai Xiong, Jinglong Gao, Zhouhao Sun, Dongliang Xu, Qing Yang, Dongchen Li, Bing Qin, Ting Liu

Challenge:	Existing top-performing methods for Lexical Relation Mining rely on pre-trained language models yet fail to distinguish nuanced lexical relations.
Approach:	They propose a framework to leverage structured sememe knowledge to enhance LRC and LE.
Outcome:	The proposed method outperforms existing methods on benchmarks and outperformed the LLMs.

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Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning (2024.acl-long)

Challenge:	Existing methods for selecting training data from general datasets fail to account for the joint distribution of instructions, resulting in inefficient learning and suboptimal knowledge transfer.
Approach:	They propose a method that constructs a mixed gradient-based instruction graph to capture the joint distribution and interdependencies among instructions.
Outcome:	The proposed method outperforms existing methods on domain adaptation tasks and in complex, data-scarce scenarios.

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Zhaorui Yang, Tianyu Pang, Haozhe Feng, Han Wang, Wei Chen, Minfeng Zhu, Qian Liu

MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct (2025.findings-acl)

Challenge:	Experimental results show that fine-tuning of large language models for specific tasks can be challenging . distribution shift during fine-timing can lead to performance degradation in general task capabilities .
Approach:	They propose a new approach that bridges the distribution gap between task datasets and LLMs by guiding fine-tuning with a distilled dataset generated by the model itself.
Outcome:	The proposed approach achieves comparable or superior performance on downstream tasks compared to the vanilla approach.

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Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Yongbin Li, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Hamid Alinejad-Rokny, Xiaobo Xia, Jingkuan Song, Fei Huang

AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities (2023.findings-acl)

Challenge:	a new framework for image-text instruction data evolution improves MLLM performance . lack of high-quality instruction data remains a major bottleneck in ML modeling .
Approach:	They propose a multimodal instruction data evolution framework that iteratively enhances data quality through fine-grained perception, cognitive reasoning, and interaction evolution.
Outcome:	The proposed approach improves MLLM performance in nine vision-language tasks while using significantly less data.

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Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Qinghong Yang, Ledell Wu

VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment (2024.emnlp-main)

Challenge:	Existing methods to build a strong multilingual multimodal representation model are lacking in good-quality text-image pairs.
Approach:	They propose a method to build a strong multilingual multimodal representation model using English text-image pairs instead of a model from scratch.
Outcome:	The proposed model outperforms the original CLIP model on multilingual multimodal benchmarks.

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Lei Li, Zhihui Xie, Mukai Li, Shunian Chen, Peiyi Wang, Liang Chen, Yazheng Yang, Benyou Wang, Lingpeng Kong, Qi Liu

EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents (2026.findings-acl)

Challenge:	Large vision-language models (LVLMs) are evolving rapidly and require data with human supervision to achieve better alignment.
Approach:	They introduce VLFeedback, the first large-scale vision-language feedback dataset . they train Silkie, an LVLM fine-tuned via direct preference optimization .
Outcome:	The proposed model outperforms its base model in helpfulness, visual faithfulness, and safety metrics and exhibits enhanced resilience against red-teaming attacks.

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Yaoqi Guo, Ying Xiao, Jie M. Zhang, Mark Harman, Yiling Lou, Yang Liu, Zhenpeng Chen

REIC: RAG-Enhanced Intent Classification at Scale (2025.emnlp-industry)

Challenge:	Large language models are reshaping modern software development, but they often incur substantial monetary cost.
Approach:	They propose an experience-driven early termination approach that extracts structured experience from prior issue-resolution executions and leverages it to guide early termination during patch generation and selection.
Outcome:	The proposed approach reduces cost by 19%–55% with negligible loss in resolution rate (at most 0.2%) EET extracts structured experience from prior issue-resolution executions and leverages it to guide early termination during patch generation and selection.

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Ziji Zhang, Michael Yang, Zhiyu Chen, Yingying Zhuang, Shu-Ting Pi, Qun Liu, Rajashekar Maragoud, Vy Nguyen, Anurag Beniwal

Werewolf Among Us: Multimodal Resources for Modeling Persuasion Behaviors in Social Deduction Games (2023.findings-acl)

Challenge:	Accurate intent classification is critical for efficient routing in customer service . however, as companies expand their product lines, intent classification faces scalability challenges .
Approach:	They propose a retrieval-augmented generation Enhanced Intent Classification approach which leverages retrieval augmented generation to integrate relevant knowledge into a model.
Outcome:	The proposed approach outperforms fine-tuning, zero-shot, and few-shot methods on real-world datasets.

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Bolin Lai, Hongxin Zhang, Miao Liu, Aryan Pariani, Fiona Ryan, Wenqi Jia, Shirley Anugrah Hayati, James Rehg, Diyi Yang

Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion (2024.acl-long)

Challenge:	Existing studies on persuasive behavior modeling focus on textual dialogues . a multimodal dataset is available for persuasion modeling .
Approach:	They propose a multimodal dataset for modeling persuasive behaviors using visual signals.
Outcome:	The proposed dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting and 26,647 utterance level annotations of persuasion strategy and game level annotation of deduction game outcomes.

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Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu

Towards Comprehensive Description Generation from Factual Attribute-value Tables (P19-1)

Challenge:	Multimodal Large Language Models (MLLMs) lack understanding of multi-image and interleaved inputs due to the visual features encoded by frozen encoders before being fed into the LLM backbone.
Approach:	They propose a two phase paradigm to enable in-depth multimodal context fusion prior to feeding the features into LLMs.
Outcome:	The proposed paradigm boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively.

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Tianyu Liu, Fuli Luo, Pengcheng Yang, Wei Wu, Baobao Chang, Zhifang Sui

SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark (2024.acl-short)

Challenge:	Existing models for comprehensive descriptions for factual attribute-value tables might suffer from missing key attributes and groundless information problems.
Approach:	They propose a force attention method to encourage the generator to pay more attention to uncovered attributes to avoid potential key attributes missing.
Outcome:	The proposed model outperforms the state-of-the-art baselines on automatic and human evaluation.

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Zhenwen Liang, Kehan Guo, Gang Liu, Taicheng Guo, Yujun Zhou, Tianyu Yang, Jiajun Jiao, Renjie Pi, Jipeng Zhang, Xiangliang Zhang

Vision-Language Models Can Self-Improve Reasoning via Reflection (2025.naacl-long)

Challenge:	SceMQA focuses on core science subjects including Mathematics, Physics, Chemistry, and Biology.
Approach:	They propose to use SceMQA to evaluate multimodal question answering at college entrance level.
Outcome:	The proposed model provides specific knowledge points for each problem and detailed explanations for each answer.

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Kanzhi Cheng, Li YanTao, Fangzhi Xu, Jianbing Zhang, Hao Zhou, Yang Liu

ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)

Challenge:	Chain-of-thought (CoT) has been shown to improve the reasoning capability of large language models (LLMs).
Approach:	They propose a framework which iteratively enhances the model’s Vision-language Reasoning by Reflecting on CoT Rationales.
Outcome:	The proposed framework improves multimodal reasoning on vision-language tasks by 23% to 60% over baselines.

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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin

ETR: Entropy Trend Reward for Efficient Chain-of-Thought Reasoning (2026.acl-long)

Challenge:	Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0.
Approach:	They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes.
Outcome:	The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation.

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Xuan Xiong, Huan Liu, Li Gu, Zhixiang Chi, Yue Qiu, Yuanhao YU, Yang Wang

JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs (2025.acl-long)

Challenge:	Existing methods to shorten CoTs use length penalties or global entropy reduction . Existing approaches to CoT reasoning have significant practical drawbacks .
Approach:	They propose a method that shortens CoTs by length penalties or global entropy reduction . they integrate ETR into Group Relative Policy Optimization and evaluate it .
Outcome:	The proposed objective improves accuracy–efficiency trade-off by +9.9% while reducing CoT length by 67% across four benchmarks.

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Junjie Chu, Yugeng Liu, Ziqing Yang, Xinyue Shen, Michael Backes, Yang Zhang

Adaptive Prompt Optimization for Open-Ended Tasks: Uncertainty Preference as a Secondary Signal (2026.findings-acl)

Challenge:	Large language models (LLMs) have been used to mitigate misuse and to align with human values.
Approach:	They propose to use large-scale evaluations of various jailbreak attacks to identify key patterns and test them under eight advanced defenses.
Outcome:	The proposed attacks achieve high success rates but are easy to mitigate by defenses.

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Shuyang Zhang, Zhixuan Liu, Zhichen Dong, Hao Zhang, Chaochao Lu, Chao Yang

QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models (2025.emnlp-main)

Challenge:	Recent training-free prompt optimizers treat performance as maximizing a single scalar score and ignore a second signal that the desired style is task dependent.
Approach:	They propose a semantic-entropy-based method that uses task uncertainty to guide prompt optimization by selecting high-entropicy candidates for creative tasks and low-energetic candidates for conservative ones.
Outcome:	The proposed method outperforms baselines on MT-Bench subsets and integrates easily into existing prompt optimizers.

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Jiajun Zhou, Yifan Yang, Kai Zhen, Ziyue Liu, Yequan Zhao, Ershad Banijamali, Athanasios Mouchtaris, Ngai Wong, Zheng Zhang

A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning (2020.coling-main)

Challenge:	Large Language Models (LLMs) are quantized to lower precision to reduce memory cost and latency in inference.
Approach:	They propose a quantized zeroth-order framework for fine-tuning Large Language Models (LLMs) using low-precision forward passes.
Outcome:	The proposed method achieves comparable results to first-order methods in FP8 and superior accuracy in INT8 and INT4 training.

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Mingtong Liu, Erguang Yang, Deyi Xiong, Yujie Zhang, Yao Meng, Changjian Hu, Jinan Xu, Yufeng Chen

Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System (2025.findings-acl)

Challenge:	Paraphrase generation is of great importance for many downstream tasks in natural language processing.
Approach:	They propose a method to generate sentences as learning objectives from the learned data distribution and employ reinforcement learning to combine these new learning objectives for model training.
Outcome:	The proposed method gains significant diversity and improves generation quality over state-of-the-art datasets.

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Weize Chen, Jiarui Yuan, Chen Qian, Cheng Yang, Zhiyuan Liu, Maosong Sun

Learning to Selectively Learn for Weakly-supervised Paraphrase Generation (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) have emerged as powerful tools for a wide range of tasks, from * Equal Contribution.
Approach:	They propose a framework that enhances communication efficiency and task effectiveness in LLM-based multi-agent systems through training.
Outcome:	The proposed framework improves communication efficiency and task effectiveness on multi-agent tasks with 2.8x performance gain with less than 10% tokens on tasks requiring heavy information exchange.

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Kaize Ding, Dingcheng Li, Alexander Hanbo Li, Xing Fan, Chenlei Guo, Yang Liu, Huan Liu

On the Hallucination in Simultaneous Machine Translation (2024.acl-short)

Challenge:	Existing approaches to generate paraphrases with weak supervision are limited in real-world scenarios due to the lack of coherent and controllable generated paraphrase.
Approach:	They propose a method to generate high-quality paraphrases with weak supervision . they obtain abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion .
Outcome:	The proposed approach achieves significant improvements over existing methods and is even comparable in performance with supervised state-of-the-arts.

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Meizhi Zhong, Kehai Chen, Zhengshan Xue, Lemao Liu, Mingming Yang, Min Zhang

Harmonizing the Past, Present, and Future: A Null-Space Constrained Region-Specific Method for Continual Learning in LLMs (2026.acl-long)

Challenge:	Currently, there are no studies which systematically analyze hallucination in SiMT.
Approach:	They conduct a comprehensive analysis of hallucination in simultaneous machine translation (SiMT) they find that halluciation is extremely severe, especially as latency increases .
Outcome:	The results show that it is possible to alleviate hallucination by decreasing the over usage of target-side information for SiMT.

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Jinhui Chen, Shizhu He, Xingchang Yang, Huanxuan Liao, Yequan Wang, Xiangwen Liao, Wenhao Teng, Kang Liu, Jun Zhao

Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones? (2024.emnlp-main)

Challenge:	Existing continual learning paradigms prioritize instant performance through dense updates, leading to catastrophic forgetting and rapid exhaustion of model capacity.
Approach:	They propose a method that preserves previously acquired knowledge and acquires new task-specific skills while preserving sufficient parameter capacity for subsequent adaptation.
Outcome:	The proposed method is based on the brain's functional partitioning and can be used to map tasks between specialized and generalist neurons.

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Zhe Yang, Yichang Zhang, Tianyu Liu, Jian Yang, Junyang Lin, Chang Zhou, Zhifang Sui

Leveraging Prefix Transfer for Multi-Intent Text Revision (2023.acl-short)

Challenge:	Large language models (LLMs) have impressive capabilities, but still suffer from inconsistency issues.
Approach:	They develop a ConsisEval benchmark to evaluate LLMs' inconsistency . they find that LLM models can paradoxically fail at easier problems .
Outcome:	The proposed model achieves highest consistency score but inconsistent to specific questions due to distraction by redundant information, misinterpretation of questions, etc.

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Ruining Chong, Cunliang Kong, Liu Wu, Zhenghao Liu, Ziye Jin, Liner Yang, Yange Fan, Hanghang Fan, Erhong Yang

Challenge:	Text revision is a necessary process to improve text quality.
Approach:	They propose a multi-intent text revision system that can revise texts without explicit intent annotation.
Outcome:	The proposed system outperforms baselines on the IteraTeR dataset and significantly improves the SARI score with more than 3% improvement.

TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement (2025.findings-naacl)

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Zhaopeng Feng, Yan Zhang, Hao Li, Bei Wu, Jiayu Liao, Wenqiang Liu, Jun Lang, Yang Feng, Jian Wu, Zuozhu Liu

DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling (2020.findings-emnlp)

Challenge:	Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). human evaluations reveal that LLM-generated translations still contain various errors.
Approach:	They propose a LLM-based self-refinement framework that feeds error information back into LLMs to facilitate self-finement, leading to enhanced translation quality.
Outcome:	The proposed framework outperforms internal refinement and feedback methods while ensuring a robust translation quality baseline.

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Jiecao Chen, Liu Yang, Karthik Raman, Michael Bendersky, Jung-Jung Yeh, Yun Zhou, Marc Najork, Danyang Cai, Ehsan Emadzadeh

Navigating the Infinite Dynamic Web Space: Effective In-Context Exploration via Cognitive Multi-Agent Collaboration (2026.eacl-long)

Challenge:	Existing knowledge distillation models are not optimized for dealing with pairs (or tuples) of texts.
Approach:	They propose a framework for distilling fast and accurate models on text pair tasks using a scalable end-to-end training strategy.
Outcome:	Empirical studies on academic and real-world e-commerce benchmarks show the proposed framework can achieve speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model.

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Guozhao Mo, Yanjiang Liu, Yafei Shi, Jiawei Chen, Yang Li, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Bo Zheng, Xianpei Han

RLKGF: Reinforcement Learning from Knowledge Graph Feedback Without Human Annotations (2025.findings-acl)

Challenge:	Existing methods for dynamic web navigation rely on greedy strategies or value estimation, struggle to achieve effective backtracking and are heavily dependent on proprietary models.
Approach:	They propose a cognitive multi-agent collaboration framework that enhances cyberspace exploration capability through In-Context Exploration.
Outcome:	The proposed framework surpasses the proprietary model Claude-3.5 Sonnet on the WebArena benchmark.

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Lian Yan, Chen Tang, Yi Guan, Haotian Wang, Songyuan Wang, Haifeng Liu, Yang Yang, Jingchi Jiang

Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision (2021.emnlp-main)

Challenge:	Lack of human preference labels remains a significant bottleneck when applying RLHF to a downstream domain.
Approach:	They propose a method that leverages human priors encoded in Knowledge Graphs (KGs) to derive RL rewards in the absence of manual annotations.
Outcome:	Experiments on three public and one private medical dialogue datasets show that the proposed method outperforms the competitive RLAIF in improving LLM diagnostic accuracy.

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Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, Maosong Sun

A Reinforcement Learning Framework for Robust and Secure LLM Watermarking (2026.eacl-long)

Challenge:	Recent state-of-the-art (SOTA) effective neural network methods have been used in Chinese word segmentation (CWS) However, the robustness of the previous neural methods is limited by the large-scale annotated corpus.
Approach:	They propose a self-supervised Chinese word segmentation approach with a straightforward and effective architecture.
Outcome:	The proposed approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness.

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Li An, Yujian Liu, Yepeng Liu, Yuheng Bu, Yang Zhang, Shiyu Chang

Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation (2025.emnlp-main)

Challenge:	Existing watermarking algorithms rely on heuristic green/red token lists . however, these lists are inconsistent and can be compromised .
Approach:	They propose a framework for robust and secure LLM watermarking using reinforcement learning.
Outcome:	The proposed method achieves state-of-the-art trade-off across all criteria with notable improvements in resistance to spoofing attacks without degrading other criteria.

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Dingwei Chen, Ziqiang Liu, Feiteng Fang, Chak Tou Leong, Shiwen Ni, Ahmadreza Argha, Hamid Alinejad-Rokny, Min Yang, Chengming Li

Listen, Watch, and Learn to Feel: Retrieval-Augmented Emotion Reasoning for Compound Emotion Generation (2025.findings-acl)

Challenge:	Existing approaches to generating factually inconsistent outputs are resource-intensive.
Approach:	They propose a plug-and-play intervention designed to enhance factuality by inserting premature layers formed through mathematical interpolation with adjacent layers.
Outcome:	The proposed intervention reduces hallucinations while outperforming baselines on four datasets.

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Zhuofan Wen, Zheng Lian, Shun Chen, Hailiang Yao, Longjiang Yang, Bin Liu, Jianhua Tao

Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution (2022.emnlp-main)

Challenge:	Existing methods to assess human emotion are limited by the subjective nature of emotion perception, limiting the robustness of existing models.
Approach:	They propose a plug-and-play module that enhances MLLMs’ ability to tackle compound and context-rich emotion tasks.
Outcome:	The proposed framework improves MLLMs' ability to tackle compound and context-rich emotion tasks and the Compound Emotion QA dataset shows it performs well across both benchmarks and evaluation frameworks.

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Aiwei Liu, Honghai Yu, Xuming Hu, Shu’ang Li, Li Lin, Fukun Ma, Yawen Yang, Lijie Wen

Rethinking Data Augmentation in Text-to-text Paradigm (2022.coling-1)

Challenge:	Existing methods to attack transformer models are not effective at character level, but they are a natural attack scenario.
Approach:	They propose a character-level adversarial attack method against transformer models . they use a gradient-based method to find the most vulnerable words in a sentence .
Outcome:	The proposed method outperforms previous methods on sentence-level and token-level tasks.

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Yanan Chen, Yang Liu

AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)

Challenge:	Existing approaches to augment training data are limited or marginal, or even diminishing or adverse especially given original training corpus is relatively sufficient or the backbone classifiers are PLM based.
Approach:	They propose to integrate text-to-text language models and construct a new two-phase framework for augmentation using two novel schemes.
Outcome:	The proposed framework synthesizes new samples benefiting from the knowledge learned from pre-trained language models on two public classification datasets and shows remarkable gains.

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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun

OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding (2026.acl-long)

Challenge:	Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs.
Approach:	They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction.
Outcome:	The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI.

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Deming Ding, Shichun Liu, Enhui Yang, Jiahang Lin, Ziying Chen, Shihan Dou, Honglin Guo, Weiyu Cheng, Pengyu Zhao, Chengjun Xiao, Qunhong Zeng, Qi Zhang, Xuanjing Huang, Qidi Xu, Tao Gui

Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future (2023.emnlp-main)

Challenge:	coding scaffolds that follow heterogeneous instructions remain under-examined in software engineering . coding models are capable software agents, but their ability to follow constraints remains under-explored .
Approach:	They introduce OctoBench, which benchmarks scaffold-aware instruction following in agentic coding.
Outcome:	The proposed benchmark aims to accelerate the development of more scaffold-aware agents.

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Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang

DataTales: A Benchmark for Real-World Intelligent Data Narration (2024.emnlp-main)

Challenge:	Existing literature on the generalization of machine learning models to out-of-distribution data is lacking.
Approach:	They propose to present the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding.
Outcome:	The proposed survey provides the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding.

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Yajing Yang, Qian Liu, Min-Yen Kan

Judge as A Judge: Improving the Evaluation of Retrieval-Augmented Generation through the Judge-Consistency of Large Language Models (2025.findings-acl)

Challenge:	Existing benchmarks fail to capture the requisite analytical complexity for practical applications.
Approach:	They propose a benchmark to assess the proficiency of language models in data narration.
Outcome:	The proposed model combines financial reports with market data to demonstrate proficiency in data narration.

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Shuliang Liu, Xinze Li, Zhenghao Liu, Yukun Yan, Cheng Yang, Zheni Zeng, Zhiyuan Liu, Maosong Sun, Ge Yu

AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)

Challenge:	Existing evaluation metrics cannot fairly evaluate the outputs of RAG models during training and evaluation.
Approach:	They propose a method which prompts LLMs to generate different judgments based on various combinations of judgment dimensions and utilizes the judge-consistency to evaluate these judgments.
Outcome:	The proposed method generates more accurate evaluations for RAG models across different RAG model and datasets.

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Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei

Effective Convolutional Attention Network for Multi-label Clinical Document Classification (2021.emnlp-main)

Challenge:	Large language models are increasingly employed to empower autonomous agents to simulate human behavior.
Approach:	They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts.
Outcome:	The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning.

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Yang Liu, Hua Cheng, Russell Klopfer, Matthew R. Gormley, Thomas Schaaf

The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions (2023.emnlp-main)

Challenge:	a large number of medical encounters need to be coded everyday due to long document sets and large label set.
Approach:	They propose a convolutional attention network for multi-label document classification problem . they use convolution-based encoders and convolution networks to aggregate information across documents .
Outcome:	The proposed model outperforms prior best model and multilingual Transformer model on a widely used dataset in the medical domain.

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Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han

EvolvR: Self-Evolving Pairwise Reasoning for Story Evaluation to Enhance Generation (2026.acl-long)

Challenge:	Recent advances in large language models (LLMs) have produced models that exhibit remarkable performance across a variety of NLP tasks.
Approach:	They analyze a large-scale collection of user-GPT conversations to identify a significant gap between academic research in NLP and the needs of real-world NLP applications.
Outcome:	The proposed model outperforms existing models in a large-scale collection of user-GPT conversations and identifies a significant gap between the tasks that users frequently request from LLMs and the tasks commonly studied in academic research.

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Xinda Wang, Zhengxu Hou, Yangshijie Zhang, null Yanbingren, Jialin Liu, ChenZhuo Zhao, Zhibo Yang, Bin-Bin Yang, Feng Xiao

MCS: An In-battle Commentary System for MOBA Games (2022.coling-1)

Challenge:	Existing methods for story evaluation lack reasoning capabilities for open-source models . evolvR framework provides high-fidelity evaluators for story generation tasks .
Approach:	They propose a framework that self-synthesizes chain-of-thought data via a multi-persona strategy . they propose evolvR to provide a reward model for story generation .
Outcome:	The proposed framework achieves state-of-the-art performance on three evaluation benchmarks . it also enhances the quality of generated stories, validating the superiority of the framework .

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Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li

Challenge:	In-battle commentary is an important component of live streaming of e-sports competitions and is applicable to a wide range of scenarios like combat information analysis and live streaming.
Approach:	They propose a generative system for in-battle real-time commentary in mobile MOBA games and propose 'transform' method to convert match statistics and utterances into consistent encoding space.
Outcome:	The proposed system is based on real-time match statistics and events and can be used for live streaming, e-sports commentary and combat information analysis.

Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)

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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang

LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation (2025.emnlp-industry)

Challenge:	Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets.
Approach:	They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains.
Outcome:	The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings.

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Weizhi Zhang, Liangwei Yang, Wooseong Yang, Henry Peng Zou, Yuqing Liu, Ke Xu, Sourav Medya, Philip S. Yu

Continual Pretraining on Encrypted Synthetic Data for Privacy-Preserving LLMs (2026.findings-eacl)

Challenge:	Existing algorithms for collaborative filtering are limited by their computational demands and latency.
Approach:	They propose a framework to integrate pre-trained LLM embeddings into CF models through selective initialization strategies.
Outcome:	The proposed framework improves recommendation performance while maintaining low computational costs.

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Honghao Liu, Xuhui Jiang, Chengjin Xu, Cehao Yang, Yiran Cheng, Lionel Ni, Jian Guo

Multi-Programming Language Sandbox for LLMs (2025.acl-demo)

Challenge:	Existing methods to protect PII from training on small corpora are difficult to implement in real-world applications.
Approach:	They propose an entity-based framework that synthesizes encrypted training data to protect PII.
Outcome:	The proposed framework outperforms base models and ensures PII security on limited-scale datasets while exhibiting a modest performance gap compared to models trained on unencrypted synthetic data.

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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang

Why Do More Experts Fail? A Theoretical Analysis of Model Merging (2026.acl-long)

Challenge:	MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Approach:	They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models.
Outcome:	The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs).

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Zijing Wang, Xingle Xu, YongKang Liu, Yiqun Zhang, Peiqin Lin, Shi Feng, Daling Wang, Xiaocui Yang, Hinrich Schuetze

Challenge:	Existing methods for model merging struggle to maintain performance gains as the number of merged models increases.
Approach:	They propose a Reparameterized Heavy-Tailed method to extend the merged model’s coverage and enhance performance.
Outcome:	The proposed method extends the merged model’s coverage and enhances performance on 19 benchmarks, including knowledge-intensive and general-purpose tasks.

VPL: Visual Proxy Learning Framework for Zero-Shot Medical Image Diagnosis (2024.findings-emnlp)

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Jiaxiang Liu, Tianxiang Hu, Huimin Xiong, Jiawei Du, Yang Feng, Jian Wu, Joey Zhou, Zuozhu Liu

Generating Summaries with Topic Templates and Structured Convolutional Decoders (P19-1)

Challenge:	Insufficient medical text precision and the modal disparity between text and vision spaces pose challenges for vision-language models like CLIP.
Approach:	They propose a visual proxy learning framework that combines a text refinement module and a stable Sinkhorn algorithm to enhance the diagnostic performance.
Outcome:	The proposed model outperforms the state-of-the-art CLIP inference by 1.69% to 15.31% on five datasets covering various diseases.

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Laura Perez-Beltrachini, Yang Liu, Mirella Lapata

Multi-Agent Collaboration via Cross-Team Orchestration (2025.findings-acl)

Challenge:	Existing neural generation approaches create multi-sentence text as a single sequence . Existing approaches create multiple sentences as if they were a sequence based on content structure .
Approach:	They propose a structured convolutional decoder that is guided by the content structure of target summaries.
Outcome:	The proposed model outperforms existing decoders on three datasets representing different domains.

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Zhuoyun Du, Chen Qian, Wei Liu, Zihao Xie, YiFei Wang, Rennai Qiu, Yufan Dang, Weize Chen, Cheng Yang, Ye Tian, Xuantang Xiong, Lei Han

Challenge:	Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents.
Approach:	They propose a framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation.
Outcome:	Experiments show that the framework can generate better software quality compared to state-of-the-art frameworks.

FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging (2025.acl-long)

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Zichen Tang, Haihong E, Ziyan Ma, Haoyang He, Jiacheng Liu, Zhongjun Yang, Zihua Rong, Rongjin Li, Kun Ji, Qing Huang, Xinyang Hu, Yang Liu, Qianhe Zheng

LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety (2026.acl-long)

Challenge:	Compared to existing benchmarks, FinanceReasoning provides three key advancements: (1) credibility; (2) comprehensiveness; (3) numerical precision; (4) complexity; (5) complexity; and (6) complexity.
Approach:	They propose a benchmark to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems.
Outcome:	The proposed benchmark exceeds existing benchmarks in 67.8% of financial concepts and formulas and is credible, comprehensive, and challenging.

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Junxiao Yang, Haoran Liu, Jinzhe Tu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Jiaqi Weng, Jialing Tao, Hui Xue, Hongning Wang, Han Qiu, Minlie Huang

Challenge:	Large language models (LLMs) have demonstrated better safety performance in high-resource languages than in low-resourced languages.
Approach:	They propose language-agnostic semantic alignment (LASA) which anchors safety alignment directly in semantic bottlenecks.
Outcome:	The proposed approach significantly improves safety across all languages: average attack success rate drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains within 3–4% across Qwen2.5 and Qwend3 Instruct models (7B–32B).

Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning (2022.coling-1)

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Xiusheng Huang, Hang Yang, Yubo Chen, Jun Zhao, Kang Liu, Weijian Sun, Zuyu Zhao

Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference (2025.naacl-long)

Challenge:	Existing document-level relation extraction methods are sparse in relational entity pairs and the representation of entity pairs is insufficient.
Approach:	They propose a Pair-Aware and Entity-Enhanced(PAEE) model to solve two challenges . they propose predicting potential relational entity pairs and assembling directional entity pairs .
Outcome:	The proposed model can obtain state-of-the-art performance on four benchmark datasets . it can predict potential relational entity pairs and assemble directional entity pairs .

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Zeping Li, Xinlong Yang, Ziheng Gao, Ji Liu, Guanchen Li, Zhuang Liu, Dong Li, Jinzhang Peng, Lu Tian, Emad Barsoum

Fusing Context Into Knowledge Graph for Commonsense Question Answering (2021.findings-acl)

Challenge:	Existing methods such as Medusa lack adequate information interaction between different drafting heads.
Approach:	They propose an enhanced speculative decoding framework that builds upon Medusa and integrates a drafting block capable of parallel inference.
Outcome:	The proposed framework outperforms Medusa in terms of head accuracy and latency.

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Yichong Xu, Chenguang Zhu, Ruochen Xu, Yang Liu, Michael Zeng, Xuedong Huang

Towards Database-Free Text-to-SQL Evaluation: A Graph-Based Metric for Functional Correctness (2025.coling-main)

Challenge:	Existing methods to combine language modeling and knowledge graphs (KG) lack the context to provide a more precise understanding of the concepts.
Approach:	They propose to use external entity descriptions to provide contextual information for commonsense question answering models.
Outcome:	The proposed model achieves state-of-the-art among non-generative models in OpenBookQA and is the first of its kind in the field.

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Yi Zhan, Longjie Cui, Han Weng, Guifeng Wang, Yu Tian, Boyi Liu, Yingxiang Yang, Xiaoming Yin, Jiajun Xie, Yang Sun

Causal Intervention-based Few-Shot Named Entity Recognition (2023.findings-emnlp)

Challenge:	Existing metrics for evaluating functional correctness of SQL queries are prone to false positives due to inadequately prepared test databases.
Approach:	They propose a graph-based metric that uses a relational operator tree to extract rich semantic information from the logical execution plan of SQL queries and embed it into a diagram.
Outcome:	The proposed method eliminates the need for extensive test database preparation and performs graph matching on unseen SQL queries.

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Zhen Yang, Yongbin Liu, Chunping Ouyang

Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach (P19-1)

Challenge:	Existing methods to perform few-shot named entity recognition are limited and overfitting is caused by the spurious correlation resulting from the bias in selecting a few samples.
Approach:	They propose a causal intervention-based few-shot named entity recognition method that blocks the backdoor path between context and label.
Outcome:	The proposed method achieves state-of-the-art in a few-shot named entity recognition (NER) task.

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Zonghan Yang, Yong Cheng, Yang Liu, Maosong Sun

Efficient Prior-Guided Reasoning for Robust Retrieval-Augmented Generation under Conflicts (2026.acl-long)

Challenge:	Existing methods for reducing word omission errors in neural machine translation are prone to omit essential words on the source side.
Approach:	They propose a contrastive learning approach to reduce word omission errors in NMT by omitting words.
Outcome:	The proposed approach achieves better translation performance than baseline methods on Chinese-to-English, German-to English, and Russian-toEnglish translation tasks.

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Xiaowei Yuan, Ziyang Huang, Zhao Yang, Yequan Wang, Jun Zhao, Kang Liu

Adversarial Training for Machine Reading Comprehension with Virtual Embeddings (2021.starsem-1)

Challenge:	Retrieval-Augmented Generation (RAG) has become a standard paradigm for grounding Large Language Models (LLMs) however, performance degrades substantially when faced with noisy, outdated, or conflicting retrieved information.
Approach:	They propose a framework that explicitly elicits the model’s parametric knowledge as prior information to guide reasoning on retrieved documents.
Outcome:	The proposed framework achieves robust performance across varying degrees of external inconsistency and noise.

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Ziqing Yang, Yiming Cui, Chenglei Si, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency (2024.lrec-main)

Challenge:	Neural networks are vulnerable to adversarial examples that have been mixed with certain perturbations.
Approach:	They propose a novel adversarial training method that perturbs the embedding matrix instead of word vectors to differentiate the roles of passages and questions.
Outcome:	The proposed method is effective universally and further improves the performance of MRC tasks.

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Yuchen Shi, Deqing Yang, Jingping Liu, Yanghua Xiao, Zongyu Wang, Huimin Xu

InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection (2026.eacl-long)

Challenge:	Negation understanding is crucial to many downstream tasks such as sentiment analysis, question answering, Web search and natural language inference.
Approach:	They propose a novel negation triplet extraction task which aims to extract negation subject along with negation cue and scope.
Outcome:	The proposed model is based on a generative pretrained language model with a multi-task learning framework and achieves the best performance compared to baselines.

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Yuhang Liu, Pengxiang Li, Zishu Wei, Congkai Xie, Xueyu Hu, Xinchen Xu, Shengyu Zhang, Xiaotian Han, Hongxia Yang, Fei Wu

HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models (2025.acl-long)

Challenge:	Existing GUI Agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness.
Approach:	They propose an MLLM-based GUI Agent with a two-stage supervised fine-tuning pipeline that enhances GUI understanding and grounding.
Outcome:	InfiGUIAgent achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks.

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Songtao Jiang, Yan Zhang, Yeying Jin, Zhihang Tang, Yangyang Wu, Yang Feng, Jian Wu, Zuozhu Liu

GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification (P19-1)

Challenge:	Existing methods for medical vision-language models overlook modality misalignment . HSCR generates high-quality preference data with higher sampling probability .
Approach:	They propose a hierarchical self-contrastive reward approach that addresses two challenges in alignment . they leverage the inherent capability of Med-VLMs to generate dispreferred responses .
Outcome:	The proposed approach improves accuracy and trustworthiness of medical vision-label models with 2,000 training entries.

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Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun

MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)

Challenge:	Existing methods to extract information from evidence are unable to grasp relational and logical information among the evidence.
Approach:	They propose a graph-based evidence aggregating and reasoning framework to integrate evidence from multiple pieces of evidence.
Outcome:	The proposed framework achieves significant performance improvements on a large-scale benchmark dataset.

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Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang

In-Context Demonstration Selection with Cross Entropy Difference (2023.findings-emnlp)

Challenge:	Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs.
Approach:	They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair.
Outcome:	The proposed framework integrates graphical information of two molecules in pair.

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Dan Iter, Reid Pryzant, Ruochen Xu, Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu

RTADev: Intention Aligned Multi-Agent Framework for Software Development (2025.findings-acl)

Challenge:	Large language models (LLMs) can use in-context demonstrations to improve performance on zero-shot tasks.
Approach:	They propose a cross-entropy difference method for selecting in-context demonstrations that uses parameter efficient finetuning to train small models on training data.
Outcome:	The proposed method outperforms baseline selection methods on a mix-domain dataset and shows that the effectiveness of in-context demonstrations negatively correlates with the perplexity of the test example.

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Jie Liu, Guohua Wang, Ronghui Yang, Jiajie Zeng, Mengchen Zhao, Yi Cai

SafeMCP: Proactive Power Regulation for LLM Agent Defense via Environment-Grounded Look-Ahead Reasoning (2026.acl-long)

Challenge:	Large Language Models (LLMs) are efficient assistants to humans in software development tasks, but they can cause errors during the development process.
Approach:	They propose an intention aligned multi-agent framework that ensures that all agents work based on a consensus.
Outcome:	The proposed framework reduces errors and improves the quality of generated software code.

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Lichao Wang, ZhaoXing Ren, Tianzhuo Yang, Jiaming Ji, Chi Harold Liu, Yaodong Yang, Juntao Dai

FlowHN: Adaptive Token Routing for Efficient Parallel Hybrid Networks (2026.acl-industry)

Challenge:	Large Language Model (LLM) agents are expanding their action spaces to operate in complex environments.
Approach:	They propose a server-side defense plugin that constrains tool acquisition via predictive reasoning regarding future safety risks.
Outcome:	Experiments on PowerSeeking Bench, ToolEmu, and AgentHarm show that SafeMCP achieves a safe equilibrium, effectively mitigating risks while preserving agent utility.

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Mohammad Mahdi Moradi, Walid Ahmed, Shuangyue Wen, Sudhir Mudur, Weiwei Zhang, Yang Liu

ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study (2025.findings-acl)

Challenge:	Existing hybrids lack performance, latency, and cost-efficient scaling for production LLMs.
Approach:	They propose a deployment-oriented parallel hybrid architecture that enables deterministic conditional computation via FLOP-aware token circulation across attention and SSM branches.
Outcome:	FlowHN achieves 4 higher throughput and 15% higher MFU than current models while maintaining competitive accuracy on reasoning, coding, and long-context tasks.

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Eric Modesitt, Ke Yang, Spencer Hulsey, Xin Liu, ChengXiang Zhai, Volodymyr Kindratenko

Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models (2024.emnlp-main)

Challenge:	General-purpose models lack depth for expert-level tasks because of limited domain-specific information.
Approach:	They propose a method for curating domain-specific datasets from noisy web sources to improve model performance.
Outcome:	The proposed model outperforms the baseline model on the astronomy benchmark and on the AstroBench.

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Ji Liu, Jiaxiang Ren, Ruoming Jin, Zijie Zhang, Yang Zhou, Patrick Valduriez, Dejing Dou

APPSI-139: A Parallel Corpus of English Application Privacy Policy Summarization and Interpretation (2026.acl-long)

Challenge:	Existing frameworks for learning Large Language Models (LLMs) require adaptive data processing and low-rank adjustment to improve accuracy and fine-tuning speed.
Approach:	They propose a fisher information-based adaptive federated curriculum learning framework with two novel methods to improve FL fine-tuning process.
Outcome:	The proposed framework improves performance and fine-tuning speed compared with baseline approaches.

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Pengyun Zhu, Qiheng Sun, Long Wen, Yanbo Wang, Yang Cao, Junxu Liu, Deyi Xiong, Jinfei Liu, Zhibo Wang, Kui Ren

Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue (2023.emnlp-main)

Challenge:	a lack of high-quality English privacy policy corpus optimized for legal clarity and readability is limiting translation of privacy policies . 139 privacy policies are often considered "incomprehensible" due to technical jargon, legal language, and convoluted grammatical structures.
Approach:	They propose a high-quality English privacy policy corpus annotated by domain experts . they propose APPSI-139 to summarize and interpret privacy policies in English .
Outcome:	The proposed framework outperforms large language models in terms of readability and accuracy.

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Yizhe Yang, Heyan Huang, Yuhang Liu, Yang Gao

ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection (2025.findings-acl)

Challenge:	Knowledge-grounded dialogue systems can generate informative responses based on dialogue history and external knowledge source.
Approach:	They conduct a thorough experiment to determine the optimal knowledge form, mutual effects between knowl- edge and model selection, and the few-shot performance of knowledge.
Outcome:	The proposed method combines knowledge-grounded dialogue with human-generated dialogues to generate informative and meaningful responses.

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Jiaqi Li, Xinyi Dong, Yang Liu, Zhizhuo Yang, Quansen Wang, Xiaobo Wang, Song-Chun Zhu, Zixia Jia, Zilong Zheng

Improving Radiology Summarization with Radiograph and Anatomy Prompts (2023.findings-acl)

Challenge:	ReflectEvo-460k is a large-scale, comprehensive, self-generated reflection dataset with broadened instructions and diverse multi-domain tasks.
Approach:	They propose a pipeline that iteratively generates self-reflection for self-training and a large-scale reflection dataset with broadened instructions and diverse multi-domain tasks.
Outcome:	The proposed pipeline improves Llama-3 reasoning ability by up to 71.2% and Mistral by upto 44.4%.

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Jinpeng Hu, Zhihong Chen, Yang Liu, Xiang Wan, Tsung-Hui Chang

Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction (2025.emnlp-main)

Challenge:	Recent studies focus on automatic impression generation, but this task is time-consuming and in high demand.
Approach:	They propose to use an anatomy-enhanced multimodal model to generate automatic impressions by combining radiology images with textual features.
Outcome:	The proposed model achieves state-of-the-art on two benchmark datasets and compares with existing models.

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Junkai Liu, Yujie Tong, Hui Huang, Bowen Zheng, Yiran Hu, Peicheng Wu, Chuan Xiao, Makoto Onizuka, Muyun Yang, Shuyuan Zheng

Challenge:	Existing studies use legal facts to predict judgments, but legal facts are difficult to obtain in early stages of litigation.
Approach:	They propose a legal fact prediction task that takes evidence from trial as input to make predictions in the absence of ground-truth legal facts.
Outcome:	The proposed task can predict court rulings without ground-truth legal facts . the first benchmark dataset, LFPBench, is used to evaluate the task .

CSLM: A Framework for Question Answering Dataset Generation through Collaborative Small Language Models (2024.findings-emnlp)

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Yiming Wang, Yang Liu, Lingchen Wang, An Xiao

Simple Hardware-Efficient PCFGs with Independent Left and Right Productions (2023.findings-emnlp)

Challenge:	Collecting high-quality question-answer (QA) pairs is vital for training large language models, but computational demands and associated costs often render such approaches prohibitive for the average researcher.
Approach:	They propose a small-scaled, open-source solution that generates QA pairs from documents or raw corpora using large-scale models like Llama-70B.
Outcome:	Experiments on domain-specific datasets show that the proposed model can generate high-quality QA pairs, making it accessible to a broader range of researchers.

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Wei Liu, Songlin Yang, Yoon Kim, Kewei Tu

Jakiro: Boosting Speculative Decoding via Decoupled MoE (2026.acl-long)

Challenge:	Existing approaches to scaling dense PCFGs to thousands of nonterminals have shown to be beneficial for unsupervised parsing, but they still perform poorly as a language model and as an unsupervised model.
Approach:	They propose a simple PCFG formalism with independent left and right productions that scales more effectively as a language model and as an unsupervised parser.
Outcome:	The proposed formalism scales better as a language model and as an unsupervised parser despite imposing a stronger independence assumption compared to low-rank approaches.

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Haiduo Huang, Fuwei Yang, Zhenhua Liu, Pengju Ren

Word-level Textual Adversarial Attacking as Combinatorial Optimization (2020.acl-main)

Challenge:	Existing methods to accelerate large language model inference have a fundamental limitation: candidates at the same tree layer share identical feature representations, constraining diversity and diminishing overall effectiveness.
Approach:	They propose a decoupled mixture of experts (MoE) into a draft model to generate diverse tokens from distinct feature spaces.
Outcome:	The proposed approach achieves significant speedups over strong baselines, with notable improvements in non-greedy scenarios where token diversity is crucial.

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Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, Maosong Sun

A Black-Box Attack on Code Models via Representation Nearest Neighbor Search (2023.findings-emnlp)

Challenge:	Existing word-level attack models are far from perfect because of unsuitable search space reduction methods and inefficient optimization algorithms.
Approach:	They propose a novel adversarial adversarialist model that incorporates word substitution and particle swarm optimization to solve two problems separately.
Outcome:	The proposed model achieves much higher success rates and crafts more high-quality adversarial examples as compared to baseline methods.

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Jie Zhang, Wei Ma, Qiang Hu, Shangqing Liu, Xiaofei Xie, Yves Le Traon, Yang Liu

A Unified Supervised and Unsupervised Dialogue Topic Segmentation Framework Based on Utterance Pair Modeling (2025.naacl-long)

Challenge:	Existing methods for generating adversarial code examples face challenges such as limted availability of substitute variables and the creation of adversarials with noticeable perturbations.
Approach:	They propose a search seed based on historical attacks to find adversarial substitutes . they employ a pre-trained variable name encoder to map the search seed to a continuous vector space .
Outcome:	The proposed approach outperforms baseline methods in terms of ASR and QT.

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Shihao Yang, Ziyi Zhang, Yue Jiang, Chunsheng Qin, Shuhua Liu

TestAug: A Framework for Augmenting Capability-based NLP Tests (2022.coling-1)

Challenge:	Unsupervised methods for dialogue topic segmentation are difficult to surpass due to short sentences, serious references and non-standard language.
Approach:	They propose a method to divide a dialogue into different topic paragraphs to better understand its structure and content.
Outcome:	The proposed method achieves the best results on multiple benchmark datasets across different scenarios.

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Guanqun Yang, Mirazul Haque, Qiaochu Song, Wei Yang, Xueqing Liu

Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (D18-1)

Challenge:	Existing work on capability-based testing requires the developer to compose each individual test template from scratch.
Approach:	They propose a capability-based NLP testing framework that requires the developer to only annotate a few test templates while leveraging the GPT-3 engine to generate the majority of test cases.
Outcome:	The proposed framework saves the developer's manual efforts and guarantees the correctness of the generated suites with a validity checker.

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Yubo Chen, Hang Yang, Kang Liu, Jun Zhao, Yantao Jia

Guide the Many-to-One Assignment: Open Information Extraction via IoU-aware Optimal Transport (2023.acl-long)

Challenge:	Existing approaches to ACE event detection treat multiple events in one sentence as independent ones and recognize them separately.
Approach:	They propose a hierarchical and bias tagging network framework to detect multiple events in one sentence collectively and a gated multi-level attention mechanism to automatically extract and fuse the sentence-level and document-level information.
Outcome:	The proposed framework outperforms state-of-the-art methods on a 2005 ACE dataset.

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Kaiwen Wei, Yiran Yang, Li Jin, Xian Sun, Zequn Zhang, Jingyuan Zhang, Xiao Li, Linhao Zhang, Jintao Liu, Guo Zhi

DocAgent: A Multi-Agent System for Automated Code Documentation Generation (2025.acl-demo)

Challenge:	Open Information Extraction (OIE) aims to extract structured information from text without the limitations of close ontology.
Approach:	They propose a method to assign ground truth labels to parallelly generated tuple proposals . they leverage intersection-over-union (IoU) as assignment quality measurement .
Outcome:	The proposed method outperforms the state-of-the-art models on three benchmarks.

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Dayu Yang, Antoine Simoulin, Xin Qian, Xiaoyi Liu, Yuwei Cao, Zhaopu Teng, Grey Yang

Evaluating Memory Capability in Continuous Lifelog Scenario (2026.findings-acl)

Challenge:	Existing methods for generating documentation using Large Language Models (LLMs) produce incomplete, unhelpful, or factually incorrect outputs.
Approach:	They propose a novel collaborative system that uses topological code processing for incremental context building to generate documentation by agents.
Outcome:	The proposed system outperforms baselines in completeness, helpfulness, and truthfulness evaluations.

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Jianjie Zheng, Zhichen Liu, Zhanyu Shen, Jingxiang Qu, Guanhua Chen, Yile Wang, Yang Xu, Yang Liu, Sijie Cheng

Challenge:	Existing benchmarks focus on online one-on-one chatting or human-AI interactions, neglecting real-world scenarios.
Approach:	They propose a framework to curate a lifelog benchmark that combines two subsets of audio data to address temporal leakage in offline settings.
Outcome:	The proposed framework outperforms existing benchmarks on live chats and AI interactions.

PLACES: Prompting Language Models for Social Conversation Synthesis (2023.findings-eacl)

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Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu, Dilek Hakkani-Tur

Persona-judge: Personalized Alignment of Large Language Models via Token-level Self-judgment (2025.findings-acl)

Challenge:	Currently, collecting high quality conversational data is expensive and infeasible for many applications . a promising direction is to generate synthetic dialogues by prompting large language models .
Approach:	They propose to use expert-written conversations as in-context examples to generate synthetic dialogues by prompting large language models.
Outcome:	The proposed approach is generalizable to multi-party conversations, compared to human-collected conversations.

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Xiaotian Zhang, Ruizhe Chen, Yang Feng, Zuozhu Liu

Token-Aware Editing of Internal Activations for Large Language Model Alignment (2025.emnlp-main)

Challenge:	Existing methods for aligning language models with human preferences rely on reward signals and additional annotated data, limiting their scalability and adaptability to diverse human values.
Approach:	They propose a discriminative paradigm that leverages the intrinsic preference judgment capabilities of the model to align language models with human preferences.
Outcome:	The proposed model is scalable and efficient, paving the way for more adaptive personalized alignment.

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Tianbo Wang, Yuqing Ma, Kewei Liao, Chengzhao Yang, Zhange Zhang, Jiakai Wang, Xianglong Liu

CRASpell: A Contextual Typo Robust Approach to Improve Chinese Spelling Correction (2022.findings-acl)

Challenge:	Existing methods to optimize the behavior of large language models neglect misalignment discrepancies among tokens, resulting in deviant alignment direction and inflexible editing strength.
Approach:	They propose a token-aware editing approach to exploit the misalignment discrepancy among tokens to enhance activation probing and facilitate intervention.
Outcome:	Extensive experiments on three alignment capabilities demonstrate the efficacy of the proposed approach surpassing baseline by 25.8% on the primary metric of truthfulness with minimal cost.

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Shulin Liu, Shengkang Song, Tianchi Yue, Tao Yang, Huihui Cai, TingHao Yu, Shengli Sun

Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)

Challenge:	Recent research on Chinese spelling correction methods has poor performance on multi-typo texts.
Approach:	They propose to use Bert-based Chinese spelling correction models to overcome these limitations by constructing a noisy context for each training sample and a copy mechanism to encourage the model to choose the input character when the miscorrected and input character are both valid.
Outcome:	The proposed model outperforms state-of-the-art models on widely used benchmarks and achieves a remarkable gain.

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Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun

On Safety Risks in Experience-Driven Self-Evolving Agents (2026.findings-acl)

Challenge:	Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores.
Approach:	They propose a benchmark for score-level musical understanding across textual and visual modalities.
Outcome:	The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others.

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Weixiang Zhao, Yichen Zhang, Yingshuo Wang, Yang Deng, Yanyan Zhao, Xuda Zhi, Yongbo Huang, Hao He, Wanxiang Che, Bing Qin, Ting Liu

Challenge:	Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduces underexplored safety risks.
Approach:	They investigate how experience accumulation and utilization in self-evolving agents affect safety performance across web-based and embodied environments.
Outcome:	The findings expose inherent limitations of current self-evolving agents and call for more principled strategies to ensure safe and reliable adaptation.

Automatically Select Emotion for Response via Personality-affected Emotion Transition (2021.findings-acl)

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Zhiyuan Wen, Jiannong Cao, Ruosong Yang, Shuaiqi Liu, Jiaxing Shen

The Role of Visual Modality in Multimodal Mathematical Reasoning: Challenges and Insights (2025.acl-long)

Challenge:	Existing studies focus on rendering specified emotions in responses, yet the individual difference in emotion expression is overlooked.
Approach:	They propose to equip a dialog system with personality and enable it to select emotions in responses like humans.
Outcome:	The proposed system can select emotions in responses like humans by simulating the emotion transition of humans in conversation.

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Yufang Liu, Yao Du, Tao Ji, Jianing Wang, Yang Liu, Yuanbin Wu, Aimin Zhou, Mengdi Zhang, Xunliang Cai

LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces (2026.findings-acl)

Challenge:	Existing models that leverage visual information do not improve math reasoning performance . authors suggest that visual information is important for multimodal reasoning .
Approach:	They propose a dataset to require image reliance for problem-solving and challenge models with similar, yet distinct, images that change the correct answer.
Outcome:	The proposed model performance is unaffected by changes to or removal of images in the dataset.

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Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Shenglin Zhang, Kaipeng Zhang

SARA: Salience-Aware Reinforced Adaptive Decoding for Large Language Models in Abstractive Summarization (2025.acl-long)

Challenge:	Existing benchmarks for agentic programming in long-horizon command-line interface tasks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics.
Approach:	They propose a benchmark to evaluate agentic capabilities across long-horizon command-line interface tasks.
Outcome:	The proposed benchmarks cover four engineering categories: from scratch, feature addition, bug fixing, and refactoring.

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Nayu Liu, Junnan Zhu, Yiming Ma, Zhicong Lu, Wenlei Xu, Yong Yang, Jiang Zhong, Kaiwen Wei

Weak-to-Strong Reasoning (2024.findings-emnlp)

Challenge:	Existing decoding strategies neglect the explicit use of salient contextual information and rely on static hyperparameters to fix the balance between contextual and prior knowledge.
Approach:	They propose a salience-aware reinforced adaptive decoding (SARA) which incorporates salient contextual information and allows the model to determine reliance on source document's context, salient context, and model's prior knowledge based on pointwise mutual information.
Outcome:	The proposed model improves the quality and faithfulness of summaries across LLMs without modifying their weights.

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Yuqing Yang, Yan Ma, Pengfei Liu

Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection (2026.findings-acl)

Challenge:	Existing approaches to supervise large language models (LLMs) exceed human capabilities, but the effectiveness of this approach is still unexplored.
Approach:	They propose a weak-to-strong reasoning framework that enables strong models to refine training data . they use supervised fine-tuning and preference optimization to optimize weak models .
Outcome:	The proposed framework improves the reasoning capabilities of a language model using three weak models.

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Zhuoyang Wu, Xinze Li, Zhenghao Liu, Yukun Yan, Zhiyuan Liu, Minghe Yu, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)

Challenge:	Existing studies treat SLMs as student models and use long-form Chains-of-Thought (CoTs) as supervision signals for Supervised Fine-Tuning (SFT). Existing research focuses on distilling reasoning ability from LLMs to enhance the mathematical reasoning performance of small-scale models.
Approach:	They propose a framework that refines teacher CoTs through an error-aware reflection process to enable the student model to construct more tailored teacher Cots.
Outcome:	Experiments on multiple mathematical reasoning benchmarks show that ORION improves performance by more than 2% over all baselines.

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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li

Challenge:	Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences.
Approach:	They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks.
Outcome:	The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge.

MPO: Multilingual Safety Alignment via Reward Gap Optimization (2025.acl-long)

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Weixiang Zhao, Yulin Hu, Yang Deng, Tongtong Wu, Wenxuan Zhang, Jiahe Guo, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

Knowledge Context Modeling with Pre-trained Language Models for Contrastive Knowledge Graph Completion (2024.findings-acl)

Challenge:	Existing preference learning methods for safety alignment are monolingual and struggle with noisy multilingual data.
Approach:	They propose a multilingual reward gaP optimization approach that leverages the well-aligned safety capabilities of the dominant language to improve safety alignment across multiple languages.
Outcome:	Extensive experiments on three LLMs, LLaMA-3.1, Gemma-2 and Qwen2.5, validate MPO’s efficacy in multilingual safety alignment without degrading general multilingual utility.

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Guangqian Yang, Yi Liu, Lei Zhang, Licheng Zhang, Hongtao Xie, Zhendong Mao

Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback (2026.findings-acl)

Challenge:	Text-based knowledge graph completion methods neglect knowledge contexts in inferring process.
Approach:	They propose a framework which models the knowledge context as additional prompts with pre-trained language models for knowledge graph completion.
Outcome:	The proposed framework achieves state-of-the-art on FB15k-237, WN18RR and Wikidata5M datasets.

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Yiyuan Yang, Zichuan Liu, Lei Song, Kai Ying, Stephen Wang, Joshua Thomas Bamford, Svitlana Vyetrenko, Jiang Bian, Qingsong Wen

Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities (2025.naacl-long)

Challenge:	Time series anomaly detection (TSAD) has traditionally focused on binary classification and lacks the fine-grained categorization and explanatory reasoning required for transparent decision-making.
Approach:	They propose a time-series reasoning task that reformulates TSAD from discriminative to reasoning-intensive paradigm.
Outcome:	The proposed task reformulates TSAD from discriminative to reasoning-intensive paradigm.

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Chung-En Sun, Xiaodong Liu, Weiwei Yang, Tsui-Wei Weng, Hao Cheng, Aidan San, Michel Galley, Jianfeng Gao

Enhancing the Transferability of Jailbreak Attacks on Large Language Models via Exploiting Reparameterization Invariance (2026.acl-long)

Challenge:	Recent research shows that Large Language Models (LLMs) are vulnerable to automated jailbreak attacks.
Approach:	They propose a framework that crafts adversarial LLMs with enhanced jailbreak ability.
Outcome:	ADV-LLM significantly reduces the computational cost of generating adversarial suffixes while achieving nearly 100% ASR on various open-source LLMs.

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Ao Wang, Xinghao Yang, Yongshun Gong, Wei Liu, Bao-di Liu, Weifeng Liu

AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models (2023.acl-short)

Challenge:	Existing token-level attacks have shown efficacy on open-source models but suffer from poor cross-model transferability.
Approach:	They propose a framework to improve cross-model transferability by modifying model parameters and generating update directions according to differences in output distributions rather than parameter-space distances.
Outcome:	The proposed framework improves cross-model transferability and success rates on open-source models.

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Siheng Li, Cheng Yang, Yichun Yin, Xinyu Zhu, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang

Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)

Challenge:	Existing research on information-seeking conversations is stymied by the lack of training data.
Approach:	They propose to use autoconv for synthetic conversation generation to capture the characteristics of the information-seeking process and fine tune an LLM with a few human conversations to generate synthetic conversations with high quality.
Outcome:	The proposed model improves on two commonly-used datasets and alleviates the dependence on human annotation.

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Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji

Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning (2026.acl-long)

Challenge:	Despite of significant achievements in improving instruction-following capabilities of large language models, the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge.
Approach:	They construct multi-turn instruction with 1.1K high-quality multi-turned conversations using the human-in-the-loop approach and examine their capabilities.
Outcome:	The proposed model shows that it is difficult to integrate multiple turns and balance competing objectives when instructions intersect or conflict.

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Yifan Li, YuKai Gu, Yingqian Min, Zikang Liu, Yifan Du, Kun Zhou, Min Yang, Xin Zhao, Minghui Qiu

GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective (2023.findings-acl)

Challenge:	Existing evaluation frameworks often rely on single-frame assessments, which can lead to outcome-hacking.
Approach:	They propose a process-aware evaluation paradigm that uses a hierarchical rubric to evaluate the validity of the intermediate steps and the final result.
Outcome:	The proposed model achieves POC@1.0 only about 20% and exhibits significant outcome-hacking.

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Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang

Multi-Level Cross-Modal Alignment for Speech Relation Extraction (2024.emnlp-main)

Challenge:	Pre-trained language models (PLMs) have improved generalization performance but the out-of-distribution (OOD) generalization problem remains a challenge in many NLP tasks.
Approach:	They propose to create a benchmark for evaluating out-of-distribution (OOD) generalization in NLP models.
Outcome:	The proposed benchmarks highlight the importance of OOD robustness and provide insights on how to measure it and improve it.

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Liang Zhang, Zhen Yang, Biao Fu, Ziyao Lu, Liangying Shao, Shiyu Liu, Fandong Meng, Jie Zhou, Xiaoli Wang, Jinsong Su

Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning (2025.emnlp-main)

Challenge:	Existing studies use synthetic speech to train and evaluate SpeechRE models, hindering their development . modality gap issue limits performance of existing models, limiting future researches .
Approach:	They propose to use speech data to train and evaluate SpeechRE models by using real speech . they propose to train a cross-modal alignment model to bridge the modality gap .
Outcome:	The proposed model can train to bridge the modality gap between speech encoder and text decoder . the proposed model is based on two real SpeechRE datasets .

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Yunhao Gou, Hansi Yang, Zhili Liu, Kai Chen, Yihan Zeng, Lanqing Hong, Zhenguo Li, Qun Liu, Bo Han, James Kwok, Yu Zhang

CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG (2025.acl-long)

Challenge:	Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), but its effectiveness is often compromised by corrupted datasets with issues such as hallucinated content and poor OCR quality.
Approach:	They propose a corruption-robust training paradigm that surpasses existing strategies for mitigating the effects of corrupted data.
Outcome:	The proposed training paradigm surpasses existing strategies for mitigating the effects of corrupted data.

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Yang Tian, Fan Liu, Jingyuan Zhang, V. W., Yupeng Hu, Liqiang Nie

C-ICL: Contrastive In-context Learning for Information Extraction (2024.findings-emnlp)

Challenge:	Recent advances in Multimodal Large Language Models have significantly improved reasoning and generation tasks by leveraging joint vision-language representations.
Approach:	They propose a framework that reconciles inconsistencies across knowledge sources . they use a four-stage pipeline to generate an internal response from parametric knowledge .
Outcome:	Experiments on KB-VQA show that CoRe-MMRAG achieves performance gains of 5.6% and 9.3% over baseline methods.

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Ying Mo, Jiahao Liu, Jian Yang, Qifan Wang, Shun Zhang, Jingang Wang, Zhoujun Li

LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Data (2025.findings-acl)

Challenge:	Existing methods for in-context learning with large language models focus on using correct or negative examples, ignoring the potential value of incorrect or negative samples.
Approach:	They propose a few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations.
Outcome:	The proposed technique outperforms previous few-shot in-context learning methods on a broad spectrum of related tasks.

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Cehao Yang, Xueyuan Lin, Chengjin Xu, Xuhui Jiang, Shengjie Ma, Aofan Liu, Hui Xiong, Jian Guo

Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication (2024.findings-emnlp)

Challenge:	Long-context processing ability has emerged as a significant challenge for large language models.
Approach:	They propose a pipeline for synthesizing faithful long-context reasoning instruction datasets . they integrate ground truth and citation-based reasoning prompts integrating them .
Outcome:	The proposed pipeline eliminates distractions and improves reasoning chains.

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Weize Chen, Chenfei Yuan, Jiarui Yuan, Yusheng Su, Chen Qian, Cheng Yang, Ruobing Xie, Zhiyuan Liu, Maosong Sun

Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)

Challenge:	Natural language (NL) has long been the predominant format for human cognition and communication, but its utility in LLMs has not been thoroughly examined.
Approach:	They propose to allow LLMs to choose the most suitable format before reasoning or communicating, and to automate the selection process.
Outcome:	The proposed format improves reasoning efficiency and reduces token usage while maintaining communicative effectiveness.

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Minghui Qiu, Liu Yang, Feng Ji, Wei Zhou, Jun Huang, Haiqing Chen, Bruce Croft, Wei Lin

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)

Challenge:	Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems.
Approach:	They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance.
Outcome:	The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist.

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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang

HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents (2026.acl-long)

Challenge:	Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning.
Approach:	AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks.
Outcome:	Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% .

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Jinchang Zhu, Jindong Li, Cheng Zhang, Jiahong Liu, Menglin Yang

What Prompts Don’t Say: Understanding and Managing Underspecification in LLM Prompts (2026.findings-acl)

Challenge:	Existing memory systems represent conversation history as unstructured embedding vectors, retrieving information through semantic similarity.
Approach:	They propose a bio-inspired memory architecture that models memory as a dynamic graph with Hebbian learning dynamics.
Outcome:	The proposed architecture leverages both semantic similarity and learned associations . it can be used to build a bio-inspired memory graph with Hebbian learning dynamics .

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Chenyang Yang, Yike Shi, Qianou Ma, Michael Xieyang Liu, Christian Kaestner, Tongshuang Wu

Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions (2025.emnlp-main)

Challenge:	Under-specified prompts are 2x as likely to regress across model or prompt changes, authors show . eliot safina: a lack of explicit prompts can cause frustrations and failures .
Approach:	They propose requirements-aware prompt optimization mechanisms that improve performance by 4.8% over baselines.
Outcome:	The proposed mechanisms improve prompt performance by 4.8% over baselines.

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Yu-Ang Lee, Guan-Ting Yi, Mei-Yi Liu, Jui-Chao Lu, Guan-Bo Yang, Yun-Nung Chen

Making LLMs Better Many-to-Many Speech-to-Text Translators with Curriculum Learning (2025.acl-long)

Challenge:	Recent advances in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex workflows.
Approach:	They propose a systematic review of recent progress in optimizing compound AI systems . they formalize the notion of compound AI system optimization and classify existing methods along several key dimensions .
Outcome:	The proposed methods outperform existing methods in the field of compound AI and highlight open research challenges and future directions.

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Yexing Du, Youcheng Pan, Ziyang Ma, Bo Yang, Yifan Yang, Keqi Deng, Xie Chen, Yang Xiang, Ming Liu, Bing Qin

Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media (2025.acl-long)

Challenge:	Existing studies on English-centric translation tasks have focused on multimodal large language models, but the exploration of many-to-many translation is limited by the scarcity of parallel data.
Approach:	They propose a three-stage curriculum learning strategy that leverages the machine translation capabilities of large language models and adapts them to S2TT tasks.
Outcome:	The proposed strategy achieves state-of-the-art average performance in 1514 language pairs, requiring fewer than 10 hours of speech data per language to achieve competitive results.

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Zhen Sun, Zongmin Zhang, Xinyue Shen, Ziyi Zhang, Yule Liu, Michael Backes, Yang Zhang, Xinlei He

RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning (2025.emnlp-industry)

Challenge:	Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs) however, the misuse of AIGTs could have profound implications for public opinion .
Approach:	They collect a dataset with 2.4M posts from 3 major social media platforms . they then construct a diverse dataset to train and evaluate AIGT detectors .
Outcome:	The proposed dataset analyzes 2.4M posts from 3 major social media platforms from 2022 to 2024 . it finds that Medium and Quora show marked increases in AAR .

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Deyi Ji, Yuekui Yang, Liqun Liu, Peng Shu, Haiyang Wu, Shaogang Tang, Xudong Chen, Shaoping Ma, Tianrun Chen, Lanyun Zhu

RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)

Challenge:	Recent advances in large language models have improved the detection of non-compliant content, but critical gaps persist in fine-grained understanding, explainability, and generalization.
Approach:	They propose a framework that combines active reinforcement learning, fine-grained violation understanding and progressive multi-stage training.
Outcome:	The proposed framework outperforms general-purpose LLMs and specialized models in fine-grained violation understanding, explainability, and generalization.

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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang

SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training (2025.findings-acl)

Challenge:	Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting.
Approach:	They propose a representation-aware model merging framework for continual learning without access to historical data.
Outcome:	The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios.

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Wenxi Chen, Ziyang Ma, Ruiqi Yan, Yuzhe Liang, Xiquan Li, Ruiyang Xu, Zhikang Niu, Yanqiao Zhu, Yifan Yang, Zhanxun Liu, Kai Yu, Yuxuan Hu, Jinyu Li, Yan Lu, Shujie Liu, Xie Chen

Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models (2024.lrec-main)

Challenge:	a new spoken dialogue system with single-stage training is demonstrating its low latency and high quality . SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens .
Approach:	They propose a timbre-controllable, end-to-end voice interaction system with single-stage training.
Outcome:	The proposed system outperforms previous models on 4 GPUs with limited data.

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Pusheng Liu, Lianwei Wu, Linyong Wang, Sensen Guo, Yang Liu

Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning (2026.findings-acl)

Challenge:	Existing methods for autoregressive text generation have low controllability and accumulating errors.
Approach:	They propose a three-stage prompt-based approach to express autoregressive text in a specific region editing task using a word frequency-based strategy.
Outcome:	Experiments on publicly competitive datasets confirm that the proposed approach achieves state-of-the-art performance.

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Xudong Li, Yuhang Tian, Dandan Song, Zhijing Wu, Shuhao Zhang, Jun Yang, Yongyu Huo, Changzhi Zhou, Xinyu Zhang, Chenhao Li, Huipeng Ma, Luan Zhang, Yan Xu, Qian Liu

Citation-Enhanced Generation for LLM-based Chatbots (2024.acl-long)

Challenge:	Existing knowledge editing methods rely on unidirectional, feed-forward pipelines . a minor retrieval error or logical mismatch at an early hop can become a silent failure .
Approach:	They propose a framework for closed-loop post-edit reasoning that uses a Critic agent to verify coherence and step-wise correctness.
Outcome:	Experiments on MQuAKE-2002 and MQuADE-hard show that CARE effectively mitigates error propagation . a minor retrieval error or logical mismatch at an early hop can become a silent failure .

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Weitao Li, Junkai Li, Weizhi Ma, Yang Liu

A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction (2024.lrec-main)

Challenge:	Existing efforts to alleviate hallucination in chatbots require additional training and data annotation.
Approach:	They propose a Citation-Enhanced Generation approach that uses retrieval argumentation to generate citations and a natural language inference-based citation generation module to generate content.
Outcome:	The proposed method outperforms state-of-the-art methods on three benchmarks.

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Jian Zhang, Changlin Yang, Haiping Zhu, Qika Lin, Fangzhi Xu, Jun Liu

Improving Pacing in Long-Form Story Planning (2023.findings-emnlp)

Challenge:	Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from unstructured document.
Approach:	They propose a document-prompt-based method for document-level event argument extraction that uses a semantic mention graph to capture relations between documents and prompts.
Outcome:	The proposed method surpasses baseline methods and achieves state-of-the-art performance on RAMS and WikiEvents datasets.

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Yichen Wang, Kevin Yang, Xiaoming Liu, Dan Klein

Challenge:	Existing systems for writing long-form stories suffer from unnatural pacing, whether glossing over important events or over-elaborating on insignificant details.
Approach:	They propose a system that uses a concreteness evaluator to judge which of two events is more concrete.
Outcome:	The proposed system improves pacing when automatically generating story outlines.

LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning (2025.acl-long)

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Jin Jiang, Yuchen Yan, Yang Liu, Jianing Wang, Shuai Peng, Xunliang Cai, Yixin Cao, Mengdi Zhang, Liangcai Gao

Mulan: A Multi-Level Alignment Model for Video Question Answering (2023.findings-emnlp)

Challenge:	LogicPro is a data synthesis method that uses LeetCode-style algorithm problems and their corresponding Program solutions to generate complex logic data.
Approach:	They propose a new method which leverages LeetCode-style algorithm Problems and their corresponding Program solutions to synthesize complex logic data in text format.
Outcome:	The proposed method outperforms existing models for BBH27, LogicBench, DROP, AR-LSAT, and GSM8K, and a wide range of reasoning datasets.

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Yu Fu, Cong Cao, Yuling Yang, Yuhai Lu, Fangfang Yuan, Dakui Wang, Yanbing Liu

DEEM: Dynamic Experienced Expert Modeling for Stance Detection (2024.lrec-main)

Challenge:	Existing methods focus on visual-language alignment at the video level, but they do not account for fine-grained semantic interaction between video and text.
Approach:	They propose a multi-level Alignment Model for Video Question Answering that establishes alignment between visual and textual modalities at the object-level, frame-level and video-level.
Outcome:	The proposed model outperforms state-of-the-art methods even with a small amount of extra visual-language pre-training data and a reduced number of trainable parameters.

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Xiaolong Wang, Yile Wang, Sijie Cheng, Peng Li, Yang Liu

Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)

Challenge:	Existing work on stance detection tasks using large language models shows promising results, but it may not be able to provide detailed background knowledge.
Approach:	They propose a method which leverages the generated experienced experts and lets LLMs reason in a semi-parametric way.
Outcome:	The proposed method outperforms methods with self-consistency reasoning and reduces bias.

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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv

Intriguing Effect of the Correlation Prior on ICD-9 Code Assignment (2023.acl-srw)

Challenge:	Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build.
Approach:	They propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence.
Outcome:	The proposed method generates diverse pseudo-paraphrases in distinct surface structures for a given sentence.

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Zihao Yang, Chenkang Zhang, Muru Wu, Xujin Liu, Lavender Jiang, Kyunghyun Cho, Eric Oermann

Joint Knowledge Base Completion and Question Answering by Combining Large Language Models and Small Language Models (2026.acl-long)

Challenge:	The Ninth Revision of the International Classification of Diseases (ICD-9) is a standardized coding system used worldwide to classify and code diseases, injuries, and other health conditions.
Approach:	They evaluate the usefulness of correlation bias and suggest it could improve ICD-9 code assignment in some cases.
Outcome:	The proposed model improves on classes that are more imbalanced and less correlated with other codes, but the effect on individual class can be negative or positive.

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Yinan Liu, Dongying Lin, Sigang Luo, Xiaochun Yang, Bin Wang

DESED: Dialogue-based Explanation for Sentence-level Event Detection (2022.coling-1)

Challenge:	Existing studies rely on the small language model (SLM) to enhance them jointly, and the large language model’s strong reasoning ability is ignored.
Approach:	They propose a framework which can make knowledge base completion and knowledge base question answering enhance each other in an iterative manner by combining the strengths of the small language model and the large language model.
Outcome:	The proposed framework surpasses baselines for both KBC and KBQA tasks over two public benchmark data sets.

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Yinyi Wei, Shuaipeng Liu, Jianwei Lv, Xiangyu Xi, Hailei Yan, Wei Ye, Tong Mo, Fan Yang, Guanglu Wan

DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data (P18-4)

Challenge:	Existing methods for sentence-level event detection depend on manual annotations or domain expertise to design sophisticated templates and rules.
Approach:	They propose a dialogue-based explanation paradigm to enhance sentence semantics for event detection.
Outcome:	The proposed method can be applied to two event detection datasets.

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Hang Yang, Yubo Chen, Kang Liu, Yang Xiao, Jun Zhao

ProBench: Judging Multimodal Foundation Models on Open-ended Multi-domain Expert Tasks (2025.findings-acl)

Challenge:	Existing methods to extract events from documents are limited due to the high cost of labeling . Experimental results demonstrate the effectiveness of a document-level Chinese financial event extraction system.
Approach:	They propose a document-level Chinese financial event extraction framework which detects event mentions and extracts events from financial news.
Outcome:	The proposed system detects event mentions and extracts events from financial news . it can generate large scale labeled data and extract events from entire document .

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Yan Yang, Dongxu Li, Haoning Wu, Bei Chen, Liu Liu, Liyuan Pan, Junnan Li

Representation Learning with Ordered Relation Paths for Knowledge Graph Completion (D19-1)

Challenge:	Solving expert-level multimodal tasks requires strong user query understanding, domain-specific knowledge, and advanced reasoning abilities.
Approach:	They propose a benchmark of open-ended user queries encapsulating professional expertise and advanced reasoning.
Outcome:	The proposed benchmark is publicly accessible at TBC.

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Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yang Song, Tao Zhang

Dangling-Aware Entity Alignment with Mixed High-Order Proximities (2022.findings-naacl)

Challenge:	Existing knowledge graphs are incomplete and lack the order of relations in paths.
Approach:	They propose a method which takes relation paths into account but ignores order of relations in paths which is important for reasoning.
Outcome:	The proposed method performs better than state-of-the-art methods on two benchmark datasets.

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Juncheng Liu, Zequn Sun, Bryan Hooi, Yiwei Wang, Dayiheng Liu, Baosong Yang, Xiaokui Xiao, Muhao Chen

AttnPO: Attention-Guided Process Supervision for Efficient Reasoning (2026.acl-long)

Challenge:	Existing methods for dangling-aware entity alignment are underexplored but important problem.
Approach:	They propose a framework that uses high-order proximities to detect dangling entities and align matchable entities.
Outcome:	The proposed framework detects dangling entities and aligns matchable entities better than existing methods.

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Shuaiyi Nie, null Dingsiyu, Wenyuan Zhang, Linhao Yu, Tianmeng Yang, Yao Chen, Weichong Yin, Yu Sun, Hua Wu, Tingwen Liu

ThinkAnswer Loss: Balancing Semantic Similarity and Exact Matching for LLM Reasoning Enhancement (2025.findings-emnlp)

Challenge:	Existing trajectory-level length penalties fail to effectively shorten reasoning length and degrade accuracy, as they treat all reasoning steps uniformly and lack fine-grained signals to distinguish redundancy from necessity.
Approach:	They propose a low-overhead process-supervised RL framework that leverages the model’s intrinsic attention signals for step-level credit assignment.
Outcome:	The proposed framework reduces reasoning length while improving performance across 9 benchmarks.

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Shan Yang, Kun Wu, Zeju Li, Linlin Zhang, Xiangyu Pei, Leike An, Yu Liu

FlashAudio: Rectified Flow for Fast and High-Fidelity Text-to-Audio Generation (2025.acl-long)

Challenge:	Existing methods for knowledge distillation use Chain-of-Thought (CoT) and answer pairs, but they lack appropriate supervision signals.
Approach:	They propose a framework that decouples CoT and answer supervision . the framework applies semantic similarity constraints while maintaining strict literal matching for the answer .
Outcome:	The proposed framework decouples CoT and answer supervision while maintaining strict literal matching for the answer.

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Huadai Liu, Jialei Wang, Rongjie Huang, Yang Liu, Heng Lu, Zhou Zhao, Wei Xue

Eye Movement Features Can Predict Human Preferences on Machine-Generated Texts (2026.acl-srw)

Challenge:	Recent advances in latent diffusion models (LDMs) have markedly enhanced text-to-audio generation, yet their iterative sampling processes impose substantial computational demands, limiting practical deployment.
Approach:	They propose to learn straight flow for fast simulation by using flashAudio with rectified flows and immiscible flow to minimize the total distance of data-noise pairs in a batch vias assignment.
Outcome:	The proposed method can learn straight flow for fast simulations and reduce noise distribution.

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Xiaoshan He, Xiaoqun Liu, Haodong He, Yu Wang, Yang Xu

E2EDev: Benchmarking Large Language Models in End-to-End Software Development Task (2026.acl-long)

Challenge:	Existing studies on eye movement in text quality assessment are limited . eye-movement features are important predictors of human judgments of text quality, but are costly and inconsistent.
Approach:	They propose to capture eye-movement features during screen reading of LLM-generated text using a dataset that includes eye-motion recordings, reading-time measurements, and post-reading evaluations.
Outcome:	The proposed dataset shows that eye-movement features can significantly improve models over other probabilistic metrics, including negative log-likelihood (NLL).

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Jingyao Liu, Chen Huang, Zhizhao Guan, Wenqiang Lei, Yang Deng

Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis (2022.naacl-main)

Challenge:	Existing E2ESD benchmarks are limited by coarse-grained requirement specifications and unreliable evaluation protocols.
Approach:	They propose a benchmark to assess whether generated software meets user needs . they use a fine-grained set of user requirements and a fully automated testing pipeline .
Outcome:	E2EDev is a benchmark to assess whether generated software meets user needs through mimicking real user interactions.

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Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi

Retrieval Enhanced Model for Commonsense Generation (2021.findings-acl)

Challenge:	Existing studies rely on entity information for sentence-level relation extraction (RE) but this can leak superficial and spurious clues of relations.
Approach:	They propose to use entity mentions to extract relations from textual context . they use a causal graph to model dependencies between variables in RE models .
Outcome:	The proposed method yields significant gains on both effectiveness and generalization for RE.

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Han Wang, Yang Liu, Chenguang Zhu, Linjun Shou, Ming Gong, Yichong Xu, Michael Zeng

GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval (2022.findings-emnlp)

Challenge:	Existing frameworks for commonsense generation are lacking for pre-trained models.
Approach:	They propose a framework that uses concept matching to retrieve prototype sentences and trainable sentence retriever to enhance pre-training and fine-tuning.
Outcome:	The proposed framework achieves state-of-the-art on the large-scale Common-Gen benchmark.

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Jiduan Liu, Jiahao Liu, Yang Yang, Jingang Wang, Wei Wu, Dongyan Zhao, Rui Yan

RRAtention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference (2026.acl-long)

Challenge:	Existing approaches to perform large-scale query-passage retrieval are term-based, but they lose interaction between query-pastage pairs.
Approach:	They propose to fuse query (passage) information into query representations via graph neural networks that are constructed by queries and their top retrieved passages.
Outcome:	The proposed model outperforms existing models on MSMARCO, Natural Questions and TriviaQA datasets and achieves the new state-of-the-art on these datasets.

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Siran Liu, Guoxia Wang, Sa Wang, Jinle Zeng, Haoyang Xie, Siyu Lou, Jiabin Yang, Dianhai Yu, Haifeng Wang, Chao Yang

Self-contradictory reasoning evaluation and detection (2024.findings-emnlp)

Challenge:	Existing approaches to dynamic sparse attention require preprocessing, lack global evaluation, violate query independence, or incur high computational overhead.
Approach:	They propose a dynamic sparse attention method that achieves all desirable properties through a head round-robin (RR) sampling strategy.
Outcome:	Experiments on natural language understanding and multimodal video comprehension show that the proposed method achieves 2.4 speedup at 128K context length outperforming existing methods.

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Ziyi Liu, Soumya Sanyal, Isabelle Lee, Yongkang Du, Rahul Gupta, Yang Liu, Jieyu Zhao

Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models (2026.acl-long)

Challenge:	Large language models (LLMs) have shown impressive reasoning ability, but many downstream reasoning tasks focus on performance-wise evaluation.
Approach:	They define and assess the Self-Contra rate across three datasets and delve into finer-grained categories of Self-contra reasoning.
Outcome:	The proposed model can detect self-contra reasoning with a 52.2% F1 score, much lower than for humans.

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Zhiqing Cui, Binwu Wang, Qingxiang Liu, Yeqiang Wang, Zhengyang Zhou, Yuxuan Liang, Yang Wang

CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

Challenge:	Large language models (LLMs) have emerged as a promising avenue for time series forecasting . existing approaches face limitations such as marginalized role in model architectures and lack of interpretability.
Approach:	They propose a framework that exploits LLM causal reasoning to discover and use directed causal associations among covariates.
Outcome:	The proposed model improves predictive accuracy while yielding transparent, traceable reasoning about variable interactions.

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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan

Challenge:	Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach:	They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome:	The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .

Scaling Parameter-Constrained Language Models with Quality Data (2024.emnlp-industry)

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Ernie Chang, Matteo Paltenghi, Yang Li, Pin-Jie Lin, Changsheng Zhao, Patrick Huber, Zechun Liu, Rastislav Rabatin, Yangyang Shi, Vikas Chandra

KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)

Challenge:	Scaling laws in language modeling quantify training loss as a function of dataset size and model parameters, but neglect the critical role of data quality in model generalization.
Approach:	They propose to use effective training tokens as a combination of text diversity and syntheticity as measured by a teacher model to calculate scaling laws.
Outcome:	The proposed term effective training tokens is a combination of two readily-computed indicators of text diversity and syntheticity as measured by a teacher model.

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Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Inference-Time Language Model Alignment via Integrated Value Guidance (2024.findings-emnlp)

Challenge:	None. None.. None!
Approach:	None. None.. None!
Outcome:	None. None. No. :

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Zhixuan Liu, Zhanhui Zhou, Yuanfu Wang, Chao Yang, Yu Qiao

MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization (2021.naacl-main)

Challenge:	Large language models are fine-tuned to align with human preferences, but tuning large models is computationally intensive and complex.
Approach:	They propose a method that uses implicit and explicit value functions to guide language model decoding at token and chunk-level respectively.
Outcome:	The proposed method outperforms traditional methods and circumvents the complexities of fine-tuning.

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Chenguang Zhu, Yang Liu, Jie Mei, Michael Zeng

GAP: a Global Adaptive Pruning Method for Large Language Models (2025.emnlp-main)

Challenge:	Existing datasets for dialogue summarization are limited to their small sizes and are built from a narrow domain.
Approach:	They propose a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries.
Outcome:	The proposed dataset is larger and contains multi-party conversations from multiple domains.

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Zhihua Ban, Haotian Ma, Siheng Zhang, Shengyu Liu, Xichen Chen, Ming Yang

Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)

Challenge:	Existing structured pruning methods employ uniform compression rates across network layers, neglecting the varying importance of different network depths.
Approach:	They propose a pruning framework that minimizes global capability loss by layer-adaptive pruning rates.
Outcome:	The proposed approach achieves comparable performance with state-of-the-art methods at high pruning rates and shows significant advantages at low pruning rates.

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Jiali Zeng, Jinsong Su, Huating Wen, Yang Liu, Jun Xie, Yongjing Yin, Jianqiang Zhao

Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning (2026.eacl-industry)

Challenge:	Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model.
Approach:	They propose to use mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains.
Outcome:	The proposed model distinguishes and exploits word-level domain contexts on Chinese-English and English-French translation tasks.

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Minseok Kim, Jingxiang Chen, Seong-Gyun Leem, Yin Huang, Rashi Rungta, Zhicheng Ouyang, Haibin Wu, Surya Teja Appini, Ankur Bansal, Yang Bai, Yue Liu, Florian Metze, Ahmed A Aly, Anuj Kumar, Ariya Rastrow, Zhaojiang Lin

MoLAN: A Unified Modality-Aware Noise Dynamic Editing Framework for Multimodal Sentiment Analysis (2026.findings-acl)

Challenge:	Using paralinguistic cues is challenging for speech large language models, authors say . limited training data, annotation difficulty, and models exploiting lexical shortcuts are challenges . a recent study shows that modeling paralinguistic reasoning with multitask RL improves paralinguistics understanding .
Approach:	They propose multi-task reinforcement learning with chain-of-thought prompting that elicits explicit affective reasoning.
Outcome:	The proposed model improves paralinguistics understanding over baselines and strong proprietary models by 8-12% on Expresso, IEMOCAP, and RAVDESS.

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Xingle Xu, YongKang Liu, Dexian Cai, Shi Feng, Xiaocui Yang, Daling Wang, Yifei Zhang

SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation (2026.findings-acl)

Challenge:	Existing approaches to multimodal sentiment analysis treat entire modality as an independent unit for feature enhancement or denoising, which often suppresses redundant noise at the cost of weakening critical information.
Approach:	They propose a ModaLity-aware noise dynAmic editiNg framework that performs modality-awful block partitioning by dividing features of each modality into multiple blocks.
Outcome:	Experiments on five models and four datasets show that MoLAN+ achieves the state-of-the-art performance.

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Chengyi Yang, Pengzhen Li, Jiayin Qi, Aimin Zhou, Ji Wu, Ji Liu

Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection (2026.findings-acl)

Challenge:	Text-to-Video (T2V) generation is a challenge under complex scenarios.
Approach:	They propose a scenario-aware and self-correcting multi-agent prompt refinement framework for T2V prompting.
Outcome:	The proposed framework improves text-to-video alignment and overall generation quality under complex scenarios.

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Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Yan Song, Dong Yu, Wang Min

Tree-KG: An Expandable Knowledge Graph Construction Framework for Knowledge-intensive Domains (2025.acl-long)

Challenge:	Existing methods focus on designing efficient multimodal fusion frameworks to bridge the semantic gap between images and texts.
Approach:	They propose a covariance matrix-driven image channel allocation method that expands the number of original channel maps and assigns importance scores to the expanded channel maps.
Outcome:	The proposed method achieves state-of-the-art on three public multimodal fake news detection benchmark datasets.

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Songjie Niu, Kaisen Yang, Rui Zhao, Yichao Liu, Zonglin Li, Hongning Wang, Wenguang Chen

Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs (2026.acl-long)

Challenge:	Knowledge graphs are a useful tool for organizing complex data in knowledge-intensive domains.
Approach:	They propose an expandable framework that combines structured domain texts with advanced semantic techniques to create a tree-like graph from textbooks.
Outcome:	The proposed framework surpasses competing methods in the text-Annotated dataset with high scores on the Text-Annalytated data.

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Binxing Xu, Hao Gu, Lujun Li, Hao Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Xintong Yang, Chao Li, Sirui Han, Yike Guo

Hebbian-Guided Bi-Directional Rank Adaptation for Parameter-Efficient Fine-Tuning (2026.findings-acl)

Challenge:	Existing approaches to training LLMs at ultra-low precisions suffer from convergence instability and substantial training costs.
Approach:	They propose a progressive QAT framework with outlier channel splitting to address these issues . they use nested structure of integer quantization grids to enable a "train once, deploy any precision" paradigm .
Outcome:	The proposed framework outperforms baselines on both Llama2/3 and W2A16, with an 11 speedup over BF16.

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Xing Zhao, Liu Yang, Xi-Le Zhao, Yueqi Yang, Tianqi Jiang

Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)

Challenge:	Low-Rank Adaptation (LoRA) is a widely used method to fine-tune large language models . but its fixed-rank design cannot capture the varying importance across different layers .
Approach:	They propose a framework that bi-directionally reallocates low-rank capacity using Hebbian-inspired importance estimation.
Outcome:	Experiments show that HeBiRA improves performance over baselines.

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Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang

MisinfoBench: A Multi-Dimensional Benchmark for Evaluating LLMs’ Resilience to Misinformation (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent.
Approach:	They propose a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function calls.
Outcome:	The proposed framework reduces tool-use errors and improves interpretability and transparency of tool-using agents.

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Ye Yang, Donghe Li, Zuchen Li, Fengyuan Li, Jingyi Liu, Li Sun, Qingyu Yang

A Bounding Box is Worth One Token - Interleaving Layout and Text in a Large Language Model for Document Understanding (2025.findings-acl)

Challenge:	Existing benchmarks assess factual accuracy in isolated queries but fail to evaluate LLMs’ resilience to misinformation in interactive settings.
Approach:	MisinfoBench is a benchmark designed to assess LLMs’ ability to discern, resist, and reject misinformation.
Outcome:	MisinfoBench assesses large language models’ ability to discern, resist, and reject misinformation in interactive settings.

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Jinghui Lu, Haiyang Yu, Yanjie Wang, Yongjie Ye, Jingqun Tang, Ziwei Yang, Binghong Wu, Qi Liu, Hao Feng, Han Wang, Hao Liu, Can Huang

PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)

Challenge:	Existing methods for integrating spatial layouts with text have limitations . existing methods produce overly long text sequences or lack autoregressive traits of LLMs .
Approach:	They introduce Interleaving Layout and Text in a Large Language Model (LayTextLLM) they use OCR-derived text and spatial layouts to integrate with LLMs for document understanding .
Outcome:	The proposed model shows an increase in performance in KIE and VQA tasks.

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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei

Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)

Challenge:	Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning.
Approach:	They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios.
Outcome:	The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics.

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Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)

Challenge:	Existing black-box jailbreak methods often rely on model feedback . existing methods may be intercepted by content moderators during the search process .
Approach:	They propose a method that guides malicious prompt construction by local training a mirror model of the target black-box model through benign data distillation.
Outcome:	The proposed method achieves a 92% attack success rate and 80% stealth rate on a subset of AdvBench.

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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang

GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)

Challenge:	Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability.
Approach:	They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality.
Outcome:	The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks.

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Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li

Predicting pragmatic discourse features in the language of adults with autism spectrum disorder (2021.acl-srw)

Challenge:	Existing fact-checking methods that use large language models often generate subtle factual errors.
Approach:	They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation.
Outcome:	GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call.

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Christine Yang, Duanchen Liu, Qingyun Yang, Zoey Liu, Emily Prud’hommeaux

MARS-Bench: A Multi-turn Athletic Real-world Scenario Benchmark for Dialogue Evaluation (2025.findings-emnlp)

Challenge:	Existing tools to quantify atypicality in discourse and pragmatics are difficult to precisely identify and quantify.
Approach:	They present a corpus of transcribed natural conversations produced in an experimental setting and annotate them for three pragmatic features on a three-point scale.
Outcome:	The proposed model yields higher accuracy than previous approaches for deriving these features, with F1 exceeding 0.82 for all three pragmatic features.

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Chenghao Yang, Yinbo Luo, Zhoufutu Wen, Qi Chu, Tao Gong, Longxiang Liu, Kaiyuan Zhang, Jianpeng Jiao, Ge Zhang, Wenhao Huang, Nenghai Yu

Knowledge Diffusion for Neural Dialogue Generation (P18-1)

Challenge:	Large Language Models (LLMs) have been widely adopted in real-world dialogue applications, but their robustness is criticized all along.
Approach:	They propose to use play-by-play text commentary to build a multi-turn athletic real-world scenario dialogue benchmark to evaluate three critical aspects of multi-turned conversations: ultra multi- turn, interactive multi-twist, and cross-turn tasks.
Outcome:	The proposed benchmarks outperform open-source LLMs on three critical aspects of multi-turn conversations: ultra multi-turned, interactive multi- turn, and cross-turn tasks.

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Shuman Liu, Hongshen Chen, Zhaochun Ren, Yang Feng, Qun Liu, Dawei Yin

CLaSp: In-Context Layer Skip for Self-Speculative Decoding (2025.acl-long)

Challenge:	End-to-end neural dialogue generation does not employ knowledge to guide the generation.
Approach:	They propose a neural knowledge diffusion model to introduce knowledge into dialogue generation.
Outcome:	The proposed model outperforms baseline models on a real-world dataset.

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Longze Chen, Renke Shan, Huiming Wang, Lu Wang, Ziqiang Liu, Run Luo, Jiawei Wang, Hamid Alinejad-Rokny, Min Yang

Challenge:	Existing methods for drafting Large Language Models require additional modules to be trained, which can be challenging to implement and ensure compatibility across various LLMs.
Approach:	They propose an in-context layer-skipping strategy for self-speculative decoding that uses a plug-and-play mechanism to skip intermediate layers of the verify model to construct a compressed draft model.
Outcome:	The proposed method achieves a speedup of 1.3 1.7 on LLaMA3 series models without altering the original distribution of the generated text.

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

Accurate Word Alignment Induction from Neural Machine Translation (2020.emnlp-main)

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Yun Chen, Yang Liu, Guanhua Chen, Xin Jiang, Qun Liu

Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation (2024.emnlp-main)

Challenge:	Prior work suggests that Transformer captures poor word alignments through its attention mechanism.
Approach:	They propose two new word alignment induction methods that use attention weights to capture accurate word alignments.
Outcome:	The proposed methods outperform baselines on three publicly available datasets and are significantly better than GIZA++.

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Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng, Shimin Tao, Xiaofeng Zhao, Mahong Xia, Zhang Li, Boxing Chen, Hao Yang, Bei Li, Tong Xiao, JingBo Zhu

Morpheme Sense Disambiguation: A New Task Aiming for Understanding the Language at Character Level (2024.lrec-main)

Challenge:	Existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset.
Approach:	They propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR).
Outcome:	The proposed method outperforms Alpaca's existing methods by 32.1% in GPT-4 evaluations.

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Yue Wang, Hua Zheng, Yaqi Yin, Hansi Wang, Qiliang Liang, Yang Liu

False Friends in the Shell: Unveiling the Emoticon Semantic Confusion in Large Language Models (2026.acl-long)

Challenge:	Morphemes are a strong linguistic feature to capture lexical semantics, but lack of morpheme-informed resources and the expense of manual annotations hinder morphme-enhanced methods.
Approach:	They propose a task of Morpheme Sense Disambiguation with two subtasks in-text and in-word to generalize morpheme features on more tasks.
Outcome:	The proposed tasks are based on two morpheme-annotated datasets for Chinese . the best model yields a promising precision of 77.66% on in-text and 88.19% on in word .

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Weipeng Jiang, Xiaoyu Zhang, Juan Zhai, Shiqing Ma, Chao Shen, Yang Liu

Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore (2025.coling-main)

Challenge:	Emoticons are widely used in digital communication to convey affective intent, yet their safety implications for Large Language Models (LLMs) remain largely unexplored.
Approach:	They propose to use ASCII-based emoticons to perform unintended actions in large language models (LLMs) This vulnerability is pervasive, with an average confusion ratio exceeding 38%, and 90% of confused responses yield 'silent failures' authors call on the community to recognize this emerging vulnerability and develop effective mitigation methods to uphold the safety and reliability of human-LLM interactions.
Outcome:	The proposed framework exploits emoticon semantic confusion in six LLMs and demonstrates that existing prompt-based mitigations are ineffective.

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Junchao Wu, Runzhe Zhan, Derek F. Wong, Shu Yang, Xuebo Liu, Lidia S. Chao, Min Zhang

PEToolLLM: Towards Personalized Tool Learning in Large Language Models (2025.findings-acl)

Challenge:	Existing methods for detecting LLM-generated text require no training data.
Approach:	They propose a black-box zero-shot detection approach that calculates the Grammar Error Correction Score for a given text to differentiate between human-written and LLM-generated texts.
Outcome:	The proposed method outperforms current state-of-the-art zero-shot and supervised methods, achieving an average AUROC of 98.62% across XSum and Writing Prompts datasets.

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Qiancheng Xu, Yongqi Li, Heming Xia, Fan Liu, Min Yang, Wenjie Li

Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models (2025.coling-main)

Challenge:	Existing tool learning studies focus on general-purpose tool-use capability, but ignore the importance of personalized tool-user preferences.
Approach:	They propose a framework to adapt Large Language Models to personalized tool learning task, which is trained through supervised fine-tuning and direct preference optimization.
Outcome:	Extensive experiments on PEToolBench show that the proposed framework outperforms existing LLMs in the personalized tool learning task.

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Xuanyu Lei, Zonghan Yang, Xinrui Chen, Peng Li, Yang Liu

A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese (2026.tacl-1)

Challenge:	Existing prompting techniques for Large Multi-Modal Models (LMMs) focus on improving textual reasoning or leveraging tools for image preprocessing, lacking a simple and general visual prompting scheme to promote vision-language coordination.
Approach:	They propose a prompting scheme that scaffolds coordinates to promote vision-language coordination in Large Multi-Modal Models (LMMs) they overlay a dot matrix within the image as visual information anchors and leverage multi-dimensional coordinates as textual positional references.
Outcome:	Experiments on a wide range of vision-language tasks show the superiority of SCAFFOLD prompting over the textual Chain-of-Thought prompting.

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Yikang Liu, Yeting Shen, Hongao Zhu, Lilong Xu, Zhiheng Qian, Siyuan Song, Kejia Zhang, Jialong Tang, Pei Zhang, Baosong Yang, Rui Wang, Hai Hu

Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues (2024.findings-acl)

Challenge:	Using sub-linear length normalized log-probabilities (SLLN-LP), we find unequal lengths of sentences in minimal pairs difficult for LMs even up to 32B parameters.
Approach:	They propose to use ZhoBLiMP as a linguistic minimal pair benchmark for Chinese language models to mitigate biases.
Outcome:	The proposed metric mitigates biases in Chinese language models with over 100 paradigms . Anaphor, Quantifiers, and Ellipsis are difficult for LMs even up to 32B parameters .

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Zhiyuan Chang, Mingyang Li, Yi Liu, Junjie Wang, Qing Wang, Yang Liu

Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2024.emnlp-main)

Challenge:	Existing jailbreak attacks primarily utilize scenario camouflage techniques, however their explicit mention of malicious intent will be easily recognized and defended by LLMs.
Approach:	They propose an indirect jailbreak attack approach, Puzzler, which can bypass LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query.
Outcome:	The proposed approach can bypass the LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query.

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Tong Zhang, Chen Huang, Yang Deng, Hongru Liang, Jia Liu, Zujie Wen, Wenqiang Lei, Tat-Seng Chua

Template-Based Named Entity Recognition Using BART (2021.findings-acl)

Challenge:	Non-collaborative dialogue agents are expected to engage in strategic conversations with diverse users, and this poses two main challenges for existing dialogue agents: 1) the inability to integrate user-specific characteristics into the strategic planning; 2) the difficulty of training strategic planners that can be generalized to diverse users.
Approach:	They propose to integrate a user-aware strategic planning module and a population-based training paradigm into a non-collaborative dialogue agent for securing a mutual agreement that leans favorably towards the system's objectives.
Outcome:	The proposed model can be used to achieve a mutual agreement that leans favorably towards the system's objectives.

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Leyang Cui, Yu Wu, Jian Liu, Sen Yang, Yue Zhang

Knowledge Image Matters: Improving Knowledge-Based Visual Reasoning with Multi-Image Large Language Models (2025.acl-long)

Challenge:	Existing methods for fewshot NER do not make full use of knowledge transfer in NER model parameters.
Approach:	They propose a template-based method for NER that treats NER as a language model ranking problem in a sequence-to-sequence framework.
Outcome:	The proposed method achieves 92.55% F1 score on the CoNLL03 task and significantly better than fine-tuning BERT 10.88%, 15.34%, and 11.73% F1 scores on the MIT Movie, the ATIS, and the MATLAB task.

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Guanghui Ye, Huan Zhao, Zhixue Zhao, Xupeng Zha, Yang Liu, Zhihua Jiang

Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)

Challenge:	Knowledge-based visual reasoning (KB-VR) is a challenging task, as it requires machines not only to understand concepts and relationships of visual scenes, but also to associate them with external world knowledge to perform chain of reasoning on open-world questions.
Approach:	They propose a visual knowledge card (VKC) that integrates internal visual knowledge and external world knowledge produced by a knowledge generator into an image.
Outcome:	The proposed model achieves new state-of-the-art results compared to previous top-performing models on three popular KB-VR benchmarks.

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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang

Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning (2022.emnlp-main)

Challenge:	Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios.
Approach:	They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice.
Outcome:	The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models .

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Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tur

MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization (2026.acl-long)

Challenge:	Prefix-tuning is an essential paradigm of parameter-efficient transfer learning . fine-tuned models require separate copies of model parameters for each task .
Approach:	They propose to understand and further develop prefix-tuning through the kernel lens . they propose a new variant of prefix tuning that shares the exact mechanism as prefix tun .
Outcome:	The proposed method improves prefix-tuning performance by training only a small portion of parameters.

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Yang Zhao, Hepeng Wang, Xiao Ding, Yangou Ouyang, Bibo Cai, Kai Xiong, Jinglong Gao, Zhouhao Sun, Li Du, Bing Qin, Ting Liu

MUSE: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles (2025.findings-acl)

Challenge:	Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), but its efficacy is confined to domains with verifiable ground truths.
Approach:	They propose a meta-cognitive orchestration layer that treats reward scalarization as a dynamic latent policy, leveraging the model’s terminal hidden states as 'a semantic bottleneck' . Across seven benchmarks, MAESTRO consistently outperforms single-reward and static multi-objective baselines while preserving the efficiency advantages of GRPO.
Outcome:	The proposed model outperforms single-reward and static multi-objective baselines while preserving efficiency advantages.

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Zihan Wang, Xiaocui Yang, YongKang Liu, Shi Feng, Daling Wang, Yifei Zhang

KnowDR-REC: Auditing Knowledge-Conditioned Visual Grounding in Referring Expression Comprehension (2026.findings-acl)

Challenge:	Existing research focuses solely on text, leaving a gap with practical applications.
Approach:	They propose to synthesize a multimodal conversational recommendation dataset using multimodal large language models to automatically synthesized data from 7,000 conversations in the Clothing domain.
Outcome:	The proposed dataset contains 83,148 utterances from 7,000 conversations centered around the Clothing domain.

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Guanghao Jin, Jingpei Wu, Tianpei Guo, Yiyi Niu, Weidong Zhou, Linyi Yang, Guoyang Liu

Benchmarking LLMs on Authentic Cases from Medical Journals (2026.findings-acl)

Challenge:	Existing evaluation metrics suggest that Multimodal large language models have acquired fine-grained visual grounding capabilities.
Approach:	They propose a benchmark to assess Referring Expression Comprehension (REC) that uses intra-image visual cues to localize target objects and a controllable evaluation mechanism to test sensitivity to fine-grained factual changes.
Outcome:	The proposed benchmarks show that multimodal large language models have a high level of performance on the RefCOCO family of benchmarks.

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Wanlong Liu, Junying Chen, Yunjin Yang, Prayag Tiwari, Wenyu Chen, Benyou Wang

Challenge:	Existing medical benchmarks suffer from performance saturation due to medical exam questions.
Approach:	They evaluate the performance of over 20 open-source and proprietary large language models and benchmark them against human medical experts.
Outcome:	The new benchmark is based on authentic clinical cases sourced from medical journals and implements rigorous human review process to ensure the quality and reliability of the benchmark.

M2C: Towards Automatic Multimodal Manga Complement (2023.findings-emnlp)

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Hongcheng Guo, Boyang Wang, Jiaqi Bai, Jiaheng Liu, Jian Yang, Zhoujun Li

Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey (2026.findings-eacl)

Challenge:	Multimodal manga analysis focuses on enhancing manga understanding with visual and textual features.
Approach:	They propose a task to enhance manga understanding with visual and textual features by providing a shared semantic space for vision and language understanding.
Outcome:	The proposed task provides a shared semantic space for vision and language understanding.

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Seunghyuk Cho, Zhenyue Qin, Yang Liu, Youngbin Choi, Seungbeom Lee, Dongwoo Kim

Model Balancing Helps Low-data Training and Fine-tuning (2024.emnlp-main)

Challenge:	Plane geometry problem solving has gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models.
Approach:	They present a systematic review of existing work in PGPS and summarize their results.
Outcome:	The proposed frameworks are compared with existing frameworks and analyze them according to their architectural designs.

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Zihang Liu, Yuanzhe Hu, Tianyu Pang, Yefan Zhou, Pu Ren, Yaoqing Yang

ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)

Challenge:	Recent advances in foundation models have emphasized the need to align pre-trained models with specialized domains using small, curated datasets.
Approach:	They propose a layer-wise learning rate scheduler that balances training quality across layers . they adapt it to a curated dataset to achieve alignment with specialized domains .
Outcome:	The proposed model shows that it can be used to balance training quality across layers and improve low-data training and fine-tuning for both NLP and SciML tasks.

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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong

Think Outside the Policy: In-Context Steered Policy Optimization (2026.findings-acl)

Challenge:	Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs).
Approach:	They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models.
Outcome:	The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks.

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Hsiu-Yuan Huang, Chenming Tang, Weijie Liu, Clive Bai, Saiyong Yang, Yunfang Wu

Adaptive Immune-based Sound-Shape Code Substitution for Adversarial Chinese Text Attacks (2024.emnlp-main)

Challenge:	Existing Reinforcement Learning from Verifiable Rewards (RLVR) methods exhibit limited exploration due to reliance on on-policy rollouts which are limited to the current policy’s distribution, resulting in narrow trajectory diversity.
Approach:	They propose a framework that leverages the in-context learning capability of Large Reasoning Models to provide expert guidance using existing datasets.
Outcome:	The proposed framework improves RLVR performance and training stability on mathematical reasoning benchmarks.

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Ao Wang, Xinghao Yang, Chen Li, Bao-di Liu, Weifeng Liu

Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)

Challenge:	Existing text attack methods are designed for English text, but robust implementation of Chinese text is understudied.
Approach:	They propose an adaptive immune-based sound-shape code algorithm for Chinese text attacks . they leverage the Sound-Shape Code to generate natural substitutions .
Outcome:	The proposed algorithm produces high-quality Chinese adversarial examples . it can reduce duplication of population and improve search ability .

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Han Weng, Puzhen Wu, Cui Longjie, Yi Zhan, Boyi Liu, Yuanfeng Song, Dun Zeng, Yingxiang Yang, Qianru Zhang, Dong Huang, Xiaoming Yin, Yang Sun, Xing Chen

Empathy in Diversity: Personalized Depression and Anxiety Therapy via Dialogue State Tracking and Patient-Aware Planning (2026.acl-long)

Challenge:	Existing methods to enhance performance of large language models (LLMs) on Text-to-SQL tasks rely on execution-based or LLM-based reward models.
Approach:	They propose a reward model framework for RL-based Text-to-SQL that employs the GMNScore outcome reward model.
Outcome:	The proposed reward model outperforms existing reward models on standard benchmarks including Spider and BIRD.

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Xinwei Yang, Junyi Fan, Yuqing Liu, Jiaxuan Wang, Jiashuai Zhang, Hongru Liang, Wenqiang Lei, Yao Song

Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies? (2026.findings-acl)

Challenge:	Recent efforts have turned to large language models (LLMs) as therapeutic agents for psychological therapy tasks, yet robustness across diverse patients remains underexplored.
Approach:	They propose a realistic role-play protocol for evaluating therapeutic dialogue agents and a de-identified, expert-annotated corpus of therapist–patient dialogues.
Outcome:	The proposed framework outperforms baselines on therapeutic outcomes and dialogue quality while improving conversational efficiency.

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Mengna Zhu, Jibing Wu, Lihua Liu, Yuran Gong, Yang Hao, Fu Yachao, Mao Wang, Lei Hou, Juanzi Li

RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models (2024.findings-naacl)

Challenge:	Existing research has focused on the earlier stages of emergency response . lack of suitable datasets for reliable and compliance-aware decision-oriented modeling and evaluation is limiting current research .
Approach:	They propose a first real-world emergency decision-making dataset EDM-Bench . they propose 'rule-enhanced reasoning framework' that integrates external regulatory knowledge with constrained inference mechanisms to improve both decision safety and interpretability.
Outcome:	The proposed framework improves decision safety and interpretability by integrating regulatory knowledge with constrained inference mechanisms.

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Saeed Khaki, JinJin Li, Lan Ma, Liu Yang, Prathap Ramachandra

Unsupervised Preference-Aware Language Identification (2022.findings-acl)

Challenge:	Reinforcement learning with human feedback (RLHF) is widely employed to align large language models with user intent.
Approach:	They propose to combine rejection sampling and direct preference optimization to improve alignment with user intent by identifying pairs of contrastive samples from human annotator and alternative LLMs.
Outcome:	The proposed method outperforms existing methods including RS, PPO, and DPO in a limited resource environment.

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Xingzhang Ren, Baosong Yang, Dayiheng Liu, Haibo Zhang, Xiaoyu Lv, Liang Yao, Jun Xie

Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter (2023.findings-acl)

Challenge:	Existing studies do not consider inter-personal variations due to the lack of user annotated training data.
Approach:	They propose to use user preferences to identify ambiguous texts in multilingual applications without user annotated training data to build a preference-aware LID model.
Outcome:	The proposed model significantly outperforms existing LID systems on handling ambiguous texts.

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Yi Liu, Xiaohan Bi, Lei Li, Sishuo Chen, Wenkai Yang, Xu Sun

ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition (2026.findings-acl)

Challenge:	Existing frameworks for federated multilingual neural machine translation (Fed-MNMT) are limited in language resources.
Approach:	They propose a framework that keeps PLMs frozen and only transfers lightweight adapter modules between clients.
Outcome:	The proposed framework reduces communication cost by over 98% while achieving similar or even better performance compared to baselines.

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Yujie Liu, Zonglin Yang, Tong Xie, Jinjie Ni, Ben Gao, Yuqiang Li, Shixiang Tang, Wanli Ouyang, Erik Cambria, Dongzhan Zhou

START: Self-taught Reasoner with Tools (2025.emnlp-main)

Challenge:	Large language models have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark.
Approach:	They propose a benchmark for evaluating large language models on a sufficient set of scientific discovery sub-tasks.
Outcome:	The proposed framework extracts critical components from papers across 12 disciplines with expert validation confirming its accuracy.

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Chengpeng Li, Mingfeng Xue, Zhenru Zhang, Jiaxi Yang, Beichen Zhang, Bowen Yu, Binyuan Hui, Junyang Lin, Xiang Wang, Dayiheng Liu

Text Summarization with Pretrained Encoders (D19-1)

Challenge:	Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations.
Approach:	They propose a training-free approach that activates LRMs’ latent tool-use capabilities through artificial hints and a framework that enables models to learn effective tool utilization through diverse hint patterns and rejection-based data synthesis.
Outcome:	Experiments show that START significantly improves state-of-the-art LRMs across challenging benchmarks, including competition-level mathematics (AMC23: 95.0%, AIME24: 75.6%) and graduate-level science questions (GPQA: 64.6%).

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Yang Liu, Mirella Lapata

Self-Awareness before Action: Mitigating Logical Inertia via Proactive Cognitive Awareness (2026.findings-acl)

Challenge:	Existing pretraining languages such as ELMo and GPT have advanced a wide range of tasks.
Approach:	They propose a novel document-level encoder based on BERT which can express the semantics of a document and obtain representations for its sentences.
Outcome:	The proposed model achieves state-of-the-art in extractive and abstractive settings.

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Fulong Fan, Peilin Liu, Liu FengZhe, Shuyan Yang, Gang Yan

Language Constrained Multimodal Hyper Adapter For Many-to-Many Multimodal Summarization (2025.acl-long)

Challenge:	Existing work on abductive and long-context reasoning reports that current models still lack self-awareness of missing premises.
Approach:	They propose a reasoning framework that introduces self-awareness of missing premises before making the final decision.
Outcome:	SABA achieves best performance on all three difficulty splits of detective puzzle benchmark . a small early mistake can remain uncorrected and can guide later reasoning .

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Nayu Liu, Fanglong Yao, Haoran Luo, Yong Yang, Chen Tang, Bo Lv

Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN (2022.naacl-main)

Challenge:	Existing models that share parameters neglect the language-specific knowledge learning.
Approach:	They propose a language-constrained multimodal hyper adapter for multimodal summarization that integrates language-specific adapters into multilingual pre-trained backbones.
Outcome:	The proposed model can generate summaries based on multimodal documents such as text and visuals, allowing people to quickly locate key information from the vast multimedia con.

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Si-An Chen, Jie-jyun Liu, Tsung-Han Yang, Hsuan-Tien Lin, Chih-Jen Lin

CTC-based Non-autoregressive Speech Translation (2023.acl-long)

Challenge:	XML-CNN has been a popular research topic in NLP due to its superior performance . however, the increasing complexity brings difficulties to ensure the true architectural progress .
Approach:	They propose to re-examine an influential multi-label text classification method . they propose suitable baselines for multi-level text classification tasks .
Outcome:	The proposed method performs better than the original model, the authors show . they show that the re-implementation reveals contradictory results to the original work .

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Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu

A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020.coling-main)

Challenge:	End-to-end speech translation (E2E ST) and non-autoregressive (NAR) generation are promising in language and speech processing for their advantages of less error propagation and low latency.
Approach:	They develop a model that uses connectionist temporal classification to predict the source and target texts.
Outcome:	The proposed model achieves an average BLEU score of 29.5 with a speed-up of 5.67.

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Yiming Cui, Ting Liu, Ziqing Yang, Zhipeng Chen, Wentao Ma, Wanxiang Che, Shijin Wang, Guoping Hu

BMAM: Brain-inspired Multi-Agent Memory Framework (2026.findings-acl)

Challenge:	Using cloze-style reading comprehension, Chinese machine reading comprehension datasets are becoming more and more popular . a new task is proposed to fill the right candidate sentence into the passage with several blanks .
Approach:	They propose a Chinese task to fill the right candidate sentence into a passage with blanks . they build a dataset to evaluate the difficulty of the task and make fake candidates .
Outcome:	The proposed task fills the right candidate sentence into the passage with blanks . the proposed dataset contains over 100K blanks within over 10K passages based on Chinese narrative stories .

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Yang Li, Jiaxiang Liu, Yusong Wang, Yujie Wu, Mingkun Xu

Challenge:	Language-model-based agents operating over extended interactions face persistent challenges in preserving temporally grounded information and maintaining behavioral consistency across sessions.
Approach:	They propose a general-purpose memory architecture that decomposes agent memory into six components that operate at complementary time scales.
Outcome:	BMAM outperforms memory-augmented baselines on LoCoMo benchmarks with 78.45% accuracy . a targeted refinement of the temporal-trigger heuristics raises LongMemEval multi-session accuracy from 45.2% to 56.4%.

Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models (2024.findings-emnlp)

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Wenhao Shi, Zhiqiang Hu, Yi Bin, Junhua Liu, Yang Yang, See-Kiong Ng, Lidong Bing, Roy Lee

ACRM: Multi-Agent Trajectory Learning for Automated Credit Risk Model Refreshing in Production (2026.acl-industry)

Challenge:	Existing image instruction fine-tuning datasets do not fully exploit visual information to enhance multimodal reasoning capabilities of Large language models (LLMs).
Approach:	They propose a LLaVA-based model fine-tuned with MathV360K to bridge this gap by collecting 40K high-quality images with question-answer pairs from 24 existing datasets and synthesizing 320K new pairs.
Outcome:	The proposed model improves the multimodal reasoning capabilities of LLaVA-1.5 and demonstrates enhanced generalizability on the MMMU benchmark.

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Liangzu Liu, Mengzhe Ruan, Xiaotian Chen, null HaonanChen, null XudongNiu, Wendi Yuan, null YuechenLi, Yang Liu, Guanjun Wang

Challenge:	Credit risk models suffer from rapid performance decay due to distribution shifts, requiring frequent updates to meet strict operational guardrails.
Approach:	They propose a multi-agent framework that treats model refreshing as a learnable trajectory of agent interactions.
Outcome:	The proposed framework reduces the average model refresh cycle from weeks to 1.1 days and iteration rounds by 65% while maintaining superior stability metrics.

Evaluating the Performance of Transformer-based Language Models for Neuroatypical Language (2022.coling-1)

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Duanchen Liu, Zoey Liu, Qingyun Yang, Yujing Huang, Emily Prud’hommeaux

Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels (2023.findings-acl)

Challenge:	Difficulties with social aspects of language are among the hallmarks of autism spectrum disorder (ASD).
Approach:	They propose a transformer-based framework for identifying linguistic features associated with social aspects of communication using a corpus of conversations between adults with and without ASD and neurotypical conversational partners.
Outcome:	The proposed framework yields strong accuracy overall, but performance is significantly worse for the language of participants with ASD, suggesting they use a more diverse set of strategies for some social linguistic functions.

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Bang Yang, Fenglin Liu, Zheng Li, Qingyu Yin, Chenyu You, Bing Yin, Yuexian Zou

MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)

Challenge:	Existing approaches to generate informative titles for products with limited labels are inadequate for novel products.
Approach:	They propose a prompt-based approach to generate attractive titles for novel products . they use multimodal prompts to preserve characteristics and writing styles of novel products.
Outcome:	The proposed approach achieves state-of-the-art results on novel product categories with limited labels.

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Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun

What the DAAM: Interpreting Stable Diffusion Using Cross Attention (2023.acl-long)

Challenge:	Scientific data visualization is an essential process in research, but its use of large language models remains unexplored.
Approach:	They propose a model-agnostic LLM agent framework to automate scientific data visualization tasks.
Outcome:	The proposed framework improves performance of commercial and open-source models.

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Raphael Tang, Linqing Liu, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Pontus Stenetorp, Jimmy Lin, Ferhan Ture

Noisy Self-Knowledge Distillation for Text Summarization (2021.naacl-main)

Challenge:	a new text-image attribution analysis model for text-to-image generation is understudied due to ethical constraints . corporators have restricted the general public from using the models and their weights .
Approach:	They perform a text-image attribution analysis on Stable Diffusion, a recently open-sourced model.
Outcome:	The proposed method achieves a competitive 58.8-64.8 mIoU on noun segmentation and fair to good mean opinion scores on all parts of speech rated by humans . it also achieves good attribution quality on all part of speech, rated in humans - and the first to interpret large diffusion models from a visuolinguistic perspective.

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Yang Liu, Sheng Shen, Mirella Lapata

Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks (2023.findings-acl)

Challenge:	Existing approaches to summarize text using a single reference and noisy datasets are ill-suited to summarising on single reference datasets.
Approach:	They propose to use self-knowledge distillation to improve text summarization by generating smoothed labels for students and teachers to reduce model uncertainty.
Outcome:	The proposed framework improves on pretrained and non-pretrained models on three benchmarks.

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Zhicheng Guo, Sijie Cheng, Yile Wang, Peng Li, Yang Liu

WildSci: Advancing Scientific Reasoning from In-the-Wild Literature (2026.findings-acl)

Challenge:	Recent studies focus on retrieval to solve knowledge-intensive tasks, but the potential of retrieval for non-knowledge-intensive (NKI) tasks remains under-explored.
Approach:	They propose a task-agnostic retrieval framework for NKI tasks that uses a static index and a prompt-guided reranker to re-rank the nearest evidence according to task-specific relevance.
Outcome:	The proposed framework outperforms state-of-the-art retrieval-augmented methods on NKI tasks and will be released for further research.

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Tengxiao Liu, Deepak Nathani, Zekun Li, Kevin Yang, William Yang Wang

Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations (2025.findings-naacl)

Challenge:	Recent advances in large language model reasoning focus on mathematics and coding domains, but scientific reasoning remains limited in other domains due to limited dataset coverage.
Approach:	They propose a framework for sustainable scientific reasoning QA generation by synthesizing a new dataset of domain-specific science questions from peer-reviewed literature.
Outcome:	The proposed framework and dataset enable scalable and sustainable research in scientific reasoning.

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Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran Yang, Yumeng Zhang, Shan Huang, Yiran Wei, Wai Lam

Table-R1: Region-based Reinforcement Learning for Table Understanding (2026.findings-acl)

Challenge:	a new paradigm for dialogue systems is being developed to mimic human interactions . the current single-step dialogue paradigm lacks the depth and fluidity of human interactions.
Approach:	They propose a step-by-step dialogue paradigm that mimics human interactions . they use a dataset to fine-tune existing language models .
Outcome:	The proposed system mimics the dynamic nature of human conversations . it is compared with existing paradigms and will be released later this year .

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Zhenhe Wu, Jian Yang, Zhongjiang He, Changzai Pan, Jiaheng Liu, Xianjie Wu, Yu Zhao, Shuangyong Song, Yongxiang Li, Zhoujun Li, Xuelong Li

Are Large Language Models (LLMs) Good Social Predictors? (2024.findings-emnlp)

Challenge:	Tables are a widely used data format that poses unique challenges for language models due to their structured row-column interactions.
Approach:	They propose a region-based reinforcement learning approach that integrates region evidence into reasoning steps.
Outcome:	The proposed method outperforms baseline models on three benchmark datasets and significantly reduces the reasoning token consumption by 67.5%.

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Kaiqi Yang, Hang Li, Hongzhi Wen, Tai-Quan Peng, Jiliang Tang, Hui Liu

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens (2026.findings-acl)

Challenge:	Existing studies suggest that Large Language Models can generate human-like responses, but it is unclear how well they work and where the plausible predictions derive from.
Approach:	They propose to use LLMs to generate human-like responses by mutability and accessibility of social inputs to perform a social prediction task.
Outcome:	The proposed model performs well in three realistic settings and a novel social prediction task.

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Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu

Discarding the Crutches: Adaptive Parameter-Efficient Expert Meta-Learning for Continual Semantic Parsing (2025.coling-main)

Challenge:	Chain-of-Thought (CoT) prompting has been shown to be effective in eliciting structured reasoning from large language models (LLMs).
Approach:	They propose a data distribution lens to understand when and why CoT reasoning fails . they propose 'data-based' training that trains LLMs from scratch .
Outcome:	The proposed model enables models to generate reasoning trajectories that approximate those observed during training.

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Ruiheng Liu, Jinyu Zhang, Yanqi Song, Yu Zhang, Bailong Yang

Memory-Efficient Fine-Tuning of Transformers via Token Selection (2024.emnlp-main)

Challenge:	Continual Semantic Parsing (CSP) enables parsers to generate SQL from natural language questions in task streams, using minimal annotated data to handle dynamically evolving databases in real-world scenarios.
Approach:	They propose a Adaptive PET eXpert meta-learning approach that assists experts in adaptively warming up, ensuring better model initialization.
Outcome:	The proposed method outperforms existing methods on two benchmarks and achieves superior performance without data replay or ideal settings.

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Antoine Simoulin, Namyong Park, Xiaoyi Liu, Grey Yang

From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues (2024.findings-emnlp)

Challenge:	Existing methods for fine-tuning require caching of intermediate activations to update weights during the backward pass.
Approach:	They develop a method to reduce memory usage in fine-tuning of transformers by backpropagating through just a subset of input tokens.
Outcome:	The proposed method reduces memory usage and memory footprint on large transformer models . it can be easily combined with existing methods like LoRA, reducing memory cost .

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Yu Li, Devamanyu Hazarika, Di Jin, Julia Hirschberg, Yang Liu

Full Parameter Fine-tuning for Large Language Models with Limited Resources (2024.acl-long)

Challenge:	a recent study shows that robots display human-like characteristics in dialogues . this anthropomorphism raises concerns about the accuracy of AI and its capabilities .
Approach:	They propose to use a dataset to analyze self-anthropomorphic and non-self-anthropophilic responses in robots . they propose to combine these two types of responses to create a new category of bot responses .
Outcome:	The proposed approach preserves the original dialogues from existing corpora and enhances them with paired responses: self-anthropomorphic and non-self-anthropophilic for each original bot response.

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Kai Lv, Yuqing Yang, Tengxiao Liu, Qipeng Guo, Xipeng Qiu

GCPG: A General Framework for Controllable Paraphrase Generation (2022.findings-acl)

Challenge:	Large Language Models (LLMs) require massive GPU resources for training.
Approach:	They propose a parameter-efficient optimization that fuses the gradient computation and parameter update in one step to reduce memory usage.
Outcome:	The proposed method reduces memory usage to 10.8% compared to the standard approach.

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Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Haibo Zhang, Xue Zhao, Wenqing Yao, Boxing Chen

Rethinking Long Context Generation from the Continual Learning Perspective (2025.coling-main)

Challenge:	Existing studies highlight a special condition under two indispensable aspects of controllable paraphrase generation (CPG) individually, lacking a unified circumstance to explore and analyze their effectiveness.
Approach:	They propose a general controllable paraphrase generation framework that integrates lexical and syntactical conditions into a text sequence and uniformly processes them in an encoder-decoder paradigm.
Outcome:	The proposed framework can combine lexical and syntactical conditions and improve paraphrase generation.

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Zeyuan Yang, Fangzhou Xiong, Peng Li, Yang Liu

Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)

Challenge:	Large Language Models (LLMs) struggle with processing long contexts due to the limited context window.
Approach:	They propose to combine a limited context window with a continual learning perspective to improve LLMs' efficiency in processing long contexts.
Outcome:	The proposed models improve the performance of Large Language Models (LLMs) by integrating learning strategies with existing approaches.

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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang

XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)

Challenge:	Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications.
Approach:	They propose a reliable strategy for domains to choose more robust LLMs for real-world applications.
Outcome:	The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications.

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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li

End-to-End Unsupervised Vision-and-Language Pre-training with Referring Expression Matching (2022.emnlp-main)

Challenge:	Document AI parsing semi-structured image form is a key information extraction task.
Approach:	They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework.
Outcome:	The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings.

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Chi Chen, Peng Li, Maosong Sun, Yang Liu

Contrastive Pre-training for Personalized Expert Finding (2023.findings-emnlp)

Challenge:	Existing unsupervised vision-and-language pre-training methods take pre-extracted region-based visual features from external object detectors, which limits flexibility and reduces computational efficiency.
Approach:	They propose an unsupervised vision-and-language pre-training task that predicts which patches contain an object referred to in natural language from the encoded visual features.
Outcome:	The proposed approach outperforms existing methods and obtains state-of-the-art results on four vision-and-language tasks.

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Qiyao Peng, Hongtao Liu, Zhepeng Lv, Qing Yang, Wenjun Wang

T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation (2023.findings-acl)

Challenge:	Existing approaches to expert finding are effective for a community question answering platform.
Approach:	They propose a CQA-domain Contrastive Pre-training framework for Expert Finding which could learn more comprehensive question representations.
Outcome:	The proposed framework could learn more comprehensive question representations on six real-world datasets.

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Jialu Wang, Xinyue Liu, Zonglin Di, Yang Liu, Xin Wang

Analyzing the Limits of Self-Supervision in Handling Bias in Language (2022.findings-emnlp)

Challenge:	Recent advances in text-to-image generative models have produced high quality images with a breakthrough of inference speed.
Approach:	They propose a text-to-image association test framework that quantifies implicit stereotypes between concepts and valence and those in images.
Outcome:	The proposed framework quantifies implicit stereotypes between concepts and valence and those in images.

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Lisa Bauer, Karthik Gopalakrishnan, Spandana Gella, Yang Liu, Mohit Bansal, Dilek Hakkani-Tur

MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices (2020.acl-main)

Challenge:	a recent study shows that natural language models can perform tasks with little to no in-context supervision . a number of tasks are performed using self-supervised pre-training .
Approach:	They define and comprehensively evaluate how well natural language taskprompting captures the semantics of four tasks for bias: diagnosis, identification, extraction and rephrasing.
Outcome:	The proposed model performs to wide varying degrees across bias dimensions . the model is largely challenged when prompted to perform these tasks .

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Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, Denny Zhou

Uncovering Strategic Egoism Behaviors in Large Language Models (2026.findings-acl)

Challenge:	Empirical studies show that MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE . BERT is one of the largest models ever in NLP, but suffers from heavy model size and high latency .
Approach:	They propose a tool to compress and accelerate the popular BERT model by task-agnostic application.
Outcome:	The proposed model is 4.3x smaller and 5.5x faster than BERT_BASE . it achieves competitive results on well-known benchmarks .

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Yaoyuan Zhang, Zonghao Ying, Aishan Liu, Jian Yang, Tianlin Li, Yaodong Yang, Xianglong Liu

TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing (2020.acl-demos)

Challenge:	Extensive experiments on 9 proprietary LLMs reveal that SE behaviors are widespread . study identifies egoistic decision-making as a risk for large language models .
Approach:	They propose a benchmark to measure egoistic behavior in large language models . they propose toxicity, jailbreak vulnerability and a lightweight mitigation that reinforces situational constraints .
Outcome:	The proposed model has a 67.96% occurrence rate and frequently manifests as manipulative coercion.

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Ziqing Yang, Yiming Cui, Zhipeng Chen, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

Exploring the Impact of Model Scaling on Parameter-Efficient Tuning (2023.emnlp-main)

Challenge:	Large pre-trained language models have hundreds of millions of parameters and take several gigabytes of memory to train and inference.
Approach:	They propose an open-source knowledge distillation toolkit designed for natural language processing that provides a set of predefined distillation methods and can be extended with custom code.
Outcome:	The proposed method is comparable with or even higher than the public distilled BERT models with similar numbers of parameters.

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Yusheng Su, Chi-Min Chan, Jiali Cheng, Yujia Qin, Yankai Lin, Shengding Hu, Zonghan Yang, Ning Ding, Xingzhi Sun, Guotong Xie, Zhiyuan Liu, Maosong Sun

ETRQA: A Comprehensive Benchmark for Evaluating Event Temporal Reasoning Abilities of Large Language Models (2025.findings-acl)

Challenge:	Parameter-efficient tuning (PET) methods can drive large pre-trained language models by training only minimal parameters.
Approach:	They propose a parameter-efficient tuning method that is compatible with a tunable module and uses a random number generator to optimize fewer table parameters.
Outcome:	The proposed method is compatible with a tunable module and tested on 11 NLP tasks.

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Sigang Luo, Yinan Liu, Dongying Lin, Yingying Zhai, Bin Wang, Xiaochun Yang, Junpeng Liu

Pixel-Level Reasoning Segmentation via Multi-turn Conversations (2025.acl-long)

Challenge:	Event temporal reasoning (ETR) is a significant indicator that a large language model understands the physical world.
Approach:	They propose a unified taxonomy for event temporal questions and construct a benchmark based on this taxonomies.
Outcome:	The proposed taxonomy inherits and expands existing datasets and contains multiple categories of compound questions.

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Dexian Cai, Xiaocui Yang, YongKang Liu, Daling Wang, Shi Feng, Yifei Zhang, Soujanya Poria

An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing (2021.emnlp-main)

Challenge:	Existing visual perception systems focus on region-level segmentation in single-turn dialogues . existing systems cannot reason at the pixel level and comprehend dynamic user intent .
Approach:	They propose a task that tracks evolving user intent via multi-turn interactions for fine-grained segmentation.
Outcome:	The proposed method outperforms existing baselines in segmentation and reasoning metrics.

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Yi Chen, Haiyun Jiang, Lemao Liu, Shuming Shi, Chuang Fan, Min Yang, Ruifeng Xu

Mask-Align: Self-Supervised Neural Word Alignment (2021.acl-long)

Challenge:	Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET) however, there is no comprehensive understanding of how to make better use of the existing information sources and how they affect the performance of ZFET.
Approach:	They propose a multi-source fusion model targeting auxiliary information from multiple sources to improve zero-shot fine-grained entity typing (ZFET)
Outcome:	The proposed model achieves 11.42% and 22.84% gains over state-of-the-art baselines on BBN and Wiki respectively with regard to macro F1 scores.

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Chi Chen, Maosong Sun, Yang Liu

InfiMM: Advancing Multimodal Understanding with an Open-Sourced Visual Language Model (2024.findings-acl)

Challenge:	Word alignment is an important task in many natural language processing tasks.
Approach:	They propose a self-supervised word alignment model that takes advantage of the full context on the target side.
Outcome:	The proposed model outperforms previous unsupervised models and obtains state-of-the-art results on four language pairs.

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Haogeng Liu, Quanzeng You, Yiqi Wang, Xiaotian Han, Bohan Zhai, Yongfei Liu, Wentao Chen, Yiren Jian, Yunzhe Tao, Jianbo Yuan, Ran He, Hongxia Yang

OEE-CFC: A Dataset for Open Event Extraction from Chinese Financial Commentary (2024.findings-emnlp)

Challenge:	InfiMM is a multimodal large language model that adapts to complex vision-language tasks.
Approach:	They present a Multimodal Large Language Model that adapts to intricate vision-language tasks using large-scale training data and comprehensive training strategies.
Outcome:	Empirical evaluations across a variety of benchmarks underscore InfiMM’s remarkable capability in multimodal understanding.

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Qizhi Wan, Changxuan Wan, Rong Hu, Dexi Liu, Xu Wenwu, Kang Xu, Zou Meihua, Liu Tao, Jie Yang, Zhenwei Xiong

Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsification (2026.acl-long)

Challenge:	Existing corpora with unconventional entities serving as event arguments lack rich multi-events and shared arguments.
Approach:	They develop an open event template that includes 21 event argument roles and an open corpus supporting open event extraction.
Outcome:	The proposed corpus includes 17,469 events, 44,221 arguments, 3,644 complex arguments, and 5,898 shared arguments.

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Hong Huang, Decheng Wu, Qiangqiang Hu, Guanghua Yu, Jinhai Yang, Jianchen Zhu, Xue Liu, Dapeng Wu

Structured Mean-Field Variational Inference for Higher-Order Span-Based Semantic Role Labeling (2023.findings-acl)

Challenge:	ternary quantization is a powerful solution for resource-constrained edge devices . current implementations suffer from a fundamental misalignment with commodity hardware .
Approach:	They propose a hardware-efficient ternary quantization framework that packs weights into five bits to restore power-of-two alignment.
Outcome:	The proposed framework reduces weights to -1, 0, +1 while preserving power-of-two alignment.

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Wei Liu, Songlin Yang, Kewei Tu

Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning (2024.findings-acl)

Challenge:	Span-based semantic role labeling is traditionally tackled by BIObased sequence labeling approaches.
Approach:	They propose to decompose the edge from predicate word to argument span into three different edges, enabling higher-order inference.
Outcome:	The proposed model outperforms vanilla MFVI on span-based semantic role labeling benchmarks.

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Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu

CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models (2024.acl-long)

Challenge:	Empirical evidence shows that our proposed method improves performance across seven downstream tasks.
Approach:	They propose a logic-driven data augmentation approach that converts text into AMR graphs and converts them back into text to create augmented data.
Outcome:	The proposed method leads on the ReClor leaderboard and improves on seven downstream tasks.

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Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hongru Liang, Tat-Seng Chua

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Large language models are used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown.
Approach:	They propose a benchmark for evaluating large language models using a well-organized taxonomy.
Outcome:	The proposed model is based on a well-organized taxonomy and compares it with other models.

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Peiru Yang, Haoran Zheng, Tong Ju, Shiting Wang, Wanchun Ni, Jiajun Liu, Shangguang Wang, Yongfeng Huang, Tao Qi

MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization (2026.findings-acl)

Challenge:	Existing studies have investigated knowledge poisoning attacks in medical RAG systems . knowledge poison attacks can disrupt model outputs and undermine system reliability .
Approach:	They propose a knowledge poisoning framework that injects misinformation into textual data . they propose to use paired visual data as a query-agnostic trigger to promote retrieval .
Outcome:	The proposed framework produces clinically plausible but incorrect generations on five LLMs and datasets.

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Haidong Xin, Xinze Li, Zhenghao Liu, Yukun Yan, Shuo Wang, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun

Enhanced Language Representation with Label Knowledge for Span Extraction (2021.emnlp-main)

Challenge:	Existing memory systems can support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows.
Approach:	They propose a framework that augments memory systems with a self-evolving meta-memory . meta-meso is iteratively distilling transferable knowledge utilization experiences . results show MetaMem outperforms strong baselines by over 3.6% .
Outcome:	The proposed framework outperforms baselines by over 3.6% in the long-horizon human-LLM interaction.

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Pan Yang, Xin Cong, Zhenyu Sun, Xingwu Liu

Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization (2024.findings-acl)

Challenge:	Existing approaches to extract text spans from plain text do not fully exploit label knowledge.
Approach:	They propose a model to integrate label knowledge into text representations by encoding texts and annotations independently and then integrating label knowledge with an elaborate-designed semantics fusion module.
Outcome:	The proposed model achieves state-of-the-art performance on four benchmarks and reduces training time and inference time by 76% and 77% on average compared with the existing paradigm.

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Zhanhui Zhou, Jie Liu, Jing Shao, Xiangyu Yue, Chao Yang, Wanli Ouyang, Yu Qiao

Learning to Copy for Automatic Post-Editing (D19-1)

Challenge:	Recent approaches to language model alignment assume homogeneous human preferences, but actual human preferences vary widely and are hard to satisfy with a single language model.
Approach:	They propose an RL-free extension of Direct Preference Optimization (DPO) that folds language modeling directly into reward modeling and trains language models as collective reward models that combine all objectives with specific weights.
Outcome:	The proposed method matches or outperforms existing methods in safety alignment and long-form question answering.

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Xuancheng Huang, Yang Liu, Huanbo Luan, Jingfang Xu, Maosong Sun

Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning (2025.naacl-long)

Challenge:	Automatic post-editing (APE) is an important task in natural language processing.
Approach:	They propose a method that explicitly models how to copy words from a machine translation to a correct translation.
Outcome:	The proposed method outperforms all published methods on the WMT 2016-2017 datasets.

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Rujing Yao, Yang Wu, Chenghao Wang, Jingwei Xiong, Fang Wang, Xiaozhong Liu

DialogSum: A Real-Life Scenario Dialogue Summarization Dataset (2021.findings-acl)

Challenge:	Existing approaches to large language models focus on semantic similarity, neglecting the intricate logical structures and reasoning essential for addressing complex legal issues.
Approach:	They propose a Logical-Semantic Integration Model (LSIM) that bridges semantic and logical coherence and a supervised framework that integrates semantic features with in-context learning.
Outcome:	The proposed framework significantly improves accuracy and reliability on a real-world legal QA dataset.

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Yulong Chen, Yang Liu, Liang Chen, Yue Zhang

Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging (2024.emnlp-main)

Challenge:	Experimental results show unique challenges in dialogue summarization such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense.
Approach:	They propose a large-scale labeled dialogue summarization dataset . they use state-of-the-art neural models to analyze spoken dialogue summaries .
Outcome:	The proposed dataset can be used to analyze spoken dialogue summarization challenges.

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Deyuan Liu, Zhanyue Qin, Hairu Wang, Zhao Yang, Zecheng Wang, Fangying Rong, Qingbin Liu, Yanchao Hao, Bo Li, Xi Chen, Cunhang Fan, Zhao Lv, Dianhui Chu, Zhiying Tu, Dianbo Sui

An Extensible Plug-and-Play Method for Multi-Aspect Controllable Text Generation (2023.acl-long)

Challenge:	Existing methods for parameter pruning fail to utilize the knowledge from pruned parameters.
Approach:	They propose a method that uses manifold learning and the Information Bottleneck measure to merge similar layers to preserve model performance.
Outcome:	The proposed method outperforms pruning methods on multiple datasets and LLMs with quantization and achieves substantial compression ratios.

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Xuancheng Huang, Zijun Liu, Peng Li, Tao Li, Maosong Sun, Yang Liu

MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training (2025.acl-long)

Challenge:	Multi-aspect controllable text generation has attracted increasing attention . but the mutual interference of multiple prefixes limits its extensibility to training-time unseen combinations.
Approach:	They propose to use trainable gates to normalize the intervention of prefixes to restrain the interference.
Outcome:	The proposed approach outperforms baselines on constraint accuracy, text quality, and extensibility.

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Hui Huang, Jiaheng Liu, Yancheng He, Shilong Li, Bing Xu, Conghui Zhu, Muyun Yang, Tiejun Zhao

StableToolBench-MirrorAPI: Modeling Tool Environments as Mirrors of 7,000+ Real-World APIs (2025.findings-acl)

Challenge:	Existing methods for complex instruction-following with elaborate constraints rely on a weaker model, especially GPT-4, limiting their application.
Approach:	They propose a Multi-granularity Self-Contrastive Training framework to improve instruction alignment without relying on a stronger model.
Outcome:	The proposed framework improves instruction-following with elaborate constraints without external supervision on coarse and fine granularity.

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Zhicheng Guo, Sijie Cheng, Yuchen Niu, Hao Wang, Sicheng Zhou, Wenbing Huang, Yang Liu

Improving Abstractive Document Summarization with Salient Information Modeling (P19-1)

Challenge:	Existing tool environments face challenges in balancing stability, scale, and realism, especially for benchmarking purposes.
Approach:	They propose a framework that trains specialized LLMs to accurately simulate real API responses by supervised fine-tuning and chain-of-thought reasoning.
Outcome:	The proposed framework achieves superior accuracy and stability compared to state-of-the-art methods on the newly constructed MirrorAPI-Bench and its integration into StableToolBench.

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Yongjian You, Weijia Jia, Tianyi Liu, Wenmian Yang

AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models (2025.coling-demos)

Challenge:	Abstractive document summarization is a task of natural language generation which generates fluent summaries with salient information automatically.
Approach:	They propose to incorporate a Gaussian focal bias on attention scores into an encoder to enhance the perception of local context and to distinguish salient information precisely.
Outcome:	The proposed framework outperforms state-of-the-art models on the CNN/Daily Mail benchmark and is based on a focus-attention mechanism and two new extensions.

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Xiawei Liu, Shiyue Yang, Xinnong Zhang, Haoyu Kuang, Libo Sun, Yihang Yang, Siming Chen, Xuanjing Huang, Zhongyu Wei

Improving Zero-shot LLM Re-Ranker with Risk Minimization (2024.emnlp-main)

Challenge:	We introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation.
Approach:	They introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation.
Outcome:	The proposed system generates public responses considering demographic distributions.

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Xiaowei Yuan, Zhao Yang, Yequan Wang, Jun Zhao, Kang Liu

A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) are effective Query Likelihood Models, but their estimation is biased and the model's accuracy is poor.
Approach:	They propose a framework which leverages Bayesian decision theory to quantify and mitigate this bias.
Outcome:	The proposed framework improves re-ranking, especially in improving the Top-1 accuracy.

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Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu

CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases (2025.naacl-long)

Challenge:	With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks.
Approach:	They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information.
Outcome:	The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority.

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Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Qizhe Shieh, Wenmeng Zhou

Conflicts Make Large Reasoning Models Vulnerable to Attacks (2026.findings-acl)

Challenge:	Large Language Models excel in stand-alone code tasks but struggle with handling entire code repositories.
Approach:	They propose a system that integrates LLM agents with graph database interfaces extracted from code repositories.
Outcome:	The proposed system integrates LLM agents with graph database interfaces extracted from code repositories.

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Honghao Liu, Chengjin Xu, Xuhui Jiang, Cehao Yang, Shengming Yin, Zhengwu Ma, Lionel Ni, Jian Guo

RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy (2021.acl-long)

Challenge:	Large Reasoning Models have demonstrated outstanding capabilities in solving complex reasoning tasks by incorporating step-by-step chain-of-thought (CoT) reasoning.
Approach:	They evaluate three large reasoning models that perform explicit and coherent reasoning under conflicting objectives and use them to evaluate their performance.
Outcome:	The proposed models perform explicit and coherent reasoning before producing their outputs, improving problem-solving and multi-step decision making.

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Xiyan Fu, Yating Zhang, Tianyi Wang, Xiaozhong Liu, Changlong Sun, Zhenglu Yang

Self-Distillation with Meta Learning for Knowledge Graph Completion (2022.findings-emnlp)

Challenge:	Existing methods to learn vital information from dialogue context with limited data are limited due to limited words in utterances and huge gap between dialogue and its summary.
Approach:	They propose an unsupervised strategy to learn vital information from dialogue context . the proposed model uses a hypothetical foundation that a superior summary approximates a replacement of the original dialogue .
Outcome:	The proposed model outperforms existing models on a number of datasets.

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Yunshui Li, Junhao Liu, Min Yang, Chengming Li

MdEval: Massively Multilingual Code Debugging (2026.findings-acl)

Challenge:	Existing knowledge graph completion frameworks for knowledge graphs are far from complete and require missing triples to be added to them.
Approach:	They propose a dynamic pruning technique to obtain a pruned model from a large source model, where the pruning mask of the pruned models could be updated adaptively per epoch after the model weights are updated.
Outcome:	The proposed framework achieves competitive performance compared to strong baselines, while being 10x smaller than baselines.

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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li

Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction (2022.findings-naacl)

Challenge:	Existing benchmarks primarily focus on Python and are limited in terms of language diversity.
Approach:	They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions.
Outcome:	The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task.

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Yang Liu, Jinpeng Hu, Xiang Wan, Tsung-Hui Chang

Diffusion Glancing Transformer for Parallel Sequence-to-Sequence Learning (2024.naacl-long)

Challenge:	Existing methods for Few-shot Relation Extraction focus on implicitly introducing relation information to constrain the prototype representation learning.
Approach:	They propose a parameter-less method to promote few-shot relation extraction . they use a prototype rectification module to rectify original prototypes by relation information .
Outcome:	The proposed method achieves state-of-the-art on fewRel 1.0 and 2.0 datasets.

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Lihua Qian, Mingxuan Wang, Yang Liu, Hao Zhou

PerfCoder: Large Language Models for Interpretable Code Performance Optimization (2026.findings-acl)

Challenge:	Experimental results show that non-autoregressive generation models are superior in generation efficiency but inferior in generation quality.
Approach:	They propose a diffusion glancing transformer which employs a modality diffusion process and residual glancy sampling to improve multi-modality modeling.
Outcome:	The proposed model outperforms autoregressive and non-autoregressive models on machine translation and text generation benchmarks.

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Jiuding Yang, Shengyao Lu, Hongxuan Liu, Shayan Shirahmad Gale Bagi, Zahra Fazel, Tomasz Czajkowski, Di Niu

Longtriever: a Pre-trained Long Text Encoder for Dense Document Retrieval (2023.emnlp-main)

Challenge:	Large language models (LLMs) have advanced automatic code generation, but their ability to produce high-performance code remains limited.
Approach:	They propose a family of large language models that generate performance-enhanced code through interpretable and customized optimization strategies.
Outcome:	The proposed model outperforms existing models on the PIE code performance benchmark and produces interpretable feedback that can guide larger LLMs in a planner–optimizer workflow.

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Junhan Yang, Zheng Liu, Chaozhuo Li, Guangzhong Sun, Xing Xie

EMTIR-GRPO: Efficient Multi-Tool Augmented Large Language Models via Reinforcement Learning (2026.findings-acl)

Challenge:	Existing PLMs are infeasible for processing long documents due to computational costs and incomprehensive document understanding.
Approach:	They propose a retrieval model that models local semantics and global context semantics in a tightly-coupled manner.
Outcome:	The proposed model overcomes three core challenges of long document retrieval: substantial computational cost, incomprehensive document understanding, and scarce annotations.

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Shixin Jiang, Zhihao Zhu, Jiafeng Liang, Yang Wu, Ming Liu, Bing Qin

Real-time Ad Retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising (2025.emnlp-main)

Challenge:	Tool-integrated reasoning (TIR) enables large language models to invoke external tools for tasks beyond their internal capacity but often suffers from tool overuse.
Approach:	They propose an algorithm that uses a composite reward to model tool costs and tool efficiency.
Outcome:	The proposed algorithm models heterogeneous tool costs and encourages more cost-effective tool-use strategies.

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Tongtong Liu, Zhaohui Wang, Meiyue Qin, Zenghui Lu, Xudong Chen, Yuekui Yang, Peng Shu

Towards Robust Evidence-Aware Fake News Detection via Improving Semantic Perception (2024.lrec-main)

Challenge:	Existing methods for retrieving documents and ads use one-to-few mappings and time-consuming content extraction.
Approach:	They propose a framework that leverages LLM-generated commercial intents as an intermediate semantic representation to directly retrieve ads for queries in real-time.
Outcome:	The proposed framework has been implemented in a real-world online system, handling daily search volumes in billions.

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Yike Wu, Yang Xiao, Mengting Hu, Mengying Liu, Pengcheng Wang, Mingming Liu

NetSafe: Exploring the Topological Safety of Multi-agent System (2025.findings-acl)

Challenge:	Existing methods lack sufficient semantic perception and are easily blinded by textual expressions.
Approach:	They propose a model-agnostic training framework to improve the semantic perception of evidence-aware fake news detection by combining two kinds of data augmentations with synthetic data.
Outcome:	The proposed framework outperforms state-of-the-art methods on the extended test set while achieving competitive performance on the original one.

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Miao Yu, Shilong Wang, Guibin Zhang, Junyuan Mao, Chenlong Yin, Qijiong Liu, Kun Wang, Qingsong Wen, Yang Wang

Challenge:	Large language models (LLMs) have fueled significant progress in intelligent Multi-agent Systems (MAS), with expanding academic and industrial applications.
Approach:	They propose a framework that unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis.
Outcome:	The proposed framework unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis.

On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference (2026.acl-long)

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Zhengyi Li, Yakai Wang, Jingwen Leng, Kang Yang, Yu Yu, Jiaping Gui, Yu Feng, Ning Liu, Minyi Guo

WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent (2026.findings-acl)

Challenge:	Existing work reveals only randomly permuted activations to the client, allowing adversaries to extract model weights.
Approach:	They propose an attack that aligns differently shuffled activations to a common permutation and exploits them to extract model weights.
Outcome:	The proposed attack can align shuffled activations to a common permutation and exploit them to extract model weights with a query cost of approximately $1.

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Lingfeng Zhang, Yongan Sun, Jinpeng Hu, Hui Ma, Ying Yang, Kuien Liu, Zenglin Shi, Meng Wang

GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)

Challenge:	Existing web agents struggle with complex tasks due to rigid planning strategies and hallucination-prone reasoning.
Approach:	They propose a task-uncertainty-driven Adaptive Planning Mechanism that adaptively selects planning modes to navigate unknown environments.
Outcome:	The proposed framework performs better on the WebArena and WebVoyager benchmarks than existing frameworks.

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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen

Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset Evaluation for Text Classification (2022.naacl-main)

Challenge:	GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages.
Approach:	They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement.
Outcome:	The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3.

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Yang Xiao, Jinlan Fu, See-Kiong Ng, Pengfei Liu

Text Classification

Challenge:	Existing benchmark datasets contribute little to discriminating top-scoring systems, while those less used datasets exhibit impressive discriminative power.
Approach:	They examine the distinguishability of benchmark datasets when comparing different systems . they find that existing benchmark dataset contribute little to discriminating top-scoring systems - whereas those less used datasets exhibit impressive discriminative power.
Outcome:	The proposed datasets are released on DataLab.

Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning (2026.findings-acl)

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Cehao Yang, Xueyuan Lin, Xiaojun Wu, Chengjin Xu, Xuhui Jiang, Honghao Liu, Hui Xiong, Jian Guo

XQuant: Achieving Ultra-Low Bit KV Cache Quantization with Cross-Layer Compression (2025.emnlp-main)

Challenge:	Large reasoning models exhibit human-like behaviors such as exploration, verification, reflection, and correction.
Approach:	They propose a supervised fine-tuning framework for long chain-of-thoughts reasoning . they leverage a difficulty-aware reward model to estimate the learning value of questions .
Outcome:	The proposed framework performs fine-tuning on large reasoning models on 10% of the data selected.

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Haoqi Yang, Yao Yao, Zuchao Li, Baoyuan Qi, Liu Guoming, Hai Zhao

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks. however, their extensive memory requirements present significant challenges for deployment in resource-constrained environments.
Approach:	They propose a training-free framework that achieves ultra-low equivalent bit-width KV cache quantization.
Outcome:	The proposed framework outperforms state-of-the-art methods on TruthfulQA and LongBench.

Trustworthy and Explainable Causal Representation Learning in Transformers (2026.findings-acl)

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Yang Liu, Yinghao Zhang, Lin Liu, Jiuyong Li, Debo Cheng, Zaiwen Feng

P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs (2025.emnlp-main)

Challenge:	Existing approaches to interpretable representation learning rely on masks that weight the significance of input features, but the origin of these masks is uncertain.
Approach:	They propose a causal framework that directly learns identifiable representations from attention weights rather than relying on importance masks.
Outcome:	The proposed framework learns identifiable and explainable representations from attention weights, rather than masks, and guarantees faithfulness on real-world datasets.

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Yidan Zhang, Yu Wan, Boyi Deng, Baosong Yang, Hao-Ran Wei, Fei Huang, Bowen Yu, Dayiheng Liu, Junyang Lin, Fei Huang, Jingren Zhou

EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios (2026.acl-long)

Challenge:	Recent advances in large language models showcase varied multilingual capabilities across tasks . previous assessments focused on fundamental natural language processing (NLP) or isolated capability-specific tasks.
Approach:	They propose a multilingual multitask benchmark to assess multilingual capabilities . they use a large-scale benchmark covering fundamental and capability-specialized datasets .
Outcome:	The proposed benchmark compares models and tasks across languages and tasks and examines knowledge transfer from English to other languages.

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Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang

Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking (2022.findings-acl)

Challenge:	Existing benchmarks that focus on knowledge-intensive tasks do not reflect diverse educational scenarios.
Approach:	They propose a benchmark that incorporates 9 major scenarios and 4,000 educational contexts.
Outcome:	The proposed model performs comparable to state-of-the-art large models on the test set.

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Tianyi Luo, Rui Meng, Xin Wang, Yang Liu

Exploiting Contextual Knowledge in LLMs through 𝒱-usable Information based Layer Enhancement (2025.acl-long)

Challenge:	Existing methods for predicting research replication are insufficient especially for long research papers.
Approach:	They propose to build an interpretable neural model which can provide sentence-level explanations and apply weakly supervised approach to leverage large corpus of unlabeled datasets.
Outcome:	The proposed model can provide sentence-level explanations and leverage large unlabeled datasets to boost interpretability and improve prediction performance.

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Xiaowei Yuan, Zhao Yang, Ziyang Huang, Yequan Wang, Siqi Fan, Yiming Ju, Jun Zhao, Kang Liu

Challenge:	Existing approaches to enhance the context-faithfulness of Large Language Models (LLMs) ignore the fundamental mechanism of how contextual information is processed within LLMs’ internal states.
Approach:	They propose a method that enhances the utilization of contextual knowledge within LLMs’ internal representations by employing V-usable information analysis.
Outcome:	The proposed method improves context-faithfulness generation in Question-Answering tasks, particularly in scenarios involving unknown or conflicting contextual knowledge.

Rethinking Data Mixing from the Perspective of Large Language Models (2026.acl-short)

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Yuanjian Xu, Tianze Sun, Changwei Xu, XinLong Zhao, Jianing Hao, Ran Chen, Yang Liu, Ruijie Xu, Stephen Chen, Guang Zhang

ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues? (2025.emnlp-industry)

Challenge:	Existing methods to mix data with LLMs have relied on domain definitions derived from intuition.
Approach:	They propose a reweighting framework that restructures data scheduling as a graph-constrained optimization problem.
Outcome:	The proposed framework achieves competitive performance on GPT-2 models.

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Haoxin Wang, Xianhan Peng, Huang Cheng, Yizhe Huang, Ming Gong, Chenghan Yang, Yang Liu, Jiang Lin

Challenge:	ECom-Bench is a benchmark framework for evaluating LLM agent with multimodal capabilities in e-commerce customer support domain.
Approach:	They introduce a benchmark framework for evaluating LLM agent with multimodal capabilities in the e-commerce customer support domain.
Outcome:	The proposed benchmark features dynamic user simulation based on persona information from real e-commerce customer interactions and a realistic task dataset derived from authentic ecommerce dialogues.

Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling (D19-1)

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Linqing Liu, Wei Yang, Jinfeng Rao, Raphael Tang, Jimmy Lin

FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation (2025.acl-demo)

Challenge:	Semantic similarity modeling is central to many NLP problems such as question answering.
Approach:	They propose a pairwise word interaction model with syntactic structure priors to explore their effectiveness.
Outcome:	Extensive evaluations on eight benchmark datasets show that incorporating structural information improves over strong baselines.

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Zheqi He, Yesheng Liu, Jing-Shu Zheng, Xuejing Li, Jin-Ge Yao, Bowen Qin, Richeng Xuan, Xi Yang

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)

Challenge:	FlagEvalMM is an evaluation framework designed to assess multimodal models . it is designed to be used for vision-language understanding and generation tasks .
Approach:	They propose an evaluation framework that decouples model inference from evaluation through an independent evaluation service.
Outcome:	The evaluation framework offers accurate and efficient insights into model strengths and limitations.

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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong

Triviality Corrected Endogenous Reward (2026.acl-long)

Challenge:	Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models.
Approach:	They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Outcome:	The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.

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Xinda Wang, Zhengxu Hou, Yangshijie Zhang, null Yanbingren, Jialin Liu, ChenZhuo Zhao, Zhibo Yang, Bin-Bin Yang, Feng Xiao

MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus (2026.findings-acl)

Challenge:	Recent work on unsupervised reinforcement learning for mathematical reasoning using confidence-based endogenous rewards focuses on open-ended text generation, requiring either annotated data or powerful closed-source models.
Approach:	They propose a method that rewards the relative information gain between a specialist and a generalist reference policy, modulated by a probability-dependent correction mechanism.
Outcome:	The proposed model improves on multiple writing benchmarks and model architectures without external supervision and validates generality across different generation tasks.

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Yexing Du, Kaiyuan Liu, Bihe Zhang, Youcheng Pan, Bo Yang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Daojing He, Yang Xiang, Ming Liu, Bing Qin

From Individual Excellence to Collective Sustainability: Seeking Strategic Equilibrium in Proactive Multi-Agent Teams (2026.findings-acl)

Challenge:	Multimodal Large Language Models (MLLMs) have advanced Chinese Classical Studies (CCS) but the audio dimension of CCS remains underexplored due to a lack of high-quality, domain-specific audio corpora.
Approach:	They propose a 119-hour audio corpus comprising 22,000 audio samples to bridge this gap . it encompasses a diverse range of literary genres across six tasks .
Outcome:	The proposed corpus encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering ( SQA), Speech Understanding (SU), and Speech Reasoning (SR).

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Tong Zhang, Yang Wu, Yufei Shi, Rujing Yao, Zhuoren Jiang, Xiaozhong Liu

ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation (2026.findings-acl)

Challenge:	a team of proactive agents suffer from a greedy optimization for immediate task accuracy . a new approach to improve team collaboration is based on the opportunity cost .
Approach:	They propose a game-theoretic proactive multi-agent reinforcement learning framework to solve this imbalance . they use a Positive-Unlabeled scorer to anchor intervention quality under sparse supervision .
Outcome:	The proposed framework maintains high performance while preventing experts from over-developing.

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Ziyi Liu, Bahareh Sarrafzadeh, Pei Zhou, Longqi Yang, Jieyu Zhao, Ashish Sharma

CGF: Constrained Generation Framework for Query Rewriting in Conversational AI (2022.emnlp-industry)

Challenge:	Large Language Models (LLMs) are increasingly integrated into agentic frameworks to assist individual users in completing diverse tasks.
Approach:	They propose a simulation environment with a plug-and-play proactive AI mediator . they use a socio-cognitive evaluation framework to measure consensus changes, intervention latency, mediator effectiveness and intelligence.
Outcome:	The proposed model outperforms a generic baseline in multi-party negotiation scenarios while being 77% faster in response.

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Jie Hao, Yang Liu, Xing Fan, Saurabh Gupta, Saleh Soltan, Rakesh Chada, Pradeep Natarajan, Chenlei Guo, Gokhan Tur

Paraphrasing

Challenge:	Large-scale conversational AI agents such as Alexa, Siri and Google Assistant help millions of users to perform a lot of tasks.
Approach:	They propose a Constrained Generation Framework for query rewriting at global and personalized levels.
Outcome:	The proposed framework significantly boosts the query rewriting performance.

A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators (C18-1)

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Zhihao Fan, Zhongyu Wei, Siyuan Wang, Yang Liu, Xuanjing Huang

Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration (2026.acl-long)

Challenge:	Existing research on visual question generation is focused on training models to fit the annotated data set that makes them indifferent from other language generation tasks.
Approach:	They propose to use two discriminators to enhance the training of a visual question generator to ask natural questions about an image.
Outcome:	The proposed model outperforms state-of-the-art models in terms of automatic and human evaluation metrics.

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Zijun Liu, Zhennan Wan, Peng Li, Ming Yan, Fei Huang, Yang Liu

On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations (2024.findings-acl)

Challenge:	Existing context window extension methods obstruct scaling external knowledge input.
Approach:	They develop a multi-agent framework to overcome two core bottlenecks in existing agent orchestration designs.
Outcome:	The proposed framework overcomes two core bottlenecks and improves inference-time knowledge integration without longer-context training.

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Shiao Meng, Xuming Hu, Aiwei Liu, Fukun Ma, Yawen Yang, Shuang Li, Lijie Wen

Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation (2022.acl-long)

Challenge:	Existing DocRE models which perform well may make more mistakes when merely changing the entity names in the document, hindering the generalization to novel entity names.
Approach:	They propose a pipeline to generate entity-renamed documents by replacing the original entity names with names from Wikidata.
Outcome:	The proposed pipeline generates entity-renamed documents by replacing the original entity names with names from Wikidata.

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Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur

ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation (2025.findings-acl)

Challenge:	Current neural response generation models generate responses directly, omitting unstated implicit knowledge.
Approach:	They propose a generative approach to externalize implicit commonsense knowledge and use it to generate responses.
Outcome:	Empirical results show that TBS models outperform end-to-end RG models on most automatic metrics and generate more informative, specific, and commonsense-following responses.

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Kaiyuan Liu, Youcheng Pan, Yang Xiang, Daojing He, Jing Li, Yexing Du, Tianrun Gao

Transfer Learning for Sequence Generation: from Single-source to Multi-source (2021.acl-long)

Challenge:	Existing benchmarks lack the ability to automatically evaluate from users’ perspective and lack the explainability of the results of LLM agents’ code generation capabilities.
Approach:	They propose a new benchmark for LLM agents' automated evaluation by simulating user interaction.
Outcome:	The proposed benchmark can evaluate the generated projects by user interaction simulation and by code similarity through existing objective indicators.

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Xuancheng Huang, Jingfang Xu, Maosong Sun, Yang Liu

UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization (2023.acl-long)

Challenge:	Recent studies have shown that pretrained models are effective for low-resource downstream tasks.
Approach:	They propose a two-stage finetuning method to transfer pretrained models to MSG tasks by concatenating multiple sources into a single long sequence.
Outcome:	The proposed model outperforms baselines on the WMT17 APE task and multi-source translation task using the WTM14 test set.

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Yulong Chen, Yang Liu, Ruochen Xu, Ziyi Yang, Chenguang Zhu, Michael Zeng, Yue Zhang

Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting (2024.findings-emnlp)

Challenge:	a new benchmark summarization model is being developed to train few-shot summarizers . a large number of summarizing tasks are required to perform well in heterogeneous datasets.
Approach:	They propose a few-shot summarization model pre-trained with multiple summarizing tasks . they propose 'uniSumm' to be prefix-tuned to excel at any few-shot summarisation task .
Outcome:	The proposed model outperforms baseline models under automatic and human evaluations and achieves comparable results in human evaluation.

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Siyi Liu, Yang Li, Jiang Li, Shan Yang, Yunshi Lan

Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior (2020.findings-emnlp)

Challenge:	Existing methods for zero-shot Relation Extraction (RE) lack detailed, context-specific prompts for understanding various sentences and relations.
Approach:	They propose a framework that uses a three-stage diversity approach to prompt LLMs by generating multiple synthetic samples that encapsulate specific relations from scratch.
Outcome:	The proposed framework outperforms existing LLM-based zero-shot RE methods on benchmark datasets and shows that it produces high-quality synthetic data that enhances performance.

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Zi Lin, Jeremiah Liu, Zi Yang, Nan Hua, Dan Roth

FLOP-Efficient Training: Early Stopping Based on Test-Time Compute Awareness (2026.findings-acl)

Challenge:	Spectral-normalized identity priors (SNIP) is a structured pruning approach for a Transformer model.
Approach:	They propose a structured pruning approach which penalizes an entire residual module toward an identity mapping.
Outcome:	The proposed method improves on 5 GLUE benchmark tasks while maintaining comparable performance.

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Hossam Amer, Maryam Dialameh, Hossein Rajabzadeh, Walid Ahmed, Weiwei Zhang, Yang Liu

Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation (2021.findings-emnlp)

Challenge:	Prior work shows that increasing test-time compute (TTC) can improve accuracy of large language models.
Approach:	They propose a TTC-aware training algorithm that jointly selects a checkpoint and a corresponding TTC configuration to minimize training compute without sacrificing accuracy.
Outcome:	The proposed method reduces training compute by 92% while maintaining accuracy.

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Hua Zheng, Lei Li, Damai Dai, Deli Chen, Tianyu Liu, Xu Sun, Yang Liu

Dynamic Transformers Provide a False Sense of Efficiency (2023.acl-long)

Challenge:	Word sense disambiguation (WSD) methods have not explored word-formations in parataxis languages like Chinese.
Approach:	They propose to leverage word-formation knowledge to enhance Chinese WSD by incorporating word-forms into sense disambiguation models.
Outcome:	The proposed model improves on baselines in Chinese word sense disambiguation (WSD) with word-formation knowledge, the results show.

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Yiming Chen, Simin Chen, Zexin Li, Wei Yang, Cong Liu, Robby Tan, Haizhou Li

Synonym-unaware Fast Adversarial Training against Textual Adversarial Attacks (2025.findings-naacl)

Challenge:	Pre-trained language models typically lead to high computational cost during inference.
Approach:	They propose a slowdown attack framework that can reduce inference efficiency by 80% by leveraging existing adversarial attacks targeting model accuracy.
Outcome:	The proposed framework can reduce the efficiency of multi-exit models by 80% on average, validating its effectiveness and generalization ability.

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Yichen Yang, Xin Liu, Kun He

FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis (2024.lrec-main)

Challenge:	Existing adversarial defense methods rely on predetermined linguistic knowledge and assume that attackers’ synonym candidates are known, which is often unrealistic.
Approach:	They propose a Fast Adversarial Training method that leverages single-step perturbation generation and effective perturbation initialization to improve model robustness without requiring synonym awareness.
Outcome:	Experiments show that the proposed method outperforms existing models under character-level and word-level attacks while still maintaining the correct syntax.

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Songhua Yang, Xinke Jiang, Hanjie Zhao, Wenxuan Zeng, Hongde Liu, Yuxiang Jia

Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation (2022.naacl-main)

Challenge:	Existing methods for aspect-based sentiment analysis are limited and integrating with existing techniques is difficult.
Approach:	They propose a framework that utilizes in-context learning as a feature-aware mechanism that facilitates adaptive learning in multi-domain ABSA tasks.
Outcome:	The proposed framework achieves significant performance improvements in multiple domains compared to baselines, increasing F1 by 2.07% on average.

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Dingcheng Li, Zheng Chen, Eunah Cho, Jie Hao, Xiaohu Liu, Fan Xing, Chenlei Guo, Yang Liu

EfficientRAG: Efficient Retriever for Multi-Hop Question Answering (2024.emnlp-main)

Challenge:	Existing work on lifelong learning requires incremental memory space to learn a model . existing work on experience replay or elastic weighted consolidation requires incremental space .
Approach:	They propose a framework that leverages a recall optimization mechanism to memorize parameters of previous tasks via regularization and a domain drift estimation algorithm to compensate the drift between different domains in the embedding space.
Outcome:	The proposed framework outperforms SOTA models on paraphrase and dialog response generation tasks.

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Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

Robust and Scalable Model Editing for Large Language Models (2024.lrec-main)

Challenge:	Existing retrieval-augmented generation methods rely on multiple calls of large language models (LLMs) Large-language models lack knowledge underrepresented in training data and still face hallucinations.
Approach:	They propose an efficient retriever for multi-hop question answering that generates new queries iteratively without the need for LLM calls.
Outcome:	The proposed method surpasses existing methods on three open-domain multi-hop question-answering datasets.

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Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun

Forewarned is Forearmed: Pre-Synthesizing Jailbreak-like Instructions to Enhance LLM Safety Guardrail to Potential Attacks (2025.findings-emnlp)

Challenge:	Existing methods that ignore contextual knowledge fail to reliably fall back to parametric knowledge when presented with irrelevant context.
Approach:	They propose to use contextual knowledge to update and correct LLMs' knowledge by in-context editing instead of retraining.
Outcome:	The proposed method outperforms current state-of-the-art methods by a large margin on a dataset that contains irrelevant questions.

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Sheng Liu, Qiang Sheng, Danding Wang, Yang Li, Guang Yang, Juan Cao

MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction (2023.findings-acl)

Challenge:	Despite advances in improving large language model (LLM) to refuse to answer malicious instructions, LLMs remain vulnerable to jailbreak attacks where attackers generate instructions with distributions differing from safety alignment corpora.
Approach:	They propose a framework that leverages embedding space distribution analysis to generate jailbreak-like instructions.
Outcome:	The proposed framework shows significant decreases in attack success rate on Qwen2.5, Llama3.1, and Llma3.2 without compromising their utility.

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Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu, Dongfang Liu

Learning to Detect Noisy Labels Using Model-Based Features (2022.findings-emnlp)

Challenge:	Existing methods for product attribute value extraction focus on extracting values for a set of known attributes with sufficient training data.
Approach:	They propose a prompt tuning approach to extract attributes from product information using mixed prompts.
Outcome:	The proposed approach improves on two product benchmarks and shows parameter-efficient training and avoids model overfitting.

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Zhihao Wang, Zongyu Lin, Junjie Wen, Xianxin Chen, Peiqi Liu, Guidong Zheng, Yujun Chen, Zhilin Yang

Task-oriented Word Embedding for Text Classification (C18-1)

Challenge:	Existing approaches to reduce label noise rely on heuristics and sample losses.
Approach:	They propose a method that transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features.
Outcome:	Empirically, the proposed approach improves over strong baselines on a wide range of tasks including text classification and speech recognition.

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Qian Liu, Heyan Huang, Yang Gao, Xiaochi Wei, Yuxin Tian, Luyang Liu

A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction (2022.findings-acl)

Challenge:	Existing word embeddings only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features.
Approach:	They propose a task-oriented word embedding method that regularizes the distribution of words to enable a clear classification boundary.
Outcome:	The proposed method outperforms the state-of-the-art methods on a text classification task.

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Yang Liu, Jinpeng Hu, Xiang Wan, Tsung-Hui Chang

Judge and Improve: Towards a Better Reasoning of Knowledge Graphs with Large Language Models (2025.emnlp-main)

Challenge:	Existing approaches to introduce relation information into the model are limited by labeling and data scarcity.
Approach:	They propose a direct addition approach to introduce relation information into a model by concatenating two views of relations and adding them to the original prototype.
Outcome:	The proposed approach improves on the benchmark dataset FewRel 1.0 and shows comparable results to the state-of-the-art.

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Mo Zhiqiang, Yang Hua, Jiahui Li, Yuan Liu, Shawn Wong, Jianmin Huang

Integrating Vectorized Lexical Constraints for Neural Machine Translation (2022.acl-long)

Challenge:	Existing approaches to integrating graph and language models face two key limitations: achieving robust semantic alignment and ensuring interpretability in outputs.
Approach:	They propose a framework to integrate graph and language modalities while enhancing transparency.
Outcome:	Extensive experiments on three benchmark datasets show that the proposed framework surpasses existing methods in efficiency and generates outputs that are significantly more interpretable.

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Shuo Wang, Zhixing Tan, Yang Liu

One for All: Update Parameterized Knowledge Across Multiple Models with Once Edit (2025.acl-long)

Challenge:	Existing studies focus on integrating discrete lexical constraints into neural machine translation models.
Approach:	They propose to integrate constraints into NMT models by integrating them into keys and values . they show that their method outperforms representative baselines on four language pairs .
Outcome:	The proposed method outperforms baselines on four language pairs, showing superiority .

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Weitao Ma, Xiyuan Du, Xiaocheng Feng, Lei Huang, Yichong Huang, Huiyi Zhang, Xiaoliang Yang, Baohang Li, Xiachong Feng, Ting Liu, Bing Qin

CaM-HG: Causal-Enhanced MoE and Hypergraphs Network for Incomplete Multimodal Emotion Recognition in Conversations (2026.findings-acl)

Challenge:	Existing methods for modifying large language models focus on individual models, resulting in errors and hallucinations.
Approach:	They propose an ensemble-based approach that employs a plug-in model as the editing module and a dynamic weight mechanism to enhance its effectiveness.
Outcome:	The proposed approach outperforms existing methods while achieving superior editing efficiency.

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Mingjian Yang, Yong Wang, Peng Liu, Wen Yin

Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference (2025.coling-main)

Challenge:	Existing methods that focus on statistical reconstruction often fail to bridge these gaps, effectively leaving semantic holes.
Approach:	They propose a Causal-Enhanced Mixture-of-Experts and Hypergraph Network to bridge missing features . they use experts to synthesize missing features that are realistic and causally consistent .
Outcome:	The proposed model synthesizes missing features that are realistic and causally consistent . it surpasses benchmarks on IEMOCAP, CMU-MOSI, and CMU MOSEI by 1.43% and 1.25% .

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Lanrui Wang, Jiangnan Li, Chenxu Yang, Zheng Lin, Hongyin Tang, Huan Liu, Yanan Cao, Jingang Wang, Weiping Wang

Community-Aware Assessment of Social Textual Engagement and Resonance: A Human-Centric Perspective on User-Generated Content Evaluation (2026.acl-long)

Challenge:	Recent studies have focused on integrating commonsense knowledge into chatbots to enhance their ability to understand and generate dialogue responses.
Approach:	They propose a framework that integrates commonsense knowledge into chatbots to enable them to elicit more empathetic responses.
Outcome:	The proposed framework enables LLMs to uncover the implicit requirements of the conversation, aiming to elicit more empathetic responses.

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Tianjiao Li, Kai Zhao, Xiang Li, Yang Liu, Huyang Sun

ModSCAN: Measuring Stereotypical Bias in Large Vision-Language Models from Vision and Language Modalities (2024.emnlp-main)

Challenge:	Traditional Video Quality Assessment (VQA) focuses on aesthetic fidelity and technical distortions.
Approach:	They propose a new task that evaluates whether a UGC item has positive community resonance based on multimodal attributes rather than visual quality alone.
Outcome:	The proposed task outperforms state-of-the-art baselines on CASTER-Bench . it provides interpretable and empathetic reasoning paths that align with real community feedback.

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Yukun Jiang, Zheng Li, Xinyue Shen, Yugeng Liu, Michael Backes, Yang Zhang

TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)

Challenge:	Large vision-language models have been widely used but stereotypical biases are unexplored.
Approach:	They propose a framework to SCAN stereotypical bias within large vision-language models . they examine stereotype biases with respect to gender and race in three scenarios .
Outcome:	The proposed framework can reduce stereotypical biases in large vision-language models . the currently popular models show significant stereotype biase .

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Zhewen Tan, Wenhan Yu, Jianfeng Si, Tongxin Liu, Kaiqi Guan, Huiyan Jin, Jiawen Tao, Xiaokun Yuan, Xiangzheng Zhang, Duohe Ma, Tong Yang, Lin Sun

One-Shot Learning as Instruction Data Prospector for Large Language Models (2024.acl-long)

Challenge:	Existing approaches to safety alignment of large language models rely on costly manual annotations or human review.
Approach:	They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation.
Outcome:	The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability.

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Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li

LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models (2025.findings-naacl)

Challenge:	Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality.
Approach:	They propose a method that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets.
Outcome:	Nuggets outperforms existing methods on MT-Bench and Alpaca-Eval benchmarks.

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Kaichen Zhang, Bo Li, Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, Kairui Hu, Shuai Liu, Yuanhan Zhang, Jingkang Yang, Chunyuan Li, Ziwei Liu

OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents (2026.acl-long)

Challenge:	Current large foundational models have demonstrated transformative capabilities, approaching or surpassing human-level performances in many tasks.
Approach:	They propose a unified and standardized multimodal benchmark framework with over 50 tasks and more than 10 models to promote transparent and reproducible evaluations.
Outcome:	The proposed framework has 50 tasks and more than 10 models to promote transparent and reproducible evaluations.

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Bowen Yang, Kaiming Jin, Zhenyu Wu, Zhaoyang Liu, Qiushi Sun, Zehao Li, JingJing Xie, Zhoumianze Liu, Fangzhi Xu, Kanzhi Cheng, Yian Wang, Qingyun Li, Yu Qiao, Zun Wang, Zichen Ding

MACSum: Controllable Summarization with Mixed Attributes (2023.tacl-1)

Challenge:	Vision-Language Models (VLMs) lack visual-aware tutorial retrieval and historical visual context curation and pruning.
Approach:	They propose a framework that integrates an orchestrator and a Reflection-Memory Agent for robust automation.
Outcome:	Experimental results show that OS-Symphony delivers substantial performance gains across model scales.

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Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir Radev, Chenguang Zhu, Michael Zeng, Rui Zhang

Guided by the Plan: Enhancing Faithful Autoregressive Text-to-Audio Generation with Guided Decoding (2026.eacl-long)

Challenge:	Existing work on controllable summarization with mixed attributes lacks designated annotations.
Approach:	They propose a human-annotated summarization benchmark for controllable summarizing with mixed attributes based on news and dialogue sources .
Outcome:	The proposed dataset contains human-annotated summarization datasets with mixed attributes . hard prompt models yield the best performance on most metrics and human evaluations . mixed-attribute control is still challenging for summarizing tasks .

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Juncheng Wang, Zhe Hu, Chao Xu, Siyue Ren, Yuxiang Feng, Yang Liu, Baigui Sun, Shujun Wang

Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation (2022.naacl-main)

Challenge:	Autoregressive (AR) models excel at generating temporally coherent audio by producing tokens sequentially, yet they often falter in faithfully following complex textual prompts.
Approach:	They propose a lightweight auxiliary model trained with a GAE-inspired objective to predict final instruction-following quality from partial generations.
Outcome:	The proposed model achieves 10 points improvement in CLAP score over baseline AR models while maintaining computational parity with best-of-N decoding.

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Guangyi Liu, Zichao Yang, Tianhua Tao, Xiaodan Liang, Junwei Bao, Zhen Li, Xiaodong He, Shuguang Cui, Zhiting Hu

Knowledge-Centric Hallucination Detection (2024.emnlp-main)

Challenge:	Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss.
Approach:	They propose an Edit-Invariant Sequence Loss method which computes the matching loss of a target sequence with all n-grams in the generated sequence.
Outcome:	The proposed method outperforms the common CE loss and strong baselines on a wide range of tasks.

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Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, Zheng Zhang

Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have shown impressive capabilities but a tendency to hallucinate.
Approach:	They propose a framework that introduces claim-triplets to represent claims in LLM responses and evaluates them against a reference.
Outcome:	The proposed framework outperforms prior methods by 18.2 to 27.2 points on a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs.

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ChengYan Wu, Yiqiang Cai, Yang Liu, Pengxu Zhu, Yun Xue, Ziwei Gong, Julia Hirschberg, Bolei Ma

FuseGen: PLM Fusion for Data-generation based Zero-shot Learning (2024.emnlp-main)

Challenge:	Multimodal Emotion Recognition in Conversations (MERC) is a new way to enhance human-computer interaction.
Approach:	This survey offers a systematic overview of Multimodal Emotion Recognition in Conversations . it examines motivations, core tasks, representative methods, and evaluation strategies .
Outcome:	The survey examines the effectiveness of MERC and its evaluation strategies.

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Tianyuan Zou, Yang Liu, Peng Li, Jianqing Zhang, Jingjing Liu, Ya-Qin Zhang

End-to-End Optimization of LLM-Driven Multi-Agent Search Systems via Heterogeneous-Group-Based Reinforcement Learning (2026.acl-long)

Challenge:	Existing approaches to train Small Task-specific Models (STMs) using synthetic datasets are limited by the low quality of such datasets.
Approach:	They propose a data-generation based zero-shot learning framework that uses multiple PLMs to train small task-specific models.
Outcome:	The proposed framework outperforms existing methods in boosting performance across tasks.

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Guanzhong Chen, Shaoxiong Yang, Chao Li, Wei Liu, Jian Luan, Zenglin Xu

Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching (2022.findings-naacl)

Challenge:	Existing multi-agent reinforcement learning methods depend on large critic networks to evaluate joint actions, leading to instability and high memory costs.
Approach:	They propose a method to optimize large language models for agent-specific roles . they propose combining agent-based frameworks with retrieval-augmented generation .
Outcome:	Experiments show that multi-agent group policy optimization outperforms baselines in task performance and computational efficiency.

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Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang, Rong Tian, Zhu Tao, Haoyan Liu, Han Guo, Xingyu Bai, Weiquan Mao, Yudong Li, Weigang Guo, Taiqiang Wu, Ningyuan Sun

VIRT: Improving Representation-based Text Matching via Virtual Interaction (2022.emnlp-main)

Challenge:	Existing studies have shown that cross-lingual knowledge distillation can improve the performance of pre-trained models for cross-linguistic similarity matching tasks.
Approach:	They propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model using contrastive learning, bottleneck, and parameter recurrent strategies.
Outcome:	The proposed model can compress the size of XLM-R and MiniLM by more than 50% while the performance is only reduced by about 1%.

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Dan Li, Yang Yang, Hongyin Tang, Jiahao Liu, Qifan Wang, Jingang Wang, Tong Xu, Wei Wu, Enhong Chen

Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification (2022.coling-1)

Challenge:	Experimental results show that representation-based text matching methods suffer from performance degradation due to the lack of interactions between the pair of texts.
Approach:	They propose a virtual interaction mechanism that enables deep interaction between texts . they propose 'inteRacTion mechanism' that can be integrated into existing methods as plugins .
Outcome:	The proposed method outperforms state-of-the-art models on six text matching benchmarks.

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Wei Huang, Chen Liu, Bo Xiao, Yihua Zhao, Zhaoming Pan, Zhimin Zhang, Xinyun Yang, Guiquan Liu

Text Classification

Challenge:	Existing methods for hierarchical text classification are lacking in the field of natural language processing.
Approach:	They propose a hierarchy-aware T5 model with path-adaptive attention mechanism to exploit hierarchical dependency across different levels.
Outcome:	The proposed model outperforms state-of-the-art models especially in Macro-F1 and low Macro.

Advancing the Robustness of Large Language Models through Self-Denoised Smoothing (2024.naacl-short)

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Jiabao Ji, Bairu Hou, Zhen Zhang, Guanhua Zhang, Wenqi Fan, Qing Li, Yang Zhang, Gaowen Liu, Sijia Liu, Shiyu Chang

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)

Challenge:	Existing adversarial attacks can cause LLMs to make wrong predictions on downstream tasks or generate harmful content misaligned with human values.
Approach:	They propose to use randomized smoothing to add noise to the input and then make predictions based on these denoised versions.
Outcome:	The proposed method surpasses existing methods in both empirical and certified robustness in defending against adversarial perturbations for both downstream tasks and human alignments (i.e., jailbreak attacks).

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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang

Program Enhanced Fact Verification with Verbalization and Graph Attention Network (2020.emnlp-main)

Challenge:	Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases.
Approach:	They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
Outcome:	The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs.

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Xiaoyu Yang, Feng Nie, Yufei Feng, Quan Liu, Zhigang Chen, Xiaodan Zhu

RICO: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction (2025.emnlp-main)

Challenge:	Existing methods for fact verification based on structured data are challenging and require further study.
Approach:	They propose a program-enhanced verbalization and a graph attention network to integrate programs and execution into textual inference models.
Outcome:	The proposed framework achieves a new state-of-the-art accuracy on a benchmark dataset . it is compared with existing frameworks on symbolic and informal inference models .

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Yuchi Wang, Yishuo Cai, Shuhuai Ren, Sihan Yang, Linli Yao, Yuanxin Liu, Yuanxing Zhang, Pengfei Wan, Xu Sun

Pre3: Enabling Deterministic Pushdown Automata for Faster Structured LLM Generation (2025.acl-long)

Challenge:	Existing recaptioning methods suffer from inaccuracies due to missing fine-grained details.
Approach:	They propose a framework that refines captions through visual reconstruction using a text-to-image model and a visual reconstruction framework.
Outcome:	The proposed framework outperforms baselines on CapsBench and CompreCap by 10%.

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Junyi Chen, Shihao Bai, Zaijun Wang, Siyu Wu, Chuheng Du, Hailong Yang, Ruihao Gong, Shengzhong Liu, Fan Wu, Guihai Chen

HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks (2023.findings-acl)

Challenge:	Existing methods for structured generation of outputs are inefficient under large inference batches.
Approach:	They propose a new LLM-based method that parses LR(1) grammars into a pushdown automaton and exploits deterministic pushdown automation to optimize the constrained LLM decoding efficiency.
Outcome:	The proposed method improves time per output token (TPOT) by 40% and throughput by 36% .

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Zhengkun Zhang, Wenya Guo, Xiaojun Meng, Yasheng Wang, Yadao Wang, Xin Jiang, Qun Liu, Zhenglu Yang

ATG: Benchmarking Automated Theorem Generation for Generative Language Models (2024.findings-naacl)

Challenge:	Pretraining and fine-tuning are the dominant paradigms in natural language processing.
Approach:	They propose a parameter-efficient multitask learning framework that takes trainable hyper-embeddings and visual modality as input and outputs weights for different modules in a pretrained language model.
Outcome:	The proposed framework adds fewer trainable parameters in multi-task learning while achieving superior performances and transfer ability compared to state-of-the-art methods.

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Xiaohan Lin, Qingxing Cao, Yinya Huang, Zhicheng Yang, Zhengying Liu, Zhenguo Li, Xiaodan Liang

Grammar-Based Patches Generation for Automated Program Repair (2021.findings-acl)

Challenge:	Existing generative language models (LMs) can generate new or reusable theorems, but their ability to generate new theorels is under-explored.
Approach:	They propose to use Metamath library to generate new theorems that can be saved as reusable knowledge for future theoretical proving.
Outcome:	The proposed benchmark evaluates whether an agent can generate valuable (and possibly brand new) theorems that are applicable for downstream theoretic proving as reusable knowledge.

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Yu Tang, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, Muyun Yang

MemCoRL: Alternating Co-Optimization of Memory Retrieval and Utilization via Collaborative Reinforcement Learning (2026.acl-long)

Challenge:	Automated program repair (APR) aims to find an automatic solution to program language bugs without human intervention.
Approach:	They propose a grammar-based rule-rule model which regards the repair process as the transformation of grammar rules and employs a tree-based self-attention approach to guarantee grammar correctness.
Outcome:	The proposed model outperforms the state-of-the-art models on a Java dataset in terms of generated code accuracy.

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Yuewen Liu, Peng Xu, Muxi Diao, Anyi Zhang, Yang Li, Yutong Zhang

SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains (2025.naacl-long)

Challenge:	Existing research has proposed external memory modules for Large Language Models (LLMs) to overcome the limitations of finite input length and obtain contextual memory beyond the current input.
Approach:	They propose a two-stage alternating co-optimization reinforcement learning method that optimizes evidence retrieval and utilization using semantic feedback and rewards.
Outcome:	The proposed method outperforms baselines on lexical overlap and semantic similarity metrics, confirming the co-optimization in memory retrieval and memory utilization.

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Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He

Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks (2021.emnlp-main)

Challenge:	Retrieval-augmented generation (RAG) enhances the question answering abilities of large language models (LLMs) however, adapting general-purpose RAG systems to specialized fields poses unique challenges due to distribution shifts and limited access to domain-specific data.
Approach:	They propose a method that equips large language models with joint capabilities of question answering and question generation for domain adaptation.
Outcome:	Experiments on 11 datasets across three different domains verify the efficacy of SimRAG over baselines by 1.2%–8.6%.

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Qingbin Liu, Pengfei Cao, Cao Liu, Jiansong Chen, Xunliang Cai, Fan Yang, Shizhu He, Kang Liu, Jun Zhao

Part Represents Whole: Improving the Evaluation of Machine Translation System Using Entropy Enhanced Metrics (2022.findings-aacl)

Challenge:	Existing offline DST models require a fixed dataset to train . Existing domain-lifelong learning methods are impractical in real-world applications .
Approach:	They propose a domain-lifelong learning method to continuously train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains.
Outcome:	The proposed method outperforms state-of-the-art lifelong learning methods by 4.25% and 8.27% on the MultiWOZ and the SGD benchmarks.

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Yilun Liu, Shimin Tao, Chang Su, Min Zhang, Yanqing Zhao, Hao Yang

TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild (2024.findings-acl)

Challenge:	Existing machine translation metrics have poor correlations with human assessments . entropy-based evaluations are often limited to a limited number of samples .
Approach:	They propose a fast and unsupervised approach to enhance machine translation metrics using entropy by introducing sentence-level difficulty.
Outcome:	The proposed method outperforms existing metrics on five sub-tracks in the WMT19 Metrics shared tasks.

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Huayang Li, Siheng Li, Deng Cai, Longyue Wang, Lemao Liu, Taro Watanabe, Yujiu Yang, Shuming Shi

ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs’ Capability via Chart Editing (2025.findings-acl)

Challenge:	Large language models with instruction-following capabilities have revolutionized the field of artificial intelligence.
Approach:	They propose an annotation-free framework for empowering large language models with instruction-following capabilities.
Outcome:	The proposed framework generates multi-turn multimodal instruction-response conversations from a language model.

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Xuanle Zhao, Xuexin Liu, Yang Haoyue, Xianzhen Luo, Fanhu Zeng, Jianling Li, Qi Shi, Chi Chen

CODIS: Benchmarking Context-dependent Visual Comprehension for Multimodal Large Language Models (2024.acl-long)

Challenge:	Existing evaluations of multimodal large language models rely on limited case studies . however, they lack the ability to generate accurate edits according to the instructions .
Approach:	They propose a benchmark for chart editing that includes 1,405 edit instructions applied to 233 real-world charts.
Outcome:	The proposed benchmark includes 1,405 diverse editing instructions applied to 233 real-world charts.

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Fuwen Luo, Chi Chen, Zihao Wan, Zhaolu Kang, Qidong Yan, Yingjie Li, Xiaolong Wang, Siyu Wang, Ziyue Wang, Xiaoyue Mi, Peng Li, Ning Ma, Maosong Sun, Yang Liu

QuoteR: A Benchmark of Quote Recommendation for Writing (2022.acl-long)

Challenge:	Multimodal large language models have demonstrated promising results in a variety of tasks that combine vision and language.
Approach:	They propose a benchmark to assess the ability of models to use contextual information in free-form text to enhance visual comprehension.
Outcome:	The proposed model fails to extract and utilize contextual information to improve understanding of images.

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Fanchao Qi, Yanhui Yang, Jing Yi, Zhili Cheng, Zhiyuan Liu, Maosong Sun

Challenge:	Existing methods to recommend quotes are evaluated on unpublished datasets .
Approach:	They propose to build a dataset that is open and contains three parts including English, standard Chinese and classical Chinese.
Outcome:	The proposed model outperforms existing methods on all three parts of QuoteR.

Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)

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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni

Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error (2026.acl-long)

Challenge:	MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture.
Approach:	They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content.
Outcome:	The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context.

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Chenming Tang, Hsiu-Yuan Huang, Weijie Liu, Clive Bai, Saiyong Yang, Yunfang Wu

Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation (2022.findings-naacl)

Challenge:	Existing approaches to RLVR train LMs based on their own on-policy responses and are constrained by the initial capability of LM.
Approach:	They propose an approach that hints LMs with their self-made mistakes without external guidance.
Outcome:	The proposed approach outperforms the normal group relative policy optimization and requires no external guidance.

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Junwei Yang, Zequn Liu, Ming Zhang, Sheng Wang

Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020.coling-main)

Challenge:	Neural text generation is a novel technique to describe biomedical pathways without manually curation.
Approach:	They propose a new dataset Pathway2Text which contains 2,367 pairs of biomedical pathways and textual descriptions.
Outcome:	The proposed method improves on both Graph2Text and Text2Graph tasks and can be used as a benchmark for biomedical named entity recognition.

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Jian Wang, Junhao Liu, Wei Bi, Xiaojiang Liu, Kejing He, Ruifeng Xu, Min Yang

RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance (2024.emnlp-industry)

Challenge:	Existing task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation.
Approach:	They propose a dual dynamic memory network for multi-turn dialog generation . the model dynamically expands the dialog memory turn by turn and keeps track of dialog history .
Outcome:	The proposed model outperforms baseline models on three benchmark datasets on human evaluation and automatic evaluation.

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Tianyang Zhang, Zhuoxuan Jiang, Shengguang Bai, Tianrui Zhang, Lin Lin, Yang Liu, Jiawei Ren

Learning with Noisy Labels for Sentence-level Sentiment Classification (D19-1)

Challenge:	Large Language Models (LLMs) have improved the open-domain QA’s performance, but how to efficiently handle enterprise-exclusive corpora and build domain-specific QA systems are still not studied for industrial applications.
Approach:	They propose a general and comprehensive framework based on Retrieval Augmented Generation (RAG) and facilitate the whole business process of establishing QA systems for IT operations and maintenance.
Outcome:	The proposed framework achieves superior results on two kinds of QA tasks.

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Hao Wang, Bing Liu, Chaozhuo Li, Yan Yang, Tianrui Li

Error Analysis of Uyghur Name Tagging: Language-specific Techniques and Remaining Challenges (L18-1)

Challenge:	Existing research on learning with noisy labels dates back to the 1980s, but it is still vibrant today.
Approach:	They propose a novel DNN model called NetAb to deal with noisy labels during training and train the networks using their respective loss functions in mutual reinforcement.
Outcome:	The proposed model can fit training data with noisy labels and predict clean labels.

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Halidanmu Abudukelimu, Abudoukelimu Abulizi, Boliang Zhang, Xiaoman Pan, Di Lu, Heng Ji, Yang Liu

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents (2026.acl-long)

Challenge:	despite efforts at name tagging, there is limited understanding on the performance ceiling . despite the high-resource language, there are very few natural language processing tools available .
Approach:	They propose to use a machine learning model to identify Uyghur name tagger errors . they conclude that such a model is unlikely to be effective for Uygur, or low-resource languages .
Outcome:	The proposed model is unlikely to be effective for Uyghur, or low-resource languages in general, the authors argue . they show that the proposed model can be used for high-res languages with superficial features .

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Lei Ding, Bin He, Chenguang Wang, Yang Liu

MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing (2026.acl-demo)

Challenge:	Existing approaches to measuring and optimizing proactive task-oriented agents lack generalizable end-to-end solutions.
Approach:	They propose a framework for conversational task scheduling that integrates proactiveness reinforcement learning with a domain-agnostic annotation methodology.
Outcome:	The proposed framework enables scalable proactiveness reinforcement learning (RL) Experiments on two newly auto-annotated datasets demonstrate significant improvements in proactive timing while maintaining action consistency comparable to state-of-the-art baselines.

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Yang Liu, Jinxuan Cai, Yishen Li, Qi Meng, Zedi Liu, Xin Li, Chen Qian, Chuan Shi, Cheng Yang

HyperCRS: Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (2025.findings-acl)

Challenge:	Large language model-based multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration.
Approach:	They propose a graph-centric framework for orchestrating large language model-based multi-agent systems . they compile a user's natural-language intent into an editable workflow specification and then into an executable graph .
Outcome:	The proposed framework compiles natural-language intent into an executable graph and then compile and executes it at runtime.

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Yongsen Zheng, Mingjie Qian, Guohua Wang, Yang Liu, Ziliang Chen, Mingzhi Mao, Liang Lin, Kwok-Yan Lam

Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models (2025.coling-main)

Challenge:	Existing methods to analyze filter bubbles in the static recommendation environment are unable to burst them during user interactions.
Approach:	They propose a paradigm to learn multi-grained user preferences during dynamic user-system interactions via natural language conversations to burst filter bubbles.
Outcome:	The proposed paradigm achieves state-of-the-art performance and the superior of bursting filter bubbles in the conversational recommendation system.

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Yile Wang, Sijie Cheng, Zixin Sun, Peng Li, Yang Liu

IPS: In-Prompt Process Supervision for Short Video Content Moderation (2026.acl-industry)

Challenge:	Symbols are used in abstract reasoning, chemical property prediction, and tabular question-answering.
Approach:	They propose a method that converts symbols to language-based representations to improve their accuracy.
Outcome:	The proposed method improves the accuracy of symbols in language-based models.

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Mingchao Liu, Yu Sun, Ruixiao Sun, Xin Dong, Xiang Shen, Hongwei Wang, Hongyu Xiong, Yang Song

LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL (2025.emnlp-main)

Challenge:	Multimodal large language models (MLLMs) capture semantics of short video content but fail to account for policy-specific details.
Approach:	They propose a framework that integrates In-prompt Process Supervision into MLLMs . they propose sequential reasoning over ancillary questions during fine-tuning .
Outcome:	IPS outperforms baseline MLLMs on public and proprietary benchmarks . replacing human-annotated ancillary labels with MLML-generated ones results in performance degradation.

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Yihan Wang, Peiyu Liu, Xin Yang

UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents (2026.acl-long)

Challenge:	Existing Text-to-SQL models fail to address schema linking problems in large-scale multi-database environments.
Approach:	They propose a framework that aims to enable non-expert users to retrieve data effortlessly . they highlight four core errors leading to schema linking failures .
Outcome:	The proposed framework outperforms baselines on all schema linking metrics.

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Yifan Ji, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Qian Zhang, Zhibo Yang, Junyang Lin, Yu Gu, Ge Yu, Maosong Sun

Multi-Scale Spectral Selection and Entropy-Guided Uncertainty Fusion for Multimodal Rumor Detection (2026.findings-acl)

Challenge:	Recent Large Multimodal Models (LMMs) have shown promising potential for performing end-to-end KIE directly from document images.
Approach:	They propose a benchmark to evaluate the performance of Large Multimodal Models (LMMs) using a constrained-category KIE track and an open-categorical KIE Track.
Outcome:	Experiments on 15 state-of-the-art LMMs show performance degradation under diverse schema definitions, long-tail key fields, and complex layouts, along with pronounced performance disparities across different document types and scenarios.

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Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Fan Li, Dong Yu, Yan Song, null Zhangfengzhen

LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation (2022.emnlp-main)

Challenge:	Existing methods for multimodal content detection fail to capture cross-modal semantic inconsistencies and ignore inherent noise in multimodal features.
Approach:	They propose a multimodal rumor detection method based on a frequency domain spectral selection method and entropy-guided uncertainty fusion method to capture cross-modal semantic inconsistencies.
Outcome:	The proposed method outperforms state-of-the-art methods in multimodal rumor detection . it shows stronger detection capability and robustness on multiple datasets .

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Hongcheng Guo, Jiaheng Liu, Haoyang Huang, Jian Yang, Zhoujun Li, Dongdong Zhang, Zheng Cui

TP-Link: Fine-grained Pre-Training for Text-to-SQL Parsing with Linking Information (2024.lrec-main)

Challenge:	Recent advances struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases in the real world.
Approach:	They propose to train different MMT models to support translations between different languages.
Outcome:	The proposed model is able to handle the above issues by providing a shared semantic space for multiple languages.

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Ziqiang Liu, Shujie Li, Zefeng Cai, Xiangyu Li, Yunshui Li, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Existing pre-training frameworks for text-to-SQL parsing have shown inherent differences in distributions between tables and plain text.
Approach:	They propose a framework to improve context-dependent Text-to-SQL parsing by leveraging Linking information.
Outcome:	The proposed framework achieves state-of-the-art performance on two leading downstream benchmarks.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Not All Citations Are Equal:Entropy-Guided Citation Selection for Noise-Resistant Medical LLM (2026.findings-acl)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Minyu Gao, Hanlin Xiao, Ruoyu Wang, Shuai Yang, YeXuan Zhang, Xin Wu, Xingyu Liu

Semantically Comprehensive Token Pruning in LVLMs via Maximizing Concept Coverage (2026.acl-long)

Challenge:	Large language models have demonstrated extensive potential in medical applications . however, their practical deployment in healthcare faces significant challenges .
Approach:	They propose a training-free multi-turn reasoning framework and a post-training methodology that provides external knowledge support for large language models.
Outcome:	The proposed framework elicits internal thought, external thought, and fusion thought, with an entropy-based reward that encourages selective citation of beneficial external knowledge while penalizing noisy citations.

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Xueting Li, Qi Liu, Chenghao Xu, Xu Yang, Guangtao Lyu, Jiahua Li, Cheng Deng

Nash-Pruned CredMAS: Dynamic Panel Pruning for VLM-MAS using Nash-based Selection and Doubly-Robust Credits (2026.findings-acl)

Challenge:	Existing visual token pruning methods leverage simple metrics derived from human experience, such as attention or similarity, to rank and select tokens within a highly entangled feature space.
Approach:	They propose a novel visual token pruning method that uses a concept-driven paradigm to quantify the Marginal Semantic Gain of each token's contribution to uncovered concepts.
Outcome:	The proposed method outperforms state-of-the-art methods in a concept-driven model while maintaining semantic completeness.

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Yijia Fan, Mingyu Liu, Jing Yang, Jian Wang, Keze Wang, Jusheng Zhang

Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data (2021.emnlp-main)

Challenge:	Multi-Agent Systems (MAS) are expensive due to static panel designs, where all N agents communicate at every T round.
Approach:	They propose an economic framework that transforms agent selection into a dynamic resource allocation game.
Outcome:	The proposed system reduces token consumption by over 25% on challenging benchmarks while reducing token consumption.

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Erguang Yang, Mingtong Liu, Deyi Xiong, Yujie Zhang, Yao Meng, Changjian Hu, Jinan Xu, Yufeng Chen

Continual Knowledge Distillation for Neural Machine Translation (2023.acl-long)

Challenge:	Existing studies on syntactically controlled paraphrase generation rely on large-scale parallel data.
Approach:	They propose a syntactically-informed unsupervised paraphrasing model based on conditional variational auto-encoder which can generate texts in a specified syntastic structure.
Outcome:	The proposed model can generate diverse paraphrases with specified syntactic structure using non-parallel data.

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Yuanchi Zhang, Peng Li, Maosong Sun, Yang Liu

Pluggable Neural Machine Translation Models via Memory-augmented Adapters (2024.lrec-main)

Challenge:	Current parallel corpora are not publicly accessible but trained models are more readily available.
Approach:	They propose a method to take advantage of existing translation models to improve one model of interest.
Outcome:	The proposed method improves on Chinese-English and German-English datasets and is robust to malicious models.

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Yuzhuang Xu, Shuo Wang, Peng Li, Xuebo Liu, Xiaolong Wang, Weidong Liu, Yang Liu

Physics: Benchmarking Foundation Models on University-Level Physics Problem Solving (2025.findings-acl)

Challenge:	Recent years, neural machine translation systems are often developed with large-scale parallel data extracted from the Web.
Approach:	They propose a memory-augmented adapter to steer pretrained neural machine translation models in a pluggable manner by combining model representations and retrieved results.
Outcome:	The proposed method outperforms several representative pluggable baselines on style- and domain-specific experiments.

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Kaiyue Feng, Yilun Zhao, Yixin Liu, Tianyu Yang, Chen Zhao, John Sous, Arman Cohan

Challenge:	a benchmark for university-level physics problem solving contains 1,297 expert-annotated problems . a proprietary model, o3-mini, achieves only 59.9% accuracy, highlighting fundamental weaknesses in scientific reasoning, conceptual understanding, and mathematical precision.
Approach:	They introduce Physics, a benchmark for university-level physics problem solving.
Outcome:	The proposed model achieves only 59.9% accuracy on the most advanced model, o3-mini . the proposed model is a powerful tool for evaluating models on advanced problems .

DYNTEXT: Semantic-Aware Dynamic Text Sanitization for Privacy-Preserving LLM Inference (2025.findings-acl)

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Juhua Zhang, Zhiliang Tian, Minghang Zhu, Yiping Song, Taishu Sheng, Siyi Yang, Qiunan Du, Xinwang Liu, Minlie Huang, Dongsheng Li

A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)

Challenge:	Existing methods to protect privacy of sensitive data are differential privacy (DP) and DP is used to protect users from privacy leakage.
Approach:	They propose an LDP-based Dynamic Text sanitization for privacy-preserving LLM inference that dynamically constructs semantic-aware adjacency lists of sensitive tokens to sample non-sensitive tokens for perturbation.
Outcome:	The proposed model excels on three datasets.

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Qiyao Peng, Hongtao Liu, Hua Huang, Jian Yang, Qing Yang, Minglai Shao

Enhancing Learning-Based Binary Code Similarity Detection Model through Adversarial Training with Multiple Function Variants (2024.findings-emnlp)

Challenge:	Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation.
Approach:	They present a comprehensive synthesis of large language models and their applications . they dissect a four-module agent architecture and review representative designs .
Outcome:	The proposed models address fundamental challenges in traditional recommender systems . they include limited comprehension of complex user intents, insufficient interaction capabilities .

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Lichen Jia, Chenggang Wu, Bowen Tang, Peihua Zhang, Zihan Jiang, Yang Yang, Ning Liu, Jingfeng Zhang, Zhe Wang

Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)

Challenge:	Existing Learning-Based Binary Code Similarity Detection (LB-BCSD) methods exhibit lower accuracy in recognizing functions with the same functionality but different implementations.
Approach:	They propose a gradient-guided adversarial attack method based on critical code called FuncFooler which perturbs critical code to generate multiple variants of the same function.
Outcome:	The proposed method increases the accuracy of the current Learning-Based Binary Code Similarity Detection (LB-BCSD) model by 5%-7%.

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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu

AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension (2024.acl-long)

Challenge:	Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes.
Approach:	They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence.
Outcome:	The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show.

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Qian Yang, Jin Xu, Wenrui Liu, Yunfei Chu, Ziyue Jiang, Xiaohuan Zhou, Yichong Leng, Yuanjun Lv, Zhou Zhao, Chang Zhou, Jingren Zhou

DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation (2023.acl-long)

Challenge:	Existing benchmarks for audio-centric interaction have impeded advancements in this field . AIR-Bench evaluates LALMs' ability to understand audio signals and interact with humans .
Approach:	They propose a benchmark to evaluate the ability of large audio-language models to understand audio signals . they use 19 tasks with approximately 19k single-choice questions to examine single-task ability .
Outcome:	The proposed framework evaluates the ability of large audio-language models to understand audio signals and interact with humans in the textual format.

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Menglong Lu, Zhen Huang, Yunxiang Zhao, Zhiliang Tian, Yang Liu, Dongsheng Li

UR2 : Unify RAG and Reasoning through Reinforcement Learning (2026.acl-long)

Challenge:	Existing approaches to domain adaptation only use reliable pseudo instances, i.e., pseudo instances with high prediction confidence, to retrain the model.
Approach:	They propose a domain adversarial learning enhanced self-training framework that uses meta-learning to estimate the importance of each pseudo instance and a meta constructor to construct the meta-validation set.
Outcome:	The proposed framework reduces label noise and preserves hard examples while maintaining accuracy.

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Weitao Li, Boran Xiang, Xiaolong Wang, Jingyi Ren, Ante Wang, Zhinan Gou, Weizhi Ma, Yang Liu

Toward Optimal LLM Alignments Using Two-Player Games (2025.findings-emnlp)

Challenge:	Existing attempts to unify large language models are limited to open-domain QA with fixed retrieval settings.
Approach:	They propose a general reinforcement learning framework that dynamically coordinates retrieval and reasoning.
Outcome:	The proposed framework outperforms existing paradigms on open-domain QA, MMLU-Pro, medical, and mathematical reasoning tasks.

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Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang, Yuanshun Yao, Xiaojun Xu, Zhaoran Wang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Yang Liu, Hang Li

EARA: Improving Biomedical Semantic Textual Similarity with Entity-Aligned Attention and Retrieval Augmentation (2023.findings-emnlp)

Challenge:	Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values.
Approach:	They propose an alignment method based on a two-agent game consisting of an adversarial agent and a defensive agent.
Outcome:	The proposed method improves on a two-agent game with an adversarial agent and a defensive agent.

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Ying Xiong, Xin Yang, Linjing Liu, Ka-Chun Wong, Qingcai Chen, Yang Xiang, Buzhou Tang

Reconstructing Event Regions for Event Extraction via Graph Attention Networks (2020.aacl-main)

Challenge:	Existing methods to measure semantic similarity between biomedical texts are inefficient due to too many biomedically-related entities.
Approach:	They propose an entity-aligned, attention-based and retrieval-augmented PLM that aligns the same type of fine-grained entity information in each sentence pair with an entity alignment matrix with an auxiliary loss.
Outcome:	The proposed model can achieve state-of-the-art on both in-domain and out-of domain datasets.

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Pei Chen, Hang Yang, Kang Liu, Ruihong Huang, Yubo Chen, Taifeng Wang, Jun Zhao

Text Anomaly Detection with Simplified Isolation Kernel (2025.findings-emnlp)

Challenge:	Existing approaches for event extraction focus on sentence-level event extraction, but they lack a broader view of the document context.
Approach:	They build graphs with candidate event filler extractions enriched by sentential embeddings as nodes and use graph attention networks to identify event regions in a document and aggregate event information.
Outcome:	The proposed method performs well on two languages and shows that it is faster than previous methods.

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Yang Cao, Sikun Yang, Yujiu Yang, Lianyong Qi, Ming Liu

Multi-Docker-Eval: A ‘Shovel of the Gold Rush’ Benchmark on Automatic Environment Building for Software Engineering (2026.findings-acl)

Challenge:	High-dimensional dense embeddings extracted by large language models pose memory requirements and high computation time.
Approach:	They propose a method that maps high-dimensional dense embeddings to lower-dimensional sparse representations while preserving crucial anomaly characteristics.
Outcome:	The proposed method achieves better detection performance than 11 SOTA anomaly detection algorithms while maintaining computational efficiency and low memory cost.

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Kelin Fu, Tianyu Liu, Zeyu Shang, Yingwei MA, Jiaheng Liu, Jian Yang, Kaigui Bian

Spectral Insights into Data-Oblivious Critical Layers in Large Language Models (2025.findings-acl)

Challenge:	Automated environment configuration is a critical bottleneck in scaling software engineering (SWE) automation.
Approach:	They propose a reliable evaluation standard for automated environment configuration for 40 real-world repositories spanning 9 programming languages.
Outcome:	The proposed benchmark includes 40 real-world repositories spanning 9 programming languages and measures success in achieving executable states and efficiency under realistic constraints.

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Xuyuan Liu, Lei Hsiung, Yaoqing Yang, Yujun Yan

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training (2026.findings-acl)

Challenge:	Recent studies have identified critical layers linked to specific functions or behaviors, limiting their use to post-hoc settings.
Approach:	They propose a data-oblivious approach to identify intrinsic critical layers in pre-fine-tuned LLMs by analyzing representation dynamics via Centered Kernel Alignment.
Outcome:	The proposed approach identifies critical layers in pre-fine-tuned models . layers with significant shifts in representation space are also those most affected during fine-tuning .

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Rui Zhang, Hongwei Li, Yun Shen, Xinyue Shen, Wenbo Jiang, Guowen Xu, Yang Liu, Michael Backes, Yang Zhang

Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition (2026.findings-acl)

Challenge:	Misaligned large language models can magnify harm by exploiting them to undermine safety . et al., 2022b; Bai e.t., 2023): misalignment, realignment and model-specific resistance are important .
Approach:	They evaluate four methods to identify a mechanism asymmetry between attack and defense . they find that ORPO is most effective for misalignment, but DPO excels in realignment .
Outcome:	The proposed methods show a mechanism asymmetry between attack and defense . the proposed methods excel in realignment, but at the expense of model utility .

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Rujing Yao, Yufei Shi, Yang Wu, Ang Li, Zhuoren Jiang, XiaoFeng Wang, Haixu Tang, Xiaozhong Liu

PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning (2025.emnlp-industry)

Challenge:	Cloud-hosted Large Language Models (LLMs) offer unmatched reasoning capabilities and dynamic knowledge, yet submitting raw queries to these services can expose sensitive user intent.
Approach:	They propose a framework that formulates the trade-off between knowledge utility and privacy as a strategic game.
Outcome:	The proposed framework reduces intent leakage while maintaining high-fidelity answer quality.

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Mohammad Kachuee, Teja Gollapudi, Minseok Kim, Yin Huang, Kai Sun, Xiao Yang, Jiaqi Wang, Nirav Shah, Yue Liu, Aaron Colak, Anuj Kumar, Wen-tau Yih, Xin Luna Dong

Observations and Remedies for Large Language Model Bias in Self-Consuming Performative Loop (2026.acl-long)

Challenge:	Existing methods to improve factuality of large language models (LLMs) rely on human-engineered instructions.
Approach:	They propose a retrieval-augmented generation framework that trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages and instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without extensive human engineered instructions.
Outcome:	The proposed framework outperforms state-of-the-art solutions across 12 open-book RAG QA benchmarks and is being deployed in production.

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Yaxuan Wang, Zhongteng Cai, Yujia Bao, Xueru Zhang, Yang Liu

Streaming Hallucination Detection in Long Chain-of-Thought Reasoning (2026.findings-acl)

Challenge:	Existing synthetic training loops for large language models cause performance drops and induce emerging biases . a large amount of generated content is posted to coding platforms, social media platforms and other platforms on the internet .
Approach:	They propose a self-consuming retraining loop where models are trained on their own outputs . they use a control loop to isolate and analyze feedback-driven bias evolution .
Outcome:	The proposed model increases preference bias and decreases disparate bias.

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Haolang Lu, Minghui Pan, Ripeng LI, Guoshun Nan, Jialin Zhuang, Zijie Zhao, Zhongxiang Sun, Kun Wang, Yang Liu

OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)

Challenge:	Long chain-of-thought reasoning improves performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps.
Approach:	They propose to treat step-level hallucination judgments as local observations and introduce a cumulative prefix-level signal that tracks the global evolution of the reasoning state over the entire trajectory.
Outcome:	The proposed method enables streaming hallucination detection in long CoT reasoning, providing real-time, interpretable evidence.

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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu

Revisiting Model Interpolation for Efficient Reasoning (2026.acl-long)

Challenge:	Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024).
Approach:	They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers.
Outcome:	OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge.

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Taiqiang Wu, Runming Yang, Tao Liu, Jiahao Wang, Ngai Wong

SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)

Challenge:	Existing models that interpolate weights of two specialized models can be abused for efficient reasoning.
Approach:	They propose to merge two specialized models and create a model that combines efficiency and efficiency.
Outcome:	The proposed method outperforms existing models on efficiency and effectiveness.

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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin

EmoRes: Toward Adaptive Psychological Support via User-Agnostic Benchmark and Topic-Mining Agent (2026.findings-acl)

Challenge:	Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation.
Approach:	They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model.
Outcome:	The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks.

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Zhengwei Zou, Xuanming Jiang, Baoyi An, Dingyu Nie, Zhengxing Fang, Qingyu Liu, Xueming Qian, Guoshuai Zhao, Zhongyu Yang

Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation (2023.acl-long)

Challenge:	Large language models generate fragmented and emotionally inconsistent dialogues lacking the therapeutic structure necessary for reliable assessment.
Approach:	They propose a framework that boosts psychological reasoning via a Topic-Mining Emotional Agent and a multi-perspective Self-Reflection Agent.
Outcome:	The proposed framework improves topic continuity, emotional coherence, and clinical interpretability over baselines and validated by ablation studies and human evaluations.

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Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Mingfeng Xue, Boxing Chen, Jun Xie

Latent Part-of-Speech Sequences for Neural Machine Translation (D19-1)

Challenge:	Existing work focuses on generating sentences satisfying pre-specified attributes such as topic and sentiment, yet suffers from increases in storage and inference time.
Approach:	They propose a method that uses a pre-trained continuous vector to generate a fixed pre-trainable language model to satisfy a specified attribute.
Outcome:	The proposed model can achieve improvements on eleven attribute-specific generation tasks with 0.08% extra training parameters.

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Xuewen Yang, Yingru Liu, Dongliang Xie, Xin Wang, Niranjan Balasubramanian

Causal-Debias: Unifying Debiasing in Pretrained Language Models and Fine-tuning via Causal Invariant Learning (2023.acl-long)

Challenge:	Existing methods for learning target side syntactic structure are greedy and only allow them to explore a limited portion of the latent space.
Approach:	They propose a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics while allowing for effective inference over the latent space.
Outcome:	The proposed model captures the co-dependence between syntax and semantics while allowing for efficient inference over the latent space.

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Fan Zhou, Yuzhou Mao, Liu Yu, Yi Yang, Ting Zhong

MUCAR: Benchmarking Multilingual Cross-Modal Ambiguity Resolution for Multimodal Large Language Models (2025.emnlp-main)

Challenge:	Existing methods to remove unwanted stereotypical associations from pretrained language models (PLMs) are often focused on removing unwanted stereotypes from PLMs.
Approach:	They propose a framework to remove unwanted stereotypical associations in pretrained language models . they propose bias-relevant factors are causal, while labelrelevant factors causal .
Outcome:	The proposed framework reduces stereotypical associations after PLMs are fine-tuned . the proposed framework mitigates bias from a causal invariant perspective .

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Xiaolong Wang, Zhaolu Kang, Wangyuxuan Zhai, Xinyue Lou, Yunghwei Lai, Ziyue Wang, Yawen Wang, Kaiyu Huang, Yile Wang, Peng Li, Yang Liu

Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)

Challenge:	Existing multimodal benchmarks overlook linguistic and visual ambiguities, authors say . ambiguity resolution between modalities is lacking in multimodal large language models .
Approach:	They propose a benchmark to evaluate multimodal ambiguity resolution across multilingual and cross-modal scenarios.
Outcome:	a new benchmark evaluates multimodal ambiguity resolution across multilingual and cross-modal scenarios . the benchmark shows that MLLMs can resolve ambiguities in image-text alignment . however, existing benchmarks often overlook linguistic and visual ambiguties .

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton

InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)

Challenge:	Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options.
Approach:	They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations .
Outcome:	The proposed model outperforms human experts in multiple medical tasks.

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Junyou Su, He Zhu, Xiao Luo, Liyu Zhang, Hong-Yu Zhou, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

Minimal Distillation Schedule for Extreme Language Model Compression (2024.findings-eacl)

Challenge:	Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks .
Approach:	They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking.
Outcome:	Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data.

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Chen Zhang, Yang Yang, Qifan Wang, Jiahao Liu, Jingang Wang, Wei Wu, Dawei Song

Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter (2025.findings-emnlp)

Challenge:	Existing methods for teacher assistant-based distillation require multiple trials to find the optimal teacher assistant.
Approach:	They propose a method that allows scheduling of an optimal teacher assistant in just one trial . they show that student performance is positively correlated with the scale-performance tradeoff .
Outcome:	The proposed method can select the optimal teacher assistant in just one trial . it can be used to compare performance of student and teacher assistants on GLUE benchmarks.

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Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu

Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning (2026.acl-long)

Challenge:	Existing supervised fine-tuning (SFT) fails to address these issues, as it trains models on single gold-standard responses without modeling nuanced strategy trade-offs.
Approach:	They propose a two-stage framework that optimizes strategy selection preferences at each dialogue turn.
Outcome:	The proposed framework improves strategy selection preferences at each dialogue turn.

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Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng, Jing Huo, Yang Gao

Lifting the Curse of Capacity Gap in Distilling Language Models (2023.acl-long)

Challenge:	Existing work on large reasoning models (LRMs) focuses on using reinforcement learning (RL) to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query.
Approach:	They propose to use reinforcement learning to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query.
Outcome:	The proposed model reduces token usage by around 50%$ compared to DeepSeek-R1-Distill-Qwen-1.5B/7B and DeepScaleR-1.5b, while significantly improving accuracy.

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Chen Zhang, Yang Yang, Jiahao Liu, Jingang Wang, Yunsen Xian, Benyou Wang, Dawei Song

Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL (2021.acl-long)

Challenge:	Existing studies have shown that pretrained language models require a tremendous amount of inference compute to perform.
Approach:	They propose to compress pretrained language models to small ones with a teacher-student paradigm to fill the capacity gap.
Outcome:	The proposed model achieves state-of-the-art performance at small FLOPs compared with competitive baselines.

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Jiaqi Guo, Ziliang Si, Yu Wang, Qian Liu, Ming Fan, Jian-Guang Lou, Zijiang Yang, Ting Liu

From Regulatory Approvals to Patents: Cross-Domain Linking for Cardiovascular Device Traceability (2026.acl-long)

Challenge:	XDTS is a cross-database context-dependent text-to-sql problem with wide range of applications.
Approach:	They present a large-scale Chinese dataset for cross-database context-dependent Text-to-SQL . they find that only 35% of questions are context-independent and 28% of SQL queries are easy .
Outcome:	The proposed approach achieves an exact match accuracy of 40% over all questions and 16% over all question sequences.

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Qingqing Yang, Haijiang Liu, Moyan Li

PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection (2026.acl-long)

Challenge:	a new cross-domain entity linking problem exists between FDA-approved medical devices and patents . a recent study compared the semantics of FDA documents with patents, resulting in minimal overlap .
Approach:	They propose a framework that links FDA-approved medical devices to their patents . they propose 'MedDevKG' framework that integrates a domain-specific ontology .
Outcome:	The proposed framework outperforms existing methods in lower-bound recalls and noise reductions.

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Jinhan Liu, Yibo Yang, Ruiying Lu, Piotr Piękos, Yimeng Chen, Peng Wang, Dandan Guo

Explainable Text Classification with LLMs: Enhancing Performance through Dialectical Prompting and Explanation-Guided Training (2025.findings-emnlp)

Challenge:	Existing likelihood-based methods for detecting pretraining data are limited in black-box, zero-shot settings.
Approach:	They propose a training-free and plug-and-play framework that reweights token-level scores to amplify distinct signals from early positions while suppressing noise from later ones.
Outcome:	The proposed framework amplifys signals from early positions while suppressing noise from later positions.

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Huaming Du, Lei Yuan, Cancan Feng, Guisong Liu, Gang Kou, Carl Yang

Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation (P19-1)

Challenge:	Existing explanation methods that generate keywords may be less effective due to missing critical contextual information.
Approach:	They propose a new method to generate explanations for possible labels using LLMs and a dialectical prompt.
Outcome:	The proposed method significantly improves accuracy and explanation quality over state-of-the-art methods on multiple datasets from diverse domains.

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Shuming Ma, Pengcheng Yang, Tianyu Liu, Peng Li, Jie Zhou, Xu Sun

Large-Scale Corpus Construction and Retrieval-Augmented Generation for Ancient Chinese Poetry: New Method and Data Insights (2025.findings-naacl)

Challenge:	Existing methods for table-to-text generation use encoder-decoder framework, but lack of large parallel data is a problem for many domains.
Approach:	They propose a model to separate table-to-text generation into two stages: key fact prediction and surface realization.
Outcome:	The proposed model achieves 27.34 BLEU score with only 1,000 parallel data, while the baseline model only achieves 9.71 BLUE score.

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Yang Liu, Lan Lan, Jiahuan Cao, Hiuyi Cheng, Kai Ding, Lianwen Jin

Target-Aware Language Modeling via Granular Data Sampling (2024.emnlp-main)

Challenge:	Ancient Chinese poetry presents unique challenges for Large Language Models due to data scarcity and limited ability of general LLMs when dealing with ACP.
Approach:	They propose a specialized Retrieval-Augmented Generation framework to improve LLMs' performance . they use 1.1 million ancient poems and 990K related texts to address hallucination issues .
Outcome:	The proposed framework improves performance of LLMs in ancient Chinese poetry domain from 49.2% to 89.0%.

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Ernie Chang, Pin-Jie Lin, Yang Li, Changsheng Zhao, Daeil Kim, Rastislav Rabatin, Zechun Liu, Yangyang Shi, Vikas Chandra

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction (2023.emnlp-main)

Challenge:	Language model pretraining is the cornerstone of universal language models (LMs), creating generalpurpose representations to excel across a variety of downstream tasks.
Approach:	They propose to use multi-granular tokens to sample large-scale language models for domain-specific use cases.
Outcome:	The proposed model outperforms random sampled samples on eight benchmarks with 1% of the data and performs on par with the full RefinedWeb data.

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Shiao Meng, Xuming Hu, Aiwei Liu, Shuang Li, Fukun Ma, Yawen Yang, Lijie Wen

Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddings (2025.emnlp-main)

Challenge:	Existing methods to identify semantic relations between entities are time-consuming and labor-intensive.
Approach:	They propose a relation-aware prototype learning method for document-level relation extraction (FSDLRE) they propose RAPL, which judiciously leverages relation descriptions and real NOTA instances as guidance .
Outcome:	The proposed method outperforms state-of-the-art approaches by 2.61% F1 . it generates task-specific NOTA prototypes and refines relation prototypes .

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Shiyu Li, Yang Tang, Ruijie Liu, Shi-Zhe Chen, Xi Chen

The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse (2024.findings-acl)

Challenge:	Existing studies use LoRA to fine-tune existing LLMs, but this is limited by the data and training gap between them and embedding models.
Approach:	They propose a new 1.4B-parameter LLM trained from scratch and fine-tuned as a text embedder that integrates embeddings across different languages.
Outcome:	The proposed model improves performance on the Massive Text Embedding Benchmark (MTEB) and Chinese MTEB (May 19, 2025).

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Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, Xueqi Cheng

UniTE: Unified Translation Evaluation (2022.acl-long)

Challenge:	Even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks.
Approach:	They propose to use perplexity as a surrogate metric to determine whether an edited model's performance is affected by a single edit.
Outcome:	The proposed method shows that even a single edit can cause model collapse, manifesting as significant performance degradation in various benchmark tasks.

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Yu Wan, Dayiheng Liu, Baosong Yang, Haibo Zhang, Boxing Chen, Derek Wong, Lidia Chao

Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System (2022.naacl-industry)

Challenge:	Recent methods for evaluation of translation quality are focused on one task, ignoring commonalities .
Approach:	They propose a unified framework engaged with abilities to handle all three evaluation tasks.
Outcome:	The proposed framework can universally surpass state-of-the-art or winner methods across tasks.

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Tao Zhu, Zhe Zhao, Weijie Liu, Jiachi Liu, Yiren Chen, Weiquan Mao, Haoyan Liu, Kunbo Ding, Yudong Li, Xuefeng Yang

StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models (2024.findings-acl)

Challenge:	Existing continual learning methods use data replay, parameter isolation and regularization to mitigate catastrophic forgetting.
Approach:	They propose a parameter-efficient continual learning framework that updates parameters offline and then trains using an online regularization method.
Outcome:	The proposed framework reduces catastrophic forgetting and saves the model with the changed parameters instead of all parameters.

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Zhicheng Guo, Sijie Cheng, Hao Wang, Shihao Liang, Yujia Qin, Peng Li, Zhiyuan Liu, Maosong Sun, Yang Liu

Quest2DataAgent: Automating End-to-End Scientific Data Collection (2025.emnlp-demos)

Challenge:	Large Language Models (LLMs) have witnessed remarkable advancements in recent years, prompting the exploration of tool learning.
Approach:	They propose a virtual API server and stable evaluation system to assess the stability of large-scale real-time APIs.
Outcome:	The proposed benchmarks demonstrate the stability of the proposed system and its caching system.

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Tianyu Yang, Yuhan Liu, Sobin Alosious, Ethan A. Brown, Jason R. Rohr, Tengfei Luo, Xiangliang Zhang

MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning (2023.acl-long)

Challenge:	Existing approaches for data collection are labor-intensive and dependent on domain expertise.
Approach:	They propose a general-purpose multi-agent framework for automating scientific data collection workflows.
Outcome:	The proposed framework improves data relevance, usability, and time efficiency over existing methods.

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Bang Yang, Fenglin Liu, Xian Wu, Yaowei Wang, Xu Sun, Yuexian Zou

Aspect and Sentiment Aware Abstractive Review Summarization (C18-1)

Challenge:	Existing methods for supervised visual captioning require large scale of images or videos paired with descriptions in a specific language.
Approach:	They propose a zero-shot approach that generates captions for different scenarios without labeling . they use concept prompts to retrieve concepts and auto-encode them to learn writing styles .
Outcome:	The proposed approach generates captions for different scenarios and languages without labeled vision-caption pairs.

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Min Yang, Qiang Qu, Ying Shen, Qiao Liu, Wei Zhao, Jia Zhu

MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding (2026.acl-long)

Challenge:	Abstractive summarization is a task that generates short and concise summaries of user generated reviews.
Approach:	They propose an interactive attention mechanism to learn the representations of context and aspect words within reviews, acted as an encoder.
Outcome:	The proposed model achieves impressive results compared to other strong competitors on a real-life dataset.

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Fuwen Luo, Shengfeng Lou, Chi Chen, Ziyue Wang, Chenliang Li, Weizhou Shen, Jiyue Guo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu

Visual Prompting in LLMs for Enhancing Emotion Recognition (2024.emnlp-main)

Challenge:	Existing methods for MLLMs struggle with fine-grained temporal reasoning . despite advances in video understanding, current methods struggle with time-sensitive tasks .
Approach:	They propose a time-stamp-aware multi-segment grounding method that enhances temporal understanding by introducing timestamps.
Outcome:	The proposed method outperforms existing methods on time-sensitive tasks and generalizes well across diverse temporal understanding scenarios.

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Qixuan Zhang, Zhifeng Wang, Dylan Zhang, Wenjia Niu, Sabrina Caldwell, Tom Gedeon, Yang Liu, Zhenyue Qin

FrontCoder: Scaling Visual Fidelity in Front-End Code Generation (2026.findings-acl)

Challenge:	Existing methods for enhancing in-context emotion classification fail to include spatial relationships between different people and facial features within a single face.
Approach:	They propose a set-of-vision prompting approach that uses spatial information to mark targets precisely.
Outcome:	The proposed approach improves face count and emotion categorization while preserving the enriched image context.

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Jun Feng, Jian Yang, Wei Zhang, Jing Wang, Keyi Chen, Xiaokun Yang, Weicheng Gu, Yihang Lou, Yan Bai, Xianglong Liu

Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding (2024.findings-emnlp)

Challenge:	Existing work on front-end code generation fails to provide visual fidelity and rendering quality for front- end developers.
Approach:	They propose a three-stage pipeline to enhance front-end code generation capabilities in LLMs . they use synthetic data, quality-controlled supervised fine-tuning, and reinforcement learning .
Outcome:	The proposed model achieves competitive performance with frontier models while maintaining generation efficiency.

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Liang Zhao, Xiachong Feng, Xiaocheng Feng, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin, Ting Liu

Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models (2023.findings-emnlp)

Challenge:	Existing methods to enhance length extrapolation of large language models have been developed, but a systematic survey is lacking.
Approach:	They propose to examine the effects of positional encoding on length extrapolation.
Outcome:	The proposed methods improve the extrapolation of large language models, but they are still lacking a systematic survey.

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Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang

Quantifying Stereotypes in Language (2024.eacl-long)

Challenge:	Large language models can perform a wide range of tasks by following natural language instructions without task-specific fine-tuning.
Approach:	They propose a method to automatically improve the quality of LLM instructions . they leverage the generative ability of LMS to generate diverse candidate instructions based on a scoring model trained on 575 existing NLP tasks.
Outcome:	The proposed method surpasses human-written and LLM-generated instructions on 118 out-of-domain tasks.

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Yang Liu

Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization (2023.emnlp-main)

Challenge:	Existing studies define a sentence as stereotypical and anti-stereotypical, but they lack a fine-grained quantification of stereotypes.
Approach:	They quantify stereotypes in language by annotating a dataset to quantify stereotype of sentences.
Outcome:	The proposed models validate the findings of the current studies.

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Tianshi Che, Ji Liu, Yang Zhou, Jiaxiang Ren, Jiwen Zhou, Victor Sheng, Huaiyu Dai, Dejing Dou

HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy (2024.acl-long)

Challenge:	Prompt tuning of Large Language Models (LLMs) can incur performance degradation or low training efficiency.
Approach:	They propose a prompt tuning approach with Adaptive Optimization to enable efficient FL of LLMs.
Outcome:	The proposed approach improves performance and efficiency simultaneously and addresses client drift problems on both the device and server sides.

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Mengxi Xiao, Qianqian Xie, Ziyan Kuang, Zhicheng Liu, Kailai Yang, Min Peng, Weiguang Han, Jimin Huang

ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL (2026.acl-long)

Challenge:	Large Language Models (LLMs) can be used in psychotherapy to overcome challenges such as shame, distrust, and resource scarcity.
Approach:	They propose a cognitive reframing therapy method that uses empathetic dialogue to address deep-rooted negative thoughts and fosters rational, balanced perspectives.
Outcome:	The proposed model outperforms other models in terms of empathy, guidance, and logical coherence, demonstrating its effectiveness and potential positive impact on psychotherapy.

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Yaxun Dai, Wenxuan Xie, Xialie Zhuang, Tianyu Yang, Ziyi Liu, Haiqin Yang, Yiying Yang, Yuhang Zhao, Pingfu Chao, Wenhao Jiang

Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension (2023.findings-emnlp)

Challenge:	Current Text-to-SQL reasoning models lack integrated execution feedback during generation.
Approach:	They propose a text-to-SQL framework that interacts with the SQL execution engine during decoding and dynamically adjusts reasoning based on execution feedback.
Outcome:	The proposed framework achieves 89.1% accuracy on Spider and 65.3% on BIRD at the 7B scale.

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Nuo Chen, Hongguang Li, Junqing He, Yinan Bao, Xinshi Lin, Qi Yang, Jianfeng Liu, Ruyi Gan, Jiaxing Zhang, Baoyuan Wang, Jia Li

MGR: Multi-generator Based Rationalization (2023.acl-long)

Challenge:	Existing benchmarks for conversational machine reading comprehension are inconsistent with real scenarios.
Approach:	They propose to use a Chinese CMRC benchmark to evaluate model's generalization ability towards diverse domains by using zero-shot/few-shot settings.
Outcome:	The proposed benchmarks are based on 831 hot-topic driven conversations with 4,742 turns and cover 33 domains.

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Wei Liu, Haozhao Wang, Jun Wang, Ruixuan Li, Xinyang Li, YuanKai Zhang, Yang Qiu

Challenge:	Existing approaches to explain NLP models have two key challenges: spurious correlation and degeneration.
Approach:	They propose a rationalization framework using a generator and a predictor to construct a self-explaining NLP model with spurious correlation and degeneration as key challenges.
Outcome:	The proposed method improves the F1 score by 20.9% compared to state-of-the-art methods.

From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement (2025.emnlp-main)

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JianZhi Yan, Le Liu, Youcheng Pan, Shiwei Chen, Zike Yuan, Yang Xiang, Buzhou Tang

Challenge:	Chain-of-Thought reasoning introduces significant inference latency due to its verbosity.
Approach:	They propose a framework that leverages token elasticity phenomenon to progressively compress CoTs via multiround refinement.
Outcome:	The proposed method achieves an average accuracy improvement of 5.6% over state-of-the-art baselines while reducing CoT length by an average of 47 tokens and significantly lowering latency.

Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention (2022.findings-naacl)

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Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tur

Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation (2023.acl-long)

Challenge:	Existing methods to fine-tune pre-trained language models are parameter efficient . fine- tuning the models requires multiple copies of the parameters, which is inefficient.
Approach:	They propose to use kernel-based adapters to tune only a few parameters while freezing the rest of the parameters.
Outcome:	The proposed methods achieve or improve strong performance over a diverse set of natural language generation and understanding tasks.

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Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization (2021.naacl-main)

Challenge:	Existing methods to improve k-Nearest neighbor machine translation (kNN-MT) are based on the ability to non-parametrically adapt to new domains.
Approach:	They propose a method to boost the datastore retrieval of k-Nearest neighbor machine translation by reconstructing the original datastore.
Outcome:	The proposed method boosts the retrieval and translation quality of k-Nearest neighbor machine translation by reconstructing the original datastore.

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Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev

Retrieved Sequence Augmentation for Protein Representation Learning (2024.emnlp-main)

Challenge:	Existing work on meeting summarization tasks is limited to short summaries that cover all the content of a meeting.
Approach:	They propose a query-based multi-domain meeting summarization task that generates a single short summary of meetings based on a transcript.
Outcome:	The proposed task is based on 1,808 query-summary pairs over 232 meetings in multiple domains.

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Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Sheng Wang, Lingpeng Kong

Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales (2025.findings-emnlp)

Challenge:	Using multiple sequence alignments (MSA) to extract evolutionary knowledge is limited.
Approach:	They propose to use multiple sequence alignments to augment protein representations . they propose to employ Retrieved Sequence Augmentation to enhance protein representation learning .
Outcome:	The proposed method surpasses MSA Transformer by 5% in structural and property prediction tasks while being 373 times faster.

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JianZhi Yan, Le Liu, Youcheng Pan, Shiwei Chen, Yang Xiang, Buzhou Tang

Marathon: A Race Through the Realm of Long Context with Large Language Models (2024.acl-long)

Challenge:	Existing work on rationale quality underestimates the importance of CoT distillation, focusing primarily on data quantity, which may result in transferring noisy or incorrect information to the student model.
Approach:	They propose a method which can discern and select high quality rationales for distillation and a Rationale Difficulty metric to measure the ability of the student model to generate the correct answer under a given rationale.
Outcome:	The proposed method achieves 4.6% accuracy improvement over baseline data on seven datasets over three tasks, controlling accuracy, diversity, and difficulty.

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Lei Zhang, Yunshui Li, Ziqiang Liu, Jiaxi Yang, Junhao Liu, Longze Chen, Run Luo, Min Yang

LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)

Challenge:	Existing long-context benchmarks do not accurately evaluate large language models’ comprehension and reasoning abilities in extended texts.
Approach:	They propose a new evaluation benchmark that adopts a multiple-choice question format and uses a multi-choke question format to assess the comprehension and reasoning skills of large language models.
Outcome:	The proposed benchmark provides a rapid, precise, and unbiased appraisal of the long-context comprehension skills of large language models.

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Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang

Challenge:	Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making.
Approach:	They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain.
Outcome:	The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge.

Discerning and Resolving Knowledge Conflicts through Adaptive Decoding with Contextual Information-Entropy Constraint (2024.findings-acl)

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Xiaowei Yuan, Zhao Yang, Yequan Wang, Shengping Liu, Jun Zhao, Kang Liu

InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning (2025.findings-emnlp)

Challenge:	Existing decoding methods for large language models (LLMs) are specialized in resolving knowledge conflicts and could inadvertently deteriorate performance in absence of conflicts.
Approach:	They propose an adaptive decoding method to discern whether knowledge conflicts occur and resolve them by a contextual information-entropy constraint decoding technique.
Outcome:	The proposed method improves the model’s faithfulness to conflicting context and maintains high performance among non-conflicting contexts.

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Xiaotian Han, Yiren Jian, Xuefeng Hu, Haogeng Liu, Yiqi Wang, Qihang Fan, Yuang Ai, Huaibo Huang, Ran He, Zhenheng Yang, Quanzeng You

Improving Domain Adaptation Translation with Domain Invariant and Specific Information (N19-1)

Challenge:	InfiMM-WebMath-40B is a dataset of interleaved image-text documents . it consists of 24 million web pages, 85 million image URLs, and 40 billion text tokens .
Approach:	InfiMM-WebMath-40B is a high-quality dataset of interleaved image-text documents . it contains 24 million web pages, 85 million image URLs, and 40 billion text tokens .
Outcome:	InfiMM-WebMath-40B is a high-quality dataset of interleaved image-text documents . it consists of 24 million web pages, 85 million image URLs, and 40 billion text tokens .

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Shuhao Gu, Yang Feng, Qun Liu

Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering (2026.acl-long)

Challenge:	Neural machine translation models are based on the encoder-decoder architecture, which makes them overfitting to frequent observations.
Approach:	They propose a method to explicitly model out-of-domain information in an encoder-decoder framework . they propose combining out- of-domain training data with out-out-of domain data .
Outcome:	The proposed method outperforms baselines on multiple data sets.

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Shuliang Liu, Songbo Yang, Dong Fang, Sihang Jia, Yuqi Tang, Lingfeng Su, Ruoshui Peng, Yibo Yan, Xin Zou, Xuming Hu

MPII: Multi-Level Mutual Promotion for Inference and Interpretation (2022.acl-long)

Challenge:	Existing approaches to overcome object hallucination are limited . Existing mitigations include costly retraining and a training-free inference framework .
Approach:	They propose a training-free inference framework that simulates a metacognitive self-correction process.
Outcome:	The proposed framework reduces object hallucination rates by 12.67% on MMHal-Bench and improves accuracy by 5.8% on POPE.

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Yan Liu, Sanyuan Chen, Yazheng Yang, Qi Dai

Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge (2024.emnlp-main)

Challenge:	Existing methods for providing interpretations provide human-unfriendly interpretations, resulting in sub-optimal performance.
Approach:	They propose a multi-level Mutual Promotion mechanism for self-evolved inference and sentence-level interpretation that integrates inference with interpretation in an autoregressive manner.
Outcome:	The proposed approach outperforms baseline models on NLI and CQA tasks for both inference performance and interpretation quality.

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Yuxuan Wang, Yueqian Wang, Pengfei Wu, Jianxin Liang, Dongyan Zhao, Yang Liu, Zilong Zheng

Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews (2021.acl-long)

Challenge:	despite advances in multimodal large language models, the challenge of interpreting long-form videos remains a challenge . despite advancements in video-language benchmarks, the inefficiency in temporal grounding and limited pre-trained context window size remains .
Approach:	They propose a framework that bootstraps MLLMs with advanced temporal grounding capabilities and broadens their contextual scope.
Outcome:	The proposed framework significantly enhances the temporal capabilities of existing MLLMs.

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Junhao Liu, Zhen Hai, Min Yang, Lidong Bing

The Agent’s First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios (2026.findings-acl)

Challenge:	Existing review helpfulness prediction tasks rely on text and image modalities to analyze review helpfuliness.
Approach:	They propose a task to analyze review helpfulness from text and visual modalities and propose 'multi-perspective coherent reasoning' method to combine coherence between product and review is proposed.
Outcome:	The proposed method can lead to performance increase of 8.5% compared to the best performing text-only model.

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Daocheng Fu, Jianbiao Mei, Rong Wu, Xuemeng Yang, Jia Xu, Ding Wang, Pinlong Cai, Yong Liu, Licheng Wen, Botian Shi

Challenge:	Existing research mainly focuses on performance upper bounds in static environments, overlooking stochastic real-world deployment.
Approach:	They propose a dynamic evaluation environment that simulates a "trainee" agent continuously exploring a novel setting.
Outcome:	The proposed model evaluates agents in a dynamic evaluation environment that simulates a "trainee" agent continuously exploring a novel setting.

ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning (2026.findings-acl)

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Zhensheng Wang, Xiaole Liu, Wenmian Yang, Kun Zhou, Yiquan Zhang, Weijia Jia

Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation (2020.coling-main)

Challenge:	Existing tabular question answering systems cannot perform future-oriented numerical prediction . open-domain tabular questions are a popular approach for QA tasks .
Approach:	They propose a task that covers time-series forecasting and forecast-based reasoning scenarios using real estate data.
Outcome:	The proposed framework decomposes the problem into three collaborative roles that synthesize the results to construct a precise and consistent final answer.

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Junhao Liu, Linjun Shou, Jian Pei, Ming Gong, Min Yang, Daxin Jiang

AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling (2025.findings-acl)

Challenge:	Cross-lingual Machine Reading Comprehension (CLMRC) is a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese.
Approach:	They propose a novel approach to augment cross-lingual machine reading comprehension by combining knowledge from multiple language branch models into a single model for all target languages.
Outcome:	Extensive experiments on two CLMRC benchmarks show the proposed method is effective and robust to data noises.

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Zihan Liu, Yang Chen, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping

CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures (2026.eacl-long)

Challenge:	In this paper, we introduce a suite of math models that excel in solving complex math problems.
Approach:	They propose a supervised fine-tuning process that achieves competitive performance across general domains, followed by targeted fine- tuning for the math domain using a carefully curated set of prompts and synthetically generated responses.
Outcome:	The proposed model outperforms Qwen2.5-Math-72B-Instruct, GPT-4o and Claude-3.5 Sonnet in the math domain.

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Punya Syon Pandey, Yongjin Yang, Jiarui Liu, Zhijing Jin

Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks (2025.coling-main)

Challenge:	Game-theoretic interactions between agents with large language models (LLMs) have revealed many emergent capabilities, yet the linguistic diversity of these interactions has not been quantified.
Approach:	They propose a metric to quantify the effectiveness of language use within multi-agent systems across different game-theoretic interactions.
Outcome:	The proposed metric measures the effectiveness of language use within multi-agent systems across game-theoretic interactions.

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Chenxi Wang, Zongfang Liu, Dequan Yang, Xiuying Chen

Challenge:	Existing studies on social media echo chambers have been limited to numbers and formulas.
Approach:	They propose an LLM-based simulation for the social opinion network to evaluate and counter polarization phenomena.
Outcome:	The proposed model can simulate opinion dynamics and echo chambers using language-based simulations.

Draft, Verify, Restore: Self-Refining Historical Inscription Restoration with a Unified MLLM (2026.acl-long)

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Yuyi Zhang, Junle Liu, Peirong Zhang, Jianliang Liu, Zhenhua Yang, Lianwen Jin

Challenge:	Existing methods for end-to-end historical inscription restoration rely on task-separated pipelines with irreversible error accumulation and patch-based generation that sacrifices page-level consistency.
Approach:	They propose a unified MLLM for end-to-end historical inscription restoration that integrates draft-guided localization and Hierarchical self-refinement to enable accurate damage localization.
Outcome:	The proposed model achieves superior performance in both text restoration accuracy and appearance restoration quality.

ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation (2026.findings-acl)

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Yihua Zhang, Mingfu Liang, Jiyan Yang, Rong Jin, Wen-Yen Chen, Yiping Han, Huayu Li, Buyun Zhang, Liang Luo, Luke Simon, Sijia Liu, Tianlong Chen, Xi Liu

Teaching Your Models to Understand Code via Focal Preference Alignment (2025.emnlp-main)

Challenge:	Recent advances in multimodal recommenders lack explicit reasoning and self-awareness of uncertainty.
Approach:	They propose a reasoning-augmented multimodal agent structured around a three-stage explicit reasoning pipeline.
Outcome:	The proposed agent improves ranking metrics and performance on four standard recommendation tasks across five real-world datasets.

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Jie Wu, Haoling Li, Xin Zhang, Xiao Liu, Yangyu Huang, Jianwen Luo, Yizhen Zhang, Zuchao Li, Ruihang Chu, Yujiu Yang, Scarlett Li

FaVe: Factored and Verified Search Rationale for Long-form Answer (2025.findings-acl)

Challenge:	Existing methods for supervised fine-tuning focus on unit test feedback to construct preference pairs.
Approach:	They propose a preference alignment framework that mimics human iterative debugging to refine Code LLMs.
Outcome:	Experiments show that Preference Learning improves on BigCodeBench and BigCodeBind tasks.

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Jihyuk Kim, Sungjin Lee, Seung-won Hwang, Yang Liu

From Language to Driving: A Dual-Loop SLM-Enhanced Framework for Multi-Planner Scheduling via a Domain-Specific Language (2026.acl-long)

Challenge:	Existing solutions for long-form question-answering (LFQA) use chain-of-thought (CoT) with retrieval-augmented generation (RAG).
Approach:	They propose to integrate chain-of-thought (CoQ) with retrieval-augmented generation to improve answer comprehensiveness and verifiability.
Outcome:	The proposed approach outperforms ChatGPT baselines while maintaining efficiency.

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Jiawei Liu, Xun Gong, Muli Yang, Xingrui Yu, Fen Fang, Xulei Yang, Ivor Tsang, Yunfeng hu, Hong Chen, Qing Guo

Challenge:	Recent large language model-based AD research offers new avenues to address this challenge.
Approach:	They propose a small language model (SLM) for high-level semantic reasoning and schedule generation, while an inner loop performs low-level, high-frequency schedule execution and vehicle control.
Outcome:	The proposed framework improves instruction completion rates while maintaining high safety and compliance relative to multiple baselines.

A Semantic Filter Based on Relations for Knowledge Graph Completion (2021.emnlp-main)

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Zongwei Liang, Junan Yang, Hui Liu, Keju Huang

HighMATH: Evaluating Math Reasoning of Large Language Models in Breadth and Depth (2025.findings-emnlp)

Challenge:	Knowledge graph embedding is a new form of knowledge graphing that allows for better link prediction.
Approach:	They propose to use relational embedding to fit symmetry/antisymmetry and combination relationships.
Outcome:	The proposed model can fit symmetry/antisymmetry and combination relationships.

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Yan Liu, Minghui Zhang, Bojian Xiong, Yifan Xiao, Yinong Sun, Yating Mei, Longyu Zeng, Jingchao Yang, Yang Wang, Deyi Xiong

Revisiting Who’s Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective (2024.emnlp-main)

Challenge:	a gap in math models' accuracy has been widened with the development of large language models (LLMs) . a new study aims to bridge this gap by evaluating a set of high-level math reasoning models .
Approach:	They propose to evaluate large language models on existing math benchmarks to bridge this gap . they collect 5,293 problems from Chinese senior high school mathematics exams .
Outcome:	The proposed model is based on o1-like models and a high-level model.

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Yujian Liu, Yang Zhang, Tommi Jaakkola, Shiyu Chang

Challenge:	Existing and new datasets show that our approach achieves competitive performance in all of the criteria.
Approach:	They propose a new task of LLM targeted unlearning where unlearning targets only the information about the unlearning target, rather than everything in the unlearned documents.
Outcome:	The proposed method achieves competitive performance on existing and new datasets without optimizing for the aforementioned criteria.

Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval (2024.findings-acl)

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Yan Gao, Zhiwei Cao, Zhongjian Miao, Baosong Yang, Shiyu Liu, Min Zhang, Jinsong Su

Improving Knowledge Production Efficiency With Question Answering on Conversation (2023.acl-industry)

Challenge:	Existing models for non-parametric domain adaptation lack kNN retrieval at each timestep, leading to substantial time overhead.
Approach:	They propose a kNN-MT-based model that uses a domain-specific translation knowledge store to interpolate the prediction distribution of the model.
Outcome:	The proposed model significantly extends kNN-MT with dynamic retrieval on widely-used datasets.

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Changlin Yang, Siye Liu, Sen Hu, Wangshu Zhang, Teng Xu, Jing Zheng

Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction (2024.acl-long)

Challenge:	Existing researches on conversation-based QA focus on document-based tasks . current researche focuses on document based tasks, but there is a lack of researche on conversation based qa .
Approach:	They propose a multi-span extraction model on conversation-based QA and introduce continual pre-training and multi-task learning schemes to further improve model performance.
Outcome:	The proposed model outperforms baseline on two Chinese datasets and will be released for research purposes.

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Yiren Jian, Tingkai Liu, Yunzhe Tao, Chunhui Zhang, Soroush Vosoughi, Hongxia Yang

Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search (2021.emnlp-main)

Challenge:	EVLGen is a framework for visual-language pre-training with high computational demands.
Approach:	They propose a streamlined framework for the pre-training of visually conditioned language generation models with high computational demands.
Outcome:	The proposed framework accelerates training of vision-language models by a factor of 5 without compromising performance.

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Jialu Wang, Yang Liu, Xin Wang

E-Verify: A Paradigm Shift to Scalable Embedding-based Factuality Verification (2025.findings-emnlp)

Challenge:	Extensive experiments on MS-COCO and Flickr30K benchmarks show that our methods significantly reduce the gender bias in image search models.
Approach:	They propose a fair sampling method and a feature clipping method to debias image search models.
Outcome:	The proposed methods significantly reduce gender bias in image search models on MS-COCO and Flickr30K benchmarks.

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Zeyang Liu, Jingfeng Xue, Xiuqi Yang, Wenbiao Du, Jiarun Fu, Junbao Chen, Wenjie Guo, Yong Wang

Selective Preference Optimization via Token-Level Reward Function Estimation (2025.emnlp-main)

Challenge:	Existing factuality verification methods follow a Decompose-Then-Verify paradigm, which improves granularity but suffers from poor scalability and efficiency.
Approach:	They propose a Decompose-Embed-Interact paradigm that shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space.
Outcome:	The proposed paradigm shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space .

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Kailai Yang, Zhiwei Liu, Qianqian Xie, Jimin Huang, Erxue Min, Sophia Ananiadou

RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)

Challenge:	Existing methods for maximizing preference optimization on all available tokens are noisy and inefficient.
Approach:	They propose a selective alignment strategy that centers on efficient key token selection without strong, fine-grained supervision signals.
Outcome:	The proposed strategy outperforms baseline methods on three benchmarks with up to 60% reduction in training hours.

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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang

M3TQA: Massively Multilingual Multitask Table Question Answering (2026.findings-acl)

Challenge:	Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks.
Approach:	They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions.
Outcome:	The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks.

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Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Guan Xiangyuan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, Zhoujun Li

Alignment Rationale for Natural Language Inference (2021.acl-long)

Challenge:	Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis.
Approach:	They propose a framework for massively multilingual table question answering that includes tables expanded to 97 languages from Chinese and English sources.
Outcome:	Experiments on state-of-the-art LLMs show that synthetically generated training data significantly boosts performance, especially for low-resource languages.

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Zhongtao Jiang, Yuanzhe Zhang, Zhao Yang, Jun Zhao, Kang Liu

Giving Control Back to Models: Enabling Offensive Language Detection Models to Autonomously Identify and Mitigate Biases (2024.findings-emnlp)

Challenge:	Existing explanation methods pick prominent features, but alignments between words or phrases are more enlightening clues to explain the model.
Approach:	They propose a method to generate alignment rationale explanations for co-attention based models in NLI by feature selection.
Outcome:	The proposed method is more faithful and human-readable compared with existing methods.

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Jiapeng Liu, Weijie Li, Xiaochao Fan, Wenjun Deng, Liang Yang, Yong Li, Yufeng Diao

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)

Challenge:	Existing models often rely on specific words to predict offensive content, compromising model fairness and potentially exacerbates biases against vulnerable and minority groups.
Approach:	They propose a bias self-awareness and data self-iteration framework to help models identify and mitigate biases by integrating multiple natural language processing techniques.
Outcome:	The proposed framework reduces false positive rate of models in in-distribution and out-of-difference tests, enhances model accuracy and fairness, and shows promising performance improvements on larger datasets.

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Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

Challenge:	Existing rankers excel in lexical-matching scenarios, while they struggle with complex queries requiring deep reasoning.
Approach:	They propose a new paradigm that balances flexibility and context awareness to unlock the full potential of groupwise reranking.
Outcome:	The proposed approach achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED while delivering a 6.4 inference speedup.

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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

Structured Alignment Networks for Matching Sentences (D18-1)

Challenge:	Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach:	They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome:	The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.

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Yang Liu, Matt Gardner, Mirella Lapata

Textual Inference

Challenge:	Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity.
Approach:	They propose a model of structured alignments between sentences to compare two sentences by matching their latent structures.
Outcome:	The proposed model is differentiable and trained only on the matching objective.

m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt (2024.lrec-main)

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Jian Yang, Hongcheng Guo, Yuwei Yin, Jiaqi Bai, Bing Wang, Jiaheng Liu, Xinnian Liang, LinZheng Chai, Liqun Yang, Zhoujun Li

OASIS: Mitigating Harmful Fine-tuning Attacks on LLMs via Orthogonal and Adaptive Safety Alignment Strategy (2026.acl-long)

Challenge:	Existing multimodal neural machine translation models focus on bilingual translation, but experimental results show that they outperform the text-only baselines and multilingual multimodal methods by a large margin.
Approach:	They propose a framework to leverage the multimodal prompt to guide the Multimodal Multilingual Neural Machine Translation (m3P) this framework aligns the representations of different languages with the same meaning and generates the conditional vision-language memory for translation.
Outcome:	The proposed framework outperforms previous text-only baselines and multilingual multimodal methods by a large margin.

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Jiayu Tang, Guowei Peng, Qiuhao Xie, Yuning Yang, Xiurui Xie, Guisong Liu

CodeContests+: High-Quality Test Case Generation for Competitive Programming (2025.findings-emnlp)

Challenge:	Existing methods to decouple safety enforcement from harmful feature acquisition rely on perturbation directions that conflict with harmful gradients . harmful fine-tuning attacks pose a significant challenge for service providers aiming to uphold rigorous safety standards.
Approach:	They propose an orthogonal and ad hoc safety alignment strategy to decouple safety enforcement from harmful feature acquisition.
Outcome:	Experiments on four large language models show that OASIS reduces the Harmful Score by 60% compared to baselines while maintaining stable task utility.

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Zihan Wang, Siyao Liu, Yang Sun, Ming Ding, Hongyan Li

Experiential Co-Learning of Software-Developing Agents (2024.acl-long)

Challenge:	Competitive programming has become a key task for training and evaluating large language models . but test cases of competitive programming problems are often difficult to obtain .
Approach:	They propose an LLM-based agent system that creates high-quality test cases for competitive programming problems.
Outcome:	The proposed system improves code tests on a CodeContests dataset with pass/fail labels.

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Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, YiFei Wang, Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, Zhiyuan Liu, Maosong Sun

BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation (2022.naacl-main)

Challenge:	Recent advances in large language models (LLMs) have brought significant changes to various domains, especially through autonomous agents.
Approach:	They propose a framework that lets agents learn shortcuts from their past tasks and use them for future task execution.
Outcome:	The proposed framework enables agents to tackle unseen software-developing tasks more effectively.

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Yuchen Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, Ming Zhou

Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation (2023.emnlp-main)

Challenge:	Standard evaluation metrics, e.g., BLEU, TER and METEOR, focus on the quality of translations at the sentence level and do not consider discourse-level features.
Approach:	They propose to use a metric to take discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans.
Outcome:	The proposed metric possesses better selectivity and interpretability at the document-level, and is more sensitive to document- level nuances.

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Zeyuan Yang, Peng Li, Yang Liu

Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations (2021.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated impressive performance, but they keep repeating similar mistakes due to their inability to capture relationships among samples.
Approach:	They propose a tuning-free rule accumulation framework that guides LLMs in improving their performance by learning from previous mistakes.
Outcome:	The proposed framework improves over baselines by a large margin over previous frameworks.

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Yunhe Xie, Kailai Yang, Chengjie Sun, Bingquan Liu, Zhenzhou Ji

Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)

Challenge:	Emotion Recognition in Conversation models neglect direct utterance-knowledge interaction and use emotion-indirect auxiliary tasks to augment semantic information.
Approach:	They propose a Knowledge-Interactive Network with sentiment polarity intensity-aware multi-task learning which leverages both commonsense knowledge and sentiment lexicon to augment semantic information.
Outcome:	The proposed model shows 1.04% performance improvement over the state-of-the-art model on the IEMOCAP dataset.

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Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang

Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models (2024.findings-acl)

Challenge:	Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model.
Approach:	They propose a model merging framework that modulates the contribution of each source model.
Outcome:	Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages.

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Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, Bin Gu, Mengfei Yang, Ge Li

Task-Agnostic Low-Rank Adapters for Unseen English Dialects (2023.emnlp-main)

Challenge:	Considering the vast size and wide-ranging sources of LLMs’ training data, it could explicitly or implicitly include test data.
Approach:	They propose a Contamination Detection via output Distribution (CDD) which detects data contamination only by identifying the peakedness of LLM's output distribution.
Outcome:	The proposed method improves performance by 21.8%-30.2% on humanEval and TED: trustworthy evaluation via output distribution.

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Zedian Xiao, William Held, Yanchen Liu, Diyi Yang

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

Challenge:	a recent study found that LLMs are trained on corpora disproportionally weighted in favor of Standard American English . prior work on dialect struggle with generalizing to evolving and emerging dialects in a scalable manner.
Approach:	They propose a method that leverages linguistic knowledge to enable resource-efficient adaptation . their method disentangles dialect-specific and cross-dialectal information .
Outcome:	a new method improves generalization to unseen dialects in a task-agnostic fashion . it achieves the best or most competitive performance across 5 dialects .

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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)

Challenge:	Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation.
Approach:	They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets.
Outcome:	The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark.

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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou

UniCoder: Scaling Code Large Language Model via Universal Code (2024.acl-long)

Challenge:	XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios .
Approach:	They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora.
Outcome:	The proposed dataset is labeled in English and includes only natural language understanding tasks.

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Tao Sun, Linzheng Chai, Jian Yang, Yuwei Yin, Hongcheng Guo, Jiaheng Liu, Bing Wang, Liqun Yang, Zhoujun Li

Self-Supervised Quality Estimation for Machine Translation (2021.emnlp-main)

Challenge:	Experimental results show that UniCoder with the universal code significantly outperforms the previous prompting methods by a large margin.
Approach:	They introduce the universal code (UniCode) as the intermediate representation of algorithm steps using conventions of programming languages.
Outcome:	The proposed model outperforms previous prompting methods by a large margin . the proposed model is based on a dataset of natural-language questions and code solutions .

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Yuanhang Zheng, Zhixing Tan, Meng Zhang, Mieradilijiang Maimaiti, Huanbo Luan, Maosong Sun, Qun Liu, Yang Liu

Using In-Context Learning to Improve Dialogue Safety (2023.findings-emnlp)

Challenge:	Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain.
Approach:	They propose a self-supervised method to evaluate machine-translated sentences without references by recovering masked target words.
Outcome:	The proposed method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains.

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Nicholas Meade, Spandana Gella, Devamanyu Hazarika, Prakhar Gupta, Di Jin, Siva Reddy, Yang Liu, Dilek Hakkani-Tur

METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues (2026.acl-long)

Challenge:	Recent work has highlighted safety issues with large neural-based conversational models.
Approach:	They propose a retrieval-based approach for reducing bias and toxicity in chatbot responses . they retrieve demonstrations of safe responses to similar dialogue contexts to generate a response .
Outcome:	The proposed method reduces bias and toxicity in three chatbot models . it can be used in compliment to existing dialogue safety approaches, such as RLHF.

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Haofu Yang, Jiaji Liu, Chen Huang, Faguo Wu, Wenqiang Lei, See-Kiong Ng

SeDev: Structured Semantic Exploration for LLM-Driven Code Generation (2026.acl-long)

Challenge:	Developing non-collaborative dialogue agents traditionally requires manual codification of expert strategies.
Approach:	They propose a method that formalizes expert knowledge into a Strategy Forest from raw transcripts.
Outcome:	The proposed method outperforms existing methods by 9%-10% in two benchmarks.

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Ronghui Yang, Jie Liu, Jiajie Zeng, Jiexin Wang, Jiuchuan Jiang, Bo An, Yi Cai, Mengchen Zhao

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have shown remarkable capabilities in automating code generation, but they suffer from insufficient exploration of the vast solution space.
Approach:	They propose a large-scale LLM-driven code generation framework that efficiently finds high-quality solutions in only a few iterations.
Outcome:	The proposed framework outperforms baselines while maintaining reasonable time and computational costs.

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang

Document-level Event Extraction via Parallel Prediction Networks (2021.acl-long)

Challenge:	Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question.
Approach:	They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance.
Outcome:	Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance.

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Hang Yang, Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang

Forge: Quality-Aware Reinforcement Learning for NP-Hard Optimization in LLMs (2026.findings-acl)

Challenge:	Document-level event extraction (DEE) is indispensable when events are described throughout a document.
Approach:	They propose a document-level event extraction model that can extract structured events from a text in parallel.
Outcome:	The proposed model outperforms current state-of-the-art methods on a document-level event extraction task.

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Xiaozhe Li, Xinyu Fang, Shengyuan Ding, Yang Li, Linyang Li, Haodong Duan, Qingwen Liu, Kai Chen

Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation (2026.findings-acl)

Challenge:	Existing benchmarks focus on correctness, overlooking optimality . large language models excel at math, coding, logic and puzzles .
Approach:	They propose a framework for training and evaluating Large Language Models on NP-hard optimization problems through quality-aware RLVR.
Outcome:	The proposed framework outperforms existing benchmarks on math, coding, logic and puzzles.

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Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi, Zheming Yuan, Xuxin Cheng, Cao Liu, Ke Zeng, Fengying Xie, Zhiguo Jiang, Haopeng Zhang

Knowledge Representation Learning with Contrastive Completion Coding (2021.findings-emnlp)

Challenge:	Large vision–language models suffer from object-existence hallucinations when multi-step deliberation decouples from visual evidence.
Approach:	They propose a framework that allocates visual computation by uncertainty . they propose highlighting retains global context, while selective zoom-in performs local verification.
Outcome:	The proposed framework reduces the complexity of multimodal reasoning by minimizing the operator trade-off.

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Bo Ouyang, Wenbing Huang, Runfa Chen, Zhixing Tan, Yang Liu, Maosong Sun, Jihong Zhu

Token-level Proximal Policy Optimization for Query Generation (2025.emnlp-main)

Challenge:	Existing knowledge representation learning methods suffer from immaturity on tackling potentially-imperfect knowledge graphs and highly-imbalanced positive-negative instances during training.
Approach:	They propose a framework for knowledge representation learning that incorporates two functional components to achieve robust embedding for each entity/relation.
Outcome:	The proposed framework achieves better convergence against state-of-the-art methods on several benchmarks.

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Yichen Ouyang, Lu Wang, Fangkai Yang, Pu Zhao, Chenghua Huang, Jianfeng Liu, Bochen Pang, Yaming Yang, Yuefeng Zhan, Hao Sun, Qingwei Lin, Saravan Rajmohan, Weiwei Deng, Dongmei Zhang, Feng Sun

MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) have improved search engines and recommendation systems through their text understanding capabilities.
Approach:	They propose a token-level proximal policy optimization approach to empower LLMs to perform better in query generation through fine-tuning.
Outcome:	The proposed approach outperforms existing LLMs on an open-source and industrial dataset.

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Zequn Liu, Kefei Duan, Junwei Yang, Hanwen Xu, Ming Zhang, Sheng Wang

AdaSteer: Your Aligned LLM is Inherently an Adaptive Jailbreak Defender (2025.emnlp-main)

Challenge:	Existing meta-path generation methods cannot fully exploit rich textual information in HINs.
Approach:	They propose a text-infilling-based approach to generate meta-paths from textual information in HINs.
Outcome:	The proposed approach can classify edges in the zero-shot setting, where existing methods cannot generate meta-paths.

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Weixiang Zhao, Jiahe Guo, Yulin Hu, Yang Deng, An Zhang, Xingyu Sui, Xinyang Han, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

MoDE-CoTD: Chain-of-Thought Distillation for Complex Reasoning Tasks with Mixture of Decoupled LoRA-Experts (2024.lrec-main)

Challenge:	Activation steering offers training-free defense but relies on fixed steering coefficients, resulting in suboptimal protection and increased false rejections of benign inputs.
Approach:	They propose an adaptive activation steering method that dynamically adjusts model behavior based on input characteristics.
Outcome:	The proposed method outperforms baseline methods across multiple jailbreak attacks with minimal impact on utility.

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Xiang Li, Shizhu He, Jiayu Wu, Zhao Yang, Yao Xu, Yang jun Jun, Haifeng Liu, Kang Liu, Jun Zhao

Rewiring the Transformer with Depth-Wise LSTMs (2024.lrec-main)

Challenge:	Current Chain-of-thought Distillation methods hinder CoT reasoning performance . student models are separately distilled from specific reasoning tasks . parameter update of student models severely harms CoT ability on unseen reasoning tasks.
Approach:	They propose a method which distills Chain-of-thought reasoning ability of large language models to much smaller student models.
Outcome:	The proposed method improves the reasoning ability of large language models on 14 datasets.

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Hongfei Xu, Yang Song, Qiuhui Liu, Josef van Genabith, Deyi Xiong

Plug-and-Play Data Module for Code RL: Adaptive Ambiguity Replay (2026.findings-acl)

Challenge:	Stacking non-linear layers allows deep neural networks to model complicated functions . but residual connections within each layer fail to fuse information from previous layers effectively .
Approach:	They propose a Transformer with depth-wise LSTMs connecting cascading Transformer layers and sub-layers.
Outcome:	The proposed model improves in English-German / French and multilingual tasks with BLEU.

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Jianqing Zhang, Wei Xia, Zhezheng Hao, Hong Wang, Hande Dong, Qiang Lin, Yang Liu, Jian Cao, Qiang Yang

Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge Reasoning (2025.findings-emnlp)

Challenge:	Existing approaches to reinforcement learning (RL) rely on static, in-epoch metrics that overlook training dynamics, often introducing low-utility or outdated data.
Approach:	They propose a plug-and-play module that prioritizes cross-epoch ambiguous samples to neutralize the noise from stale experiences.
Outcome:	Extensive experiments on nine LLMs show that Adaptive Ambiguity Replay outperforms state-of-the-art baselines on real-world code editing tasks.

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Jianing Wang, Jin Jiang, Yang Liu, Mengdi Zhang, Xunliang Cai

Split and Merge: Aligning Position Biases in LLM-based Evaluators (2024.emnlp-main)

Challenge:	Experimental results from competition-level complex reasoning demonstrate that bootstrapping with process prejudge can significantly enhance the reasoning ability of LLMs.
Approach:	They propose a new process prejudge strategy for LLM reasoning that bootstraps with process prejudgment .
Outcome:	The proposed method can be bootstrapped with process prejudge in LLM reasoning . it allows the model to anticipate errors rather than relying on trial and error.

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Zongjie Li, Chaozheng Wang, Pingchuan Ma, Daoyuan Wu, Shuai Wang, Cuiyun Gao, Yang Liu

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)

Challenge:	Large language models (LLMs) have shown promise as automated evaluators for assessing the quality of answers generated by AI systems.
Approach:	They propose an alignment-based system that calibrates position bias in a lightweight yet effective manner by taking into account both length and semantics and combining them into a single prompt.
Outcome:	Extensive experiments with six LLMs on 11,520 answer pairs show that PORTIA significantly improves consistency and consistency rates with humans.

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Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song

Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation (2022.findings-naacl)

Challenge:	Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges .
Approach:	They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions.
Outcome:	The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset .

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Huan Lin, Baosong Yang, Liang Yao, Dayiheng Liu, Haibo Zhang, Jun Xie, Min Zhang, Jinsong Su

MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments (2026.acl-long)

Challenge:	Existing diverse NMT models lack translation diversity due to a discrepancy between training and inference . despite the success of diverse NTM, there is still a lack of translation diversity .
Approach:	They propose a multi-candidate optimization framework for diverse NMT to deal with this defect.
Outcome:	The proposed framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality.

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Quyu Kong, Xu Zhang, Zhenyu Yang, Nolan Gao, Chen Liu, Panrong Tong, Chenglin Cai, Hanzhang Zhou, Jianan Zhang, Liangyu Chen, Zhidan Liu, Steven Hoi, Yue Wang

Self-Critique Guided Iterative Reasoning for Multi-hop Question Answering (2025.findings-acl)

Challenge:	AndroidWorld is the dominant mobile GUI agent evaluation benchmark, but its success rates are low . despite reproducible emulator environment, it lacks key application categories such as e-commerce and enterprise communication.
Approach:	They propose a benchmark for mobile GUI agents that reflects real-world usage through long-horizon, cross-application workflows.
Outcome:	The proposed framework achieves over 90% success rates, while AndroidWorld is the dominant benchmark.

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Zheng Chu, Huiming Fan, Jingchang Chen, Qianyu Wang, Mingda Yang, Jiafeng Liang, Zhongjie Wang, Hao Li, Guo Tang, Ming Liu, Bing Qin

Causality-aware Concept Extraction based on Knowledge-guided Prompting (2023.acl-long)

Challenge:	Large language models (LLMs) have demonstrated remarkable reasoning capabilities, but they still face challenges in knowledge-intensive multi-hop reasoning.
Approach:	They propose a method that uses self-critique feedback to guide iterative reasoning by enabling iteration and self-evaluation of its intermediate reasoning steps.
Outcome:	The proposed method surpasses the previous SOTA by 8.6% on three multi-hop reasoning datasets.

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Siyu Yuan, Deqing Yang, Jinxi Liu, Shuyu Tian, Jiaqing Liang, Yanghua Xiao, Rui Xie

Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction (2021.naacl-main)

Challenge:	Concepts in knowledge graphs (KGs) are far from complete in existing knowledge graph models.
Approach:	They propose to equip a PLM-based extractor with a knowledge-guided prompt to alleviate concept bias by removing spurious co-occurrence correlations from existing knowledge.
Outcome:	The proposed prompt can alleviate concept bias and improve the performance of existing models.

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Zhenghao Liu, Xiaoyuan Yi, Maosong Sun, Liner Yang, Tat-Seng Chua

Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese (2025.emnlp-main)

Challenge:	Existing GEC models produce spurious corrections or fail to detect lots of errors.
Approach:	They propose a neural network for GEC quality estimation with multiple hypotheses . VERNet establishes interactions among hypothese based on reasoning graph .
Outcome:	The proposed model achieves state-of-the-art grammatical error detection performance and best quality estimation results on four GEC datasets.

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Yikang Liu, Wanyang Zhang, Yiming Wang, Jialong Tang, Pei Zhang, Baosong Yang, Fei Huang, Rui Wang, Hai Hu

Verifiable LLM-Generated Text Detection via Projected Semantic-Structural Distributions (2026.acl-long)

Challenge:	Translationese is a linguistic property that is often introduced in the translation process that is different from those of original texts.
Approach:	They propose to use synthesized translations and translations in the wild to evaluate T-index's generalizability in cross-domain settings and its validity against human judgments.
Outcome:	The proposed measure can generalize to unseen genres, authors, and language pairs.

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Ruochong Xiong, Qien Li, Wangwang Lian, Yulong Wan, Hanlin Xue, Zhouxing Tan, Han Yang, Fengyu Lu, Junfei Liu

EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety (2025.emnlp-main)

Challenge:	Existing methods for detecting LLM-Generated text suffer from distribution misalignment and limited interpretability.
Approach:	They propose a statistical framework utilizing supervised subspace learning to extract compact features and construct conditional semantic distributions based on syntactic structures.
Outcome:	The proposed framework is superior in cross-domain, cross-model, and adversarial scenarios.

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Jiahao Qiu, Yinghui He, Xinzhe Juan, Yimin Wang, Yuhan Liu, Zixin Yao, Yue Wu, Xun Jiang, Ling Yang, Mengdi Wang

One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems (2025.findings-emnlp)

Challenge:	EmoAgent evaluates and mitigates mental health hazards in human-AI interactions, especially for vulnerable human users with psychological disorders.
Approach:	EmoAgent is a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions.
Outcome:	EmoAgent evaluates and mitigates mental health hazards in human-AI interactions.

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Zhiyuan Chang, Mingyang Li, Xiaojun Jia, Junjie Wang, Yuekai Huang, Ziyou Jiang, Yang Liu, Qing Wang

STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)

Challenge:	Existing knowledge poisoning attacks against RAG systems require multiple poisoned documents or can only function effectively on simplistic queries.
Approach:	They propose a more realistic knowledge poisoning attack that poisons only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements.
Outcome:	The proposed attack achieves success by poisoning only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements.

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Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang

MMTutorBench: The First Multimodal Benchmark for AI Math Tutoring (2026.acl-long)

Challenge:	Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience.
Approach:	They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story.
Outcome:	The proposed approach outperforms existing methods in 84.33% of the trials.

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Tengchao Yang, Sichen Guo, Mengzhao Jia, Jiaming Su, Yuanyang Liu, Zhihan Zhang, Meng Jiang

Numerical Reasoning

Challenge:	Existing benchmarks for AI math tutoring largely overlook these skills.
Approach:	They evaluate 12 leading multimodal large language models and find clear performance gaps between them.
Outcome:	The proposed benchmarks show that they can solve 770 problems and provide diagnostics and guidance to students step by step.

Detecting AI-Generated Content on Social Media with Multi-modal Language Models (2026.acl-industry)

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Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu, Sumedha Singla, Brian Vanover, Huijun Qian, Zihao Wang, Fujun Liu, Aashu Singh, Jianyu Wang, Xuewen Zhang

Single Document Summarization as Tree Induction (N19-1)

Challenge:	Existing methods for AI-generated content detection face poor generalization to newer models, reliance on single modalities, and lack of interpretable explanations.
Approach:	They propose a model that curates diverse social media data and trains a vision-language model for detection and explanation.
Outcome:	The proposed model achieves state-of-the-art detection performance on public benchmarks and observes positive downstream impacts on user engagement.

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Yang Liu, Ivan Titov, Mirella Lapata

SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training (2024.acl-long)

Challenge:	Existing approaches to extractive summarization use recurrent neural networks to model document . Existing systems use a vector representation for each sentence to generate a summary .
Approach:	They propose a model that induces a multi-root dependency tree while predicting the output summary.
Outcome:	The proposed model performs competitively against state-of-the-art methods on two benchmark datasets.

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Nan He, Weichen Xiong, Hanwen Liu, Yi Liao, Lei Ding, Kai Zhang, Guohua Tang, Xiao Han, Yang Wei

Qwen2.5-xCoder: Multi-Agent Collaboration for Multilingual Code Instruction Tuning (2025.acl-long)

Challenge:	Current methods focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication.
Approach:	They propose a method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness.
Outcome:	The proposed method significantly improves training efficiency on deduplicated datasets and improves downstream accuracy by 1.77%.

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Jian Yang, Wei Zhang, Yibo Miao, Shanghaoran Quan, Zhenhe Wu, Qiyao Peng, Liqun Yang, Tianyu Liu, Zeyu Cui, Binyuan Hui, Junyang Lin

When 20 Agents Fail to Sort: The Distributed Sorting Benchmark for Scalable Multi-Agent Systems (2026.findings-acl)

Challenge:	Existing methods to train code LLMs view each programming language in isolation . experimental results show that Qwen2.5-xCoder can bridge the gap between different programming languages .
Approach:	They propose a framework that allows agents to collaborate to enhance multilingual instruction tuning for code LLMs.
Outcome:	Experimental results show that Qwen2.5-xCoder can transfer knowledge efficiently and effectively between languages.

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Xin Yang, Junhao Wang, Bintao Tang, Xuxin Cheng, Cao Liu, Ke Zeng, Wenyuan Jiang

Challenge:	MAS-BENCH isolates coordination under explicit communication constraints . CAMOC significantly improves coordination success and efficiency across backends .
Approach:	They propose a distributed-sorting benchmark that isolates coordination under explicit communication constraints.
Outcome:	MAS-BENCH improves coordination success and efficiency across backends . CAMOC significantly improves efficiency under shared-state interaction .

On the Inference Calibration of Neural Machine Translation (2020.acl-main)

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Shuo Wang, Zhaopeng Tu, Shuming Shi, Yang Liu

Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages (2024.acl-long)

Challenge:	Existing studies show that NMT models trained with label smoothing are well-calibrated on ground-truth training data, but miscalibration remains a challenge during inference due to the discrepancy between training and inference.
Approach:	They propose a graduated label smoothing method that can improve inference calibration and translation performance.
Outcome:	The proposed method improves both inference calibration and translation performance.

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Yuanchi Zhang, Yile Wang, Zijun Liu, Shuo Wang, Xiaolong Wang, Peng Li, Maosong Sun, Yang Liu

QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training–Inference Mismatch (2026.findings-acl)

Challenge:	Contemporary large language models (LLMs) are pre-trained on multilingual corpora, but their performance lags behind in most languages compared to a few resource-rich languages.
Approach:	They propose a method that leverages the internal capabilities of large language models on resource-rich languages to enhance multilingual performance.
Outcome:	The proposed method improves multilingual performance while minimizing impact on original performance in resource-rich languages.

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Hao Gu, Hao Wang, Jiacheng Liu, Lujun Li, Qiyuan Zhu, Bei Liu, Binxing Xu, Lei Wang, Xintong Yang, Sida Lin, Sirui Han, Yike Guo

Challenge:	Recent work has shown that reinforcement learning with simple rule-based reward functions (RLVR) can induce emergent reasoning behaviors and yield gains in challenging domains such as math problem solving.
Approach:	They propose a rollout-alignment-quantization-aware RL which aligns training-side forward with the quantized rollout to minimize mismatch.
Outcome:	The proposed approach outperforms quantized-rollout training by +5.5 on Qwen3-30B-A3B MoE for math problems while maintaining low-bit throughput.

Cross-lingual Text Classification with Heterogeneous Graph Neural Network (2021.acl-short)

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Ziyun Wang, Xuan Liu, Peiji Yang, Shixing Liu, Zhisheng Wang

Boosting LLM Agents with Recursive Contemplation for Effective Deception Handling (2024.findings-acl)

Challenge:	Existing methods for cross-lingual text classification only consider factors beyond semantic similarity, causing performance degradation between some language pairs.
Approach:	They propose a method to incorporate heterogeneous information within and across languages for cross-lingual text classification using graph convolutional networks.
Outcome:	The proposed method significantly outperforms state-of-the-art models on all tasks and achieves consistent performance gain over baselines in low-resource settings.

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Shenzhi Wang, Chang Liu, Zilong Zheng, Siyuan Qi, Shuo Chen, Qisen Yang, Andrew Zhao, Chaofei Wang, Shiji Song, Gao Huang

Challenge:	Recent advances in large language models (LLMs) have led to significant success in using LLMs as agents.
Approach:	They propose a cognitive framework that incorporates first-order and second-order perspective transitions into LLMs to enhance their ability to identify and counteract deceptive information.
Outcome:	The proposed framework enhances LLMs’ ability to identify and counteract deceptive information without extra fine-tuning and data.

AMR-based Network for Aspect-based Sentiment Analysis (2023.acl-long)

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Fukun Ma, Xuming Hu, Aiwei Liu, Yawen Yang, Shuang Li, Philip S. Yu, Lijie Wen

TheraAgent: Self-Improving Therapeutic Agent for Precise and Comprehensive Treatment Planning (2026.findings-acl)

Challenge:	Recent studies have used dependency trees to extract relation between aspects and contexts, but there is a potential mismatch between the dependency tree and sentiment classification as a semantic task.
Approach:	They propose to replace the syntactic dependency tree with a semantic structure to capture the relation between an aspect and a context.
Outcome:	The proposed model improves ABSA on four public datasets with 1.13% improvement over baselines.

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Junkai Li, Yunghwei Lai, Tianyi Zhu, Zheng Long Lee, Weizhi Ma, Yang Liu

MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts (2022.emnlp-main)

Challenge:	Existing large language models rely on one-shot output without explicit verification, resulting in rough, incomplete, and potentially unsafe treatment plans.
Approach:	They propose an agentic framework that replaces one-shot generation with an iterative generate-judge-refine pipeline.
Outcome:	The proposed framework achieves state-of-the-art results on HealthBench, leading in Accuracy and Completeness.

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Xiangyu Xi, Jianwei Lv, Shuaipeng Liu, Wei Ye, Fan Yang, Guanglu Wan

RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information (2025.acl-long)

Challenge:	Recent efforts to classify unstructured texts into specific types have been limited in practical scenarios.
Approach:	They propose to use Chinese text conversations and phone conversations to expand event detection to the scenarios involving informal and heterogeneous texts.
Outcome:	The proposed dataset is based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service.

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Zhiwei Liu, Kailai Yang, Qianqian Xie, Christine de Kock, Sophia Ananiadou, Eduard Hovy

ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection (2026.findings-acl)

Challenge:	Current methods for cross-domain misinformation detection focus on in-domain tasks and do not incorporate significant sentiment and emotion features.
Approach:	They propose a retrieval augmented (RAG) LLM framework that incorporates affective information into retrieval databases.
Outcome:	The proposed framework improves on three misinformation benchmarks.

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Tao Liu, Taiqiang Wu, Runming Yang, Shaoning Sun, Junjie Wang, Yujiu Yang

Challenge:	Traditional fine-tuning ignores one-to-many nature of language, leading to overfitting . authors propose a method to fine- tune LLMs by leveraging tokens.
Approach:	They propose a method to fine-tune Large Language Models by leveraging tokens to mask low-probability tokens.
Outcome:	The proposed method outperforms baselines on general reasoning and mathematical benchmarks.

Taming Text-to-Image Synthesis for Novices: User-centric Prompt Generation via Multi-turn Guidance (2025.emnlp-main)

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Yilun Liu, Minggui He, Feiyu Yao, Yuhe Ji, Shimin Tao, Jingzhou Du, Justin Li, Jian Gao, Zhang Li, Hao Yang, Boxing Chen, Osamu Yoshie

Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models (2026.findings-acl)

Challenge:	Existing solutions for text-to-image synthesis are sensitive on textual prompts, posing a challenge for novice users.
Approach:	They propose a dialogue-based TIS prompt generation model that emphasizes user experience for novice users.
Outcome:	The proposed model emphasizes user experience for novice users . it improves user-centricity score while maintaining a competitive quality of synthesized images.

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He Xiao, Qingyao Yang, Dirui Xie, Wendong XU, Zunhai Su, Runming Yang, Haobo Liu, Wenyong Zhou, Zhengwu Liu, Ngai Wong

Dynamic Voting for Efficient Reasoning in Large Language Models (2023.findings-emnlp)

Challenge:	Large language models with billions of parameters are often over-provisioned . smaller models exhibit lower robustness under extreme low-bit quantization .
Approach:	They propose a hardware-native, metric-driven post-training quantization framework that keeps uniform bit-width within each layer while mixing precision across layers.
Outcome:	LieQ reduces large accuracy gap observed for large language models with billions of parameters while preserving standard multiplication kernels.

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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Xingzhang Ren, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv

Exploring Fine-Grained Human Motion Video Captioning (2025.coling-main)

Challenge:	Multi-path voting methods generate multiple reasoning paths for each problem, causing factual errors and illusion generation.
Approach:	They propose a multi-path voting technique that effectively reduces the number of reasoning paths during multi-path voting while preserving accuracies.
Outcome:	The proposed method outperforms Self-consistency using 24.7% of the number of paths on the LetterConcat task.

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Bingchan Zhao, Xinyi Liu, Zhuocheng Yu, Tongchen Yang, Yifan Song, Mingyu Jin, Sujian Li, Yizhou Wang

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)

Challenge:	Existing video captioning models fail to capture nuanced semantics of videos . existing models generate coarse descriptions of human motions, resulting in poor quality .
Approach:	They construct a fine-grained human motion video captioning dataset named BoFiT and a model that generates fine-grain descriptions of human motions via prompting.
Outcome:	The proposed model outperforms existing models on comprehensive metrics.

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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo

Adversarial Attacks Against Automated Fact-Checking: A Survey (2025.emnlp-main)

Challenge:	Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach:	They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome:	The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

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Fanzhen Liu, Sharif Abuadbba, Kristen Moore, Surya Nepal, Cecile Paris, Jia Wu, Jian Yang, Quan Z. Sheng

Chinese Morpheme-informed Evaluation of Large Language Models (2024.lrec-main)

Challenge:	Existing fact-checking systems are vulnerable to adversarial attacks that manipulate or generate claims, evidence, or claim-evidence pairs.
Approach:	They examine the impact of adversarial attacks on existing AFC systems and examine their impact on existing ones.
Outcome:	The findings highlight the need for resilient fact-checking frameworks in limiting misinformation spread and supporting public trust.

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Yaqi Yin, Yue Wang, Yang Liu

Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States (2025.acl-long)

Challenge:	Existing evaluations of large language models focused on the perspective of various tasks or abilities.
Approach:	They propose to evaluate large language models from a linguistic perspective and use morpheme to measure morphology and syntax.
Outcome:	The proposed model outperforms ChatGPT in Chinese scenarios with a morpheme-informed benchmark and human exam questions.

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Yang Xiao, Jiashuo Wang, Qiancheng Xu, Changhe Song, Chunpu Xu, Yi Cheng, Wenjie Li, Pengfei Liu

Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration (2025.acl-long)

Challenge:	Existing benchmarks assess basic Theory of Mind abilities but neglect temporal evolution of mental states in real-world social contexts.
Approach:	They propose a benchmark specifically designed to evaluate Large Language Models' ability to understand and track the temporal progression of mental states across interconnected scenarios.
Outcome:	The proposed benchmarks underperform humans by 44.7% and show that they can model the dynamic nature of human mental states better than existing models.

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Yuyi Zhang, Peirong Zhang, Zhenhua Yang, Pengyu Yan, Yongxin Shi, Pengwei Liu, Fengjun Guo, Lianwen Jin

Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)

Challenge:	Existing methods for historical document restoration focus on single modality or limited-size restoration, failing to meet practical needs.
Approach:	They propose a full-page HDR dataset and an automated HDR solution to replace manual restoration methods.
Outcome:	The proposed solution improves OCR accuracy from 46.83% to 84.05% when processing severely damaged documents, with enhancement to 94.25% through human-machine collaboration.

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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu

Parameter Selection: Why We Should Pay More Attention to It (2021.acl-short)

Challenge:	Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent.
Approach:	They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs.
Outcome:	The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models.

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Jie-Jyun Liu, Tsung-Han Yang, Si-An Chen, Chih-Jen Lin

Text Classification

Challenge:	supervised learning is a challenging process due to the huge number of parameter combinations.
Approach:	They present an example of parameter selection in supervised learning . authors use a set of frequently occurring labels without a parameter tuning . they say this illustrates the seriousness of parameter tuning in a supervised field .
Outcome:	The proposed study shows that without adequate attention, the research progress can be uncertain or even illusive.

Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding (2024.findings-acl)

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Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui

Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning (2024.findings-acl)

Challenge:	Large Language Models (LLMs) have a high inference latency stemming from autoregressive decoding.
Approach:	They propose a novel decoding paradigm that drafts multiple tokens and verifies them in parallel . they aim to provide a catalyst for further research on Speculative Decoding .
Outcome:	The proposed method drafts multiple tokens and verifies them in parallel . it can be used to accelerate inference in large language models.

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Yang Zhao, Li Du, Xiao Ding, Kai Xiong, Zhouhao Sun, Shi Jun, Ting Liu, Bing Qin

Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024.emnlp-main)

Challenge:	Existing studies have suggested that the composition of the pretraining corpus exerts a significant impact upon the performance of LLMs.
Approach:	They analyze the impact of 48 datasets from 5 major categories of pretraining data of Large Language Models and measure their impacts on LLMs using benchmarks about nine major categories.
Outcome:	The proposed analysis provides insights into the organization of data to support more efficient pretraining of Large Language Models.

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Yanchen Liu, Mingyu Ma, Wenna Qin, Azure Zhou, Jiaao Chen, Weiyan Shi, Wei Wang, Diyi Yang

FIER: Fine-Grained and Efficient KV Cache Retrieval for Long-context LLM Inference (2025.findings-emnlp)

Challenge:	Existing studies on susceptibility to misinformation rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications.
Approach:	They propose a computational approach to efficiently model users’ latent susceptibility levels by using demographic factors and political ideology as inputs.
Outcome:	The proposed model shows that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation.

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Dongwei Wang, Zijie Liu, Song Wang, Yuxin Ren, Jianing Deng, Jingtong Hu, Tianlong Chen, Huanrui Yang

PersonaAgent: Bridging Memory and Action for Personalized LLM Agents (2026.findings-acl)

Challenge:	Key-Value (KV) cache reading latency increases with context lengths hindering LLM inference . important tokens are sparsely distributed across the long context, making existing retrieval inaccurate .
Approach:	They propose a method to retain a small fraction of KV cache based on token importance . important tokens are often sparsely distributed across the long context .
Outcome:	The proposed method reduces decoding latency by 1.2 to 1.5.

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Weizhi Zhang, Xinyang Zhang, Chenwei Zhang, Liangwei Yang, Jingbo Shang, Zhepei Wei, Henry Peng Zou, Zijie Huang, Zhengyang Wang, Yifan Gao, Xiaoman Pan, Lian Xiong, Jingguo Liu, Philip S. Yu, Xian Li

On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning (2026.acl-long)

Challenge:	Existing Large Language Model (LLM) enabled agents lack flexibility to respond to users’ varying needs and preferences.
Approach:	They propose a test-time user-preference alignment strategy that optimizes the persona prompt, ensuring real-time preference alignment through textual loss feedback between simulated and ground-truth responses.
Outcome:	The proposed framework outperforms baseline methods in real-time and in real applications.

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Changyu Liu, Yiyang Liu, Taowen Wang, Qiao Zhuang, James Chenhao Liang, Wenhao Yang, Renjing Xu, Qifan Wang, Dongfang Liu, Cheng Han

Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding (2022.findings-emnlp)

Challenge:	Existing vision-language-action models are unsuitable for simulated or physical-world deployments . current methods fail when confronted with inherent real-world dynamic variability.
Approach:	They propose a test-time reinforcement learning framework that enables on-the-fly policy adaptation during inference.
Outcome:	Empirical results show that the proposed framework improves adaptability, stability and task success in dynamic, previously unseen scenarios.

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Jiahao Zhu, Daizong Liu, Pan Zhou, Xing Di, Yu Cheng, Song Yang, Wenzheng Xu, Zichuan Xu, Yao Wan, Lichao Sun, Zeyu Xiong

Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs (2025.emnlp-main)

Challenge:	Existing methods for temporal sentence grounding ignore two crucial issues . 1) Boundary-bias: the video downsampling process may lose these two frames . 2) Reasoning-biases: such incorrect new boundary frames lead to the reasoning bias .
Approach:	They propose a siamese sampling mechanism to generate additional contextual frames . they use a reasoning strategy to learn the inter-relationship among these frames a .
Outcome:	Extensive experiments demonstrate the effectiveness of a new siamese sampling network on three challenging datasets.

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Dayu Yang, Tianyang Liu, Daoan Zhang, Antoine Simoulin, Xiaoyi Liu, Yuwei Cao, Zhaopu Teng, Xin Qian, Grey Yang, Jiebo Luo, Julian McAuley

Uncertainty-Guided Modal Rebalance for Hateful Memes Detection (2024.acl-long)

Challenge:	Recent breakthrough models like OpenAI-o1 and DeepSeek-R1 show powerful task-solving capabilities, particularly advances in reasoning.
Approach:	They propose future research directions that may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence.
Outcome:	The proposed research may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence.

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Chuanpeng Yang, Yaxin Liu, Fuqing Zhu, Jizhong Han, Songlin Hu

Supervised Optimism Correction: Be Confident When LLMs Are Sure (2025.findings-acl)

Challenge:	Existing methods for integrating hate information from different modalities ignore the modality uncertainty caused by the contribution degree of each modality to hate sentiment.
Approach:	They propose an Uncertainty-guided Modal Rebalance framework for hateful memes detection . they propose to combine cross-modal fusion features with unimodal features .
Outcome:	The proposed framework produces state-of-the-art performance on four widely-used datasets.

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Junjie Zhang, Rushuai Yang, Shunyu Liu, Ting-En Lin, Fei Huang, Yi Chen, Yongbin Li, Dacheng Tao

E-ViC: Reasoning Beyond Text via Embodied Visual Chain for Spatial Intelligence (2026.acl-long)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated remarkable success across diverse tasks such as instruction following, code generation, and medical diagnosis.
Approach:	They propose a supervised fine-tuning-based auxiliary loss for Q-value estimations during supervised refinement.
Outcome:	The proposed method outperforms beam search on GSM8K, MATH, and GAOKAO on reasoning benchmarks.

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Junbo Qi, Yi Zhang, Hanchu Ni, Che Liu, Zhimin Yao, Ruilin Yang, Xiancong Ren, Liangjian Wen, Wei Ge, Yuya Ieiri, Osamu Yoshie, Yong Dai, Xiaozhu Ju

Analyzing the Rapid Generalization of SFT via the Perspective of Attention Head Activation Patterns (2025.acl-long)

Challenge:	Existing Vision-Language Models (VLMs) lack spatial reasoning, despite text-based CoTs . e-ViC reframes spatial intelligence as a verifiable, tool-using capability, argues a new study.
Approach:	They propose a framework that moves reasoning beyond text into the visual domain . they ground reasoning in pixel-level interactions to enable human-like "look-and-confirm" strategies .
Outcome:	The proposed framework outperforms existing Vision-Language Models with an average gain of 10.1%.

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Yang Zhao, Li Du, Xiao Ding, Kai Xiong, Ting Liu, Bing Qin

SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)

Challenge:	Currently, LLMs learn in a data-driven schema while the instructions about complex tasks are both scarce and hard to collect or construct.
Approach:	They employ a gradient-based method to dissect the process that the Supervised Fine-tuning Process (SFT) adapts LLMs to downstream tasks via the perspective of attention patterns.
Outcome:	The proposed method dissects the process that the SFT process adapts LLMs to downstream tasks via the perspective of attention patterns.

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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing

"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations (2026.acl-long)

Challenge:	Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages.
Approach:	They propose a series of language models that specifically focuses on Southeast Asian languages.
Outcome:	SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations .

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Yang Wu, Jinhong Yu, Jingwei Xiong, Zhimin Tao, Xiaozhong Liu

Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model (C18-1)

Challenge:	Existing large language models (LLMs) are reactive and respond only when prompted, limiting their effectiveness in collaborative settings.
Approach:	They introduce a proactive LLM assistant designed to enhance biomedical collaboration between AI systems and human experts through timely, context-aware interventions.
Outcome:	The proposed model outperforms baselines in intervention precision and collaborative task utility, highlighting the potential of proactive LLMs as intelligent scientific assistants.

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Lu Ji, Zhongyu Wei, Xiangkun Hu, Yang Liu, Qi Zhang, Xuanjing Huang

Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models (2024.acl-long)

Challenge:	Existing research explores different text features of reply comments on word level and ignores interactions between participants.
Approach:	They propose a co-attention mechanism based neural network to capture interactions between participants on argument level to better model dialogical argumentation.
Outcome:	The proposed model outperforms state-of-the-art methods on a publicly available dataset showing that it extracts interactive argument pairs from the original post and the reply.

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Longze Chen, Ziqiang Liu, Wanwei He, Yinhe Zheng, Hao Sun, Yunshui Li, Run Luo, Min Yang

Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality? (2022.emnlp-main)

Challenge:	Long-context modeling capabilities are important for large language models (LLMs) however, training LLMs with long context windows is insufficient since some samples do not exhibit strong semantic dependencies across long contexts.
Approach:	They propose a data mining framework ProLong that assigns each training sample with a long dependency score and ranks and filters them according to their results.
Outcome:	The proposed framework can rank and filter training samples that exhibit more powerful long-context modeling abilities.

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Pei Zhang, Baosong Yang, Hao-Ran Wei, Dayiheng Liu, Kai Fan, Luo Si, Jun Xie

MPBoCo: Multimodal Prompt-based Boundary-enhanced Continual Framework for Joint Entity and Relation Extraction (2026.acl-long)

Challenge:	Neural machine translation models are often criticized for failures that happen without competency awareness.
Approach:	They propose a method that extends conventional NMT with a self-estimator to translate a source sentence and estimate its competency.
Outcome:	The proposed method performs on translation tasks intact and on quality estimation tasks better than existing methods.

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Guanglu Sun, Xinyu Liu, Lili Liang, Yang Yu, Fei Lang, Suxia Zhu, Ming Liu

DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via Modality-Aware Visual Reasoning (2025.emnlp-main)

Challenge:	Existing methods struggle to balance real-time adaptability and computational efficiency in continual learning scenarios.
Approach:	They propose a Continual Multimodal Entity and Relation Joint Extraction task and a Multimodal Prompt-based Boundary-enhanced Continuum framework that stores task-specific knowledge via learnable multimodal prompts.
Outcome:	The proposed framework outperforms baseline methods in real-world scenarios by 5.5% and 7.2%.

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Hang Wu, Hongkai Chen, Yujun Cai, Chang Liu, Qingwen Ye, Ming-Hsuan Yang, Yiwei Wang

Identifying and Mitigating Social Bias Knowledge in Language Models (2025.findings-naacl)

Challenge:	DiMo-GUI is a training-free framework for GUI grounding that splits input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models.
Approach:	They propose a training-free framework for GUI grounding that leverages two core strategies: dynamic visual grounding and modality-aware optimization.
Outcome:	The proposed framework splits the input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models.

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Ruizhe Chen, Yichen Li, Jianfei Yang, Yang Feng, Joey Tianyi Zhou, Jian Wu, Zuozhu Liu

ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming (2025.acl-long)

Challenge:	Existing methods for debiasing may generate incorrect or nonsensical predictions but leave aside individual commonsense facts, resulting in modified knowledge that elicits unreasonable or undesired predictions.
Approach:	They propose a framework that identifies encoding locations of biases within language models and then applies the Fairness-Stamp (FAST) they also propose 'BiaScope' to evaluate the retention of commonsense knowledge and generalization across paraphrased social biase.
Outcome:	The proposed framework surpasses state-of-the-art baselines with superior debiasing performance while not compromising the overall model capability for knowledge retention and prediction.

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Xinwei Yang, Zhaofeng Liu, Chen Huang, Jiashuai Zhang, Tong Zhang, Yifan Zhang, Wenqiang Lei

Learn and Consolidate: Continual Adaptation for Zero-Shot and Multilingual Neural Machine Translation (2023.emnlp-main)

Challenge:	Existing studies on human-LLM competitive programming use scattered, application-specific human feedback.
Approach:	They propose a taxonomy of human feedback consolidating the entire programming process, which promotes fine-grained evaluation.
Outcome:	The proposed benchmark pinpoints strengths and weaknesses of existing methods and will be openly released.

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Kaiyu Huang, Peng Li, Junpeng Liu, Maosong Sun, Yang Liu

Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)

Challenge:	Existing multilingual neural machine translation models perform poorly on language pairs with no parallel corpus.
Approach:	They propose a two-stage approach that encourages original models to acquire language-agnostic multilingual representations from new data and preserves the model architecture without introducing parameters.
Outcome:	The proposed approach improves performance in translation directions where existing models are weak and mitigates degeneration in the well-performing translation directions, offering flexibility in the real-world scenario.

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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang

RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2024.findings-acl)

Challenge:	Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level.
Approach:	They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context.
Outcome:	The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set.

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Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, Junran Peng

TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have paved the way for complex tasks such as role-playing.
Approach:	They propose a framework to benchmark, elicit, and enhance role-playing abilities in Large Language Models.
Outcome:	The proposed framework improves role-playing abilities with 168,093 samples.

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Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu, Jintao Du, Yu Cheng, Weiqiang Wang, Qiliang Liu, Zhengyang Zhou, Yang Wang

Orthogonal Representation Editing: Decoupling Semantic Entanglement in Batch Knowledge Editing of LLMs (2026.findings-acl)

Challenge:	Large Language Models (LLMs) can solve complex tasks through iterative information retrieval.
Approach:	They propose a turn-level stage-aware policy optimization approach to solve this problem . they introduce a first-occurrence latent reward mechanism to allocate partial rewards .
Outcome:	Experiments show that TSPO outperforms state-of-the-art models on Qwen2.5-3B and 7B models.

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Wenhao Yu, Zhicong Lu, Bo Lv, Fangyin Ma, Kaiwen Wei, Shihao Yang, Nayu Liu

InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions (2024.naacl-long)

Challenge:	Existing knowledge editing methods suffer from performance degradation in batch knowledge editing.
Approach:	They propose an orthogonal representation editing method which decouples semantic entanglement from edit vectors and enforcing orthogonals on edit vector.
Outcome:	The proposed method outperforms existing methods and achieves superior performance in cross-lingual knowledge editing scenarios.

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Yifan Wang, Yafei Liu, Chufan Shi, Haoling Li, Chen Chen, Haonan Lu, Yujiu Yang

DGoT: Dynamic Graph of Thoughts for Scientific Abstract Generation (2024.lrec-main)

Challenge:	In order to perform downstream tasks, Large Language Models (LLMs) need continual adaptation without catastrophic forgetting.
Approach:	They propose a new paradigm that allows for continual adaptation without catastrophic forgetting . they propose to replay previous data based on task similarity with instructions .
Outcome:	The proposed method improves performance over 16 tasks with different training orders.

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Xinyu Ning, Yutong Zhao, Yitong Liu, Hongwen Yang

FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models (2025.coling-main)

Challenge:	Existing methods for generating abstracts involve collecting domain data and training corresponding models to complete the task of text summarization.
Approach:	They propose a method to train language models based on domain datasets and a Dynamic Graph of Thought (DGoT) which inherits the advantages of existing GoT prompt approach while reducing model reasoning cost.
Outcome:	The proposed method saves the cost of model training and improves reliability due to the hallucination problem of LLMs.

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Shu Liu, Shangqing Zhao, Chenghao Jia, Xinlin Zhuang, Zhaoguang Long, Jie Zhou, Aimin Zhou, Man Lan, Yang Chong

Personalized Question Answering with User Profile Generation and Compression (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, but their proficiency and reliability in the specialized domain of financial data analysis remain uncertain.
Approach:	FinDABench is a benchmark designed to evaluate the financial data analysis capabilities of Large Language Models (LLMs) it comprises 15,200 training instances and 8,900 test instances, all meticulously crafted by human experts.
Outcome:	FinDABench measures the financial data analysis capabilities of large language models (LLMs) across three dimensions: 1) Core Ability; 2) Analytical Ability; 3) Technical Ability.

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Hang Su, Yun Yang, Tianyang Liu, Xin Liu, Peng Pu, Xuesong Lu

AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)

Challenge:	Large language models are prone to providing “midguy” answers regardless of users’ knowledge background, thereby failing to meet each user’s personalized needs.
Approach:	They propose to generate personalized answers with LLMs based on users’ past question-answering records.
Outcome:	The proposed method generates personalized answers based on user's past question-answering records.

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Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E

ICA-Proto: Iterative Cross Alignment Prototypical Network for Incremental Few-Shot Relation Classification (2023.findings-eacl)

Challenge:	AEGIS examines whether current models can effectively audit AI-generated images in academic papers.
Approach:	They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics.
Outcome:	AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis.

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Wangjie Jiang, Zhihao Ye, Bang Liu, Ruihui Zhao, Jianguang Zheng, Mengyao Li, Zhiyong Li, Yujiu Yang, Yefeng Zheng

Benchmarking Robustness of Machine Reading Comprehension Models (2021.findings-acl)

Challenge:	Existing methods for relation classification suffer from the scarcity of manually annotated data.
Approach:	They propose a novel relation classification model that incorporates query representation into the encoding of novel prototypes and utilizes iteratively to achieve more interaction.
Outcome:	The proposed model outperforms the state-of-the-art model on two benchmark datasets.

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Chenglei Si, Ziqing Yang, Yiming Cui, Wentao Ma, Ting Liu, Shijin Wang

COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)

Challenge:	Existing benchmarks only evaluate models' robustness under test-time perturbations or adversarial attacks.
Approach:	They propose a model-agnostic benchmark to evaluate models' robustness under adversarial attacks.
Outcome:	The proposed model-agnostic benchmark evaluates models under four different types of adversarial attacks.

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Boci Peng, Xiao Liu, Boren Hu, Yun Zhu, Xuanbo Fan, Yanwei Yue, Chunyu Yang, Yan Zhang

Cognitive Alpha Mining via LLM-Driven Code-Based Evolution (2026.acl-long)

Challenge:	Existing paradigms treat facts independently or employ myopic search, failing to optimize collective subgraph utility.
Approach:	They propose a framework that formalizes evidence retrieval as a constrained submodular maximization problem.
Outcome:	The proposed framework captures the trade-off between information relevance and structural complexity.

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Fengyuan Liu, Yi Huang, Sichun Luo, Yuqi Wang, Yazheng Yang, Xinye Li, Zefa Hu, Junlan Feng, Qi Liu

OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward (2026.findings-acl)

Challenge:	Existing approaches to finding effective predictive signals from financial data are limited by their complexity and low signal-to-noise ratio.
Approach:	They propose a framework that combines code-level alpha representation with LLM-driven reasoning and evolutionary search.
Outcome:	The proposed framework combines code-level alpha representation with LLM-driven reasoning and evolutionary search.

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Haoyue Yang, Xuanle Zhao, Xuexin Liu, Feibing Jiang, Yao Zhu

AdaPrompt: Adaptive Model Training for Prompt-based NLP (2022.findings-emnlp)

Challenge:	Existing studies on programmable diagram generation focus on a narrow set of tasks and languages.
Approach:	They propose a unified framework that integrates diverse diagram code languages and task definitions.
Outcome:	The proposed framework can bridge complex visual information with executable code across diverse tasks and languages.

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Yulong Chen, Yang Liu, Li Dong, Shuohang Wang, Chenguang Zhu, Michael Zeng, Yue Zhang

RACC: Regret-Aware Confidence Calibration for Consistent Masked Discrete Diffusion Decoding (2026.findings-acl)

Challenge:	Prompt-based learning can tackle zero-shot and few-shot NLP tasks . authors propose a method that makes use of pre-trained language models .
Approach:	They propose to map NLP tasks into natural language prompts, which are then filled by pre-trained language models.
Outcome:	The proposed method outperforms standard prompt-based methods in few-shot settings.

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Qinglin Zeng, Jusheng Zhang, Jing Yang, Ningyuan Liu, Keze Wang

On the Language Coverage Bias for Neural Machine Translation (2021.findings-acl)

Challenge:	Masked Discrete Diffusion Models (MDMs) enable parallel generation via iterative refinement, but their current decoding paradigms are static and myopic.
Approach:	They propose a Regret-Aware Confidence Calibration framework that aligns decoding decisions with the model’s latent self-correction capabilities.
Outcome:	The proposed framework aligns decoding decisions with model’s latent self-correction capabilities.

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Shuo Wang, Zhaopeng Tu, Zhixing Tan, Shuming Shi, Maosong Sun, Yang Liu

Event Detection without Triggers (N19-1)

Challenge:	Language coverage bias is important for neural machine translation because of the target-original training data.
Approach:	They propose two approaches to alleviate the language coverage bias problem by explicitly distinguishing between the source-and target-original training data.
Outcome:	The proposed methods improve translation tasks on both back-and forward-translation and their tagged variants.

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Shulin Liu, Yang Li, Feng Zhang, Tao Yang, Xinpeng Zhou

Small Models are Valuable Plug-ins for Large Language Models (2024.findings-acl)

Challenge:	Existing approaches to event detection require annotated triggers and event types in training data.
Approach:	They propose a framework that encodes the representation of a sentence based on target event types.
Outcome:	The proposed framework achieves competitive performances compared with state-of-the-art methods.

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Canwen Xu, Yichong Xu, Shuohang Wang, Yang Liu, Chenguang Zhu, Julian McAuley

CMIG: Conceptual Metaphor Theory-Inspired Framework for Metaphorical Image Generation (2026.findings-acl)

Challenge:	Large-scale pre-trained language models are difficult to fine-tune due to their huge weights and limited context length.
Approach:	They propose an approach which allows black-box LLMs to work with locally fine-tuned smaller models, resulting in superior performance on supervised tasks.
Outcome:	The proposed approach overcomes the challenges of poor performance and instability of In-Context Learning (ICL) while reducing the complexity of in-context learning.

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Qingbao Huang, Cheng Yang, Jiawei Yao, Zhiyue Liu, Yi Cai, Xingmao Zhang

Crafting Customisable Characters with LLMs: A Persona-Driven Role-Playing Agent Framework (2025.findings-emnlp)

Challenge:	Existing text-to-image systems often produce visually plausible but semantically literal outputs.
Approach:	They propose a structured prompting framework inspired by Conceptual Metaphor Theory . they propose to identify source–target mappings, filter projectable source attributes and select a visual realization strategy in a reproducible reasoning workflow.
Outcome:	The proposed framework improves semantic alignment and controllability on metaphor prompts.

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Bohao Yang, Dong Liu, Chenghao Xiao, Kun Zhao, Chen Tang, Chao Li, Lin Yuan, Yang Guang, Chenghua Lin

Challenge:	Large Language Models (LLMs) are capable of generating human-like text, but the potential for freely customisable characters remains underexplored.
Approach:	They propose a framework which employs Large Language Models to create freely customisable characters through personalised characteristic feature injection.
Outcome:	The proposed framework provides valuable insights for developing more accurate and customisable human simulacra.

KG-FLIP: Knowledge-guided Fashion-domain Language-Image Pre-training for E-commerce (2023.acl-industry)

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Qinjin Jia, Yang Liu, Daoping Wu, Shaoyuan Xu, Huidong Liu, Jinmiao Fu, Roland Vollgraf, Bryan Wang

MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models (2026.findings-acl)

Challenge:	Various visionlanguage pre-training (VLP) models learn cross-modal alignment from large-scale well-aligned image-text datasets without leveraging external knowledge.
Approach:	They propose a knowledge-guided fashion-domain language-image pre-training framework that learns fine-grained representations in e-commerce domain and utilizes external knowledge to improve the pre-train efficiency.
Outcome:	The proposed framework outperforms state-of-the-art models on Amazon and Fashion-Gen datasets by large margins.

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Xinming Wang, Jian Xu, Bin Yu, Sheng Lian, yi Chen, Boran Wang, Yingjian Zhu, Hongzhu Yi, Hong-Ming Yang, Han Hu, Cheng-Lin Liu, Xu-Yao Zhang

Learning to Generate Question by Asking Question: A Primal-Dual Approach with Uncommon Word Generation (2022.emnlp-main)

Challenge:	Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited.
Approach:	They propose a Meta-Reasoning informed alignment framework that quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments.
Outcome:	Empirical evaluations of four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning.

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Qifan Wang, Li Yang, Xiaojun Quan, Fuli Feng, Dongfang Liu, Zenglin Xu, Sinong Wang, Hao Ma

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)

Challenge:	Existing automatic question generation methods focus on encoding passage and answer to generate question.
Approach:	They propose an automatic question generation approach which integrates question generation with its dual problem, question answering, into a unified primal-dual framework.
Outcome:	The proposed approach outperforms existing methods on SQuAD and HotpotQA benchmarks.

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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang

Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions (2023.findings-emnlp)

Challenge:	Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence.
Approach:	They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included .
Outcome:	The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model.

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Ziyue Wang, Chi Chen, Peng Li, Yang Liu

Speeding Up Neural Machine Translation Decoding by Cube Pruning (D18-1)

Challenge:	Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge in natural language tasks.
Approach:	They propose a framework that enables LLMs to ask relevant questions to uncover more details in the image, along with filters for refining the generated information.
Outcome:	The proposed framework boosts the performance of baseline methods by 2.15% on OK-VQA and achieves consistent improvements across different LLMs.

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Wen Zhang, Liang Huang, Yang Feng, Lei Shen, Qun Liu

Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives (2024.findings-naacl)

Challenge:	Neural machine translation suffers from slow translation speed due to the large search space . a trade-off has to be made between translation quality and speed, argues a new study .
Approach:	They apply cube pruning technique to speed up dynamic programming into neural machine translation to speed it up.
Outcome:	The proposed method can translate faster on GPUs and CPUs with better translation quality than naive beam search.

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Jiaxin Liu, Yi Yang, Kar Yan Tam

AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessment (2025.emnlp-main)

Challenge:	Existing pretrained embeddings and LLM embeddables fail to discern subtle financial narrative shifts, resulting in a lack of insight for investors and regulators.
Approach:	They propose a financial domain-specific NLP task to measure nuanced semantic similarity between pairs of financial narratives.
Outcome:	The proposed method outperforms existing methods trained on classic STS tasks and generic LLM embeddings on a human-annotated dataset.

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Kun Li, Lai Man Po, Hongzheng Yang, Xuyuan Xu, Kangcheng Liu, Yuzhi Zhao

Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL (2025.findings-acl)

Challenge:	Multimodal Large Language Models are increasingly used in Personalized Image Aesthetic Assessment (PIAA) however, their predictions may reflect subtle biases influenced by demographic factors such as gender, age, and education.
Approach:	They propose to evaluate MLLMs along two complementary dimensions: (1) stereotype bias and (2) alignment between model outputs and genuine human aesthetic preferences.
Outcome:	The proposed benchmark covers three subtasks: aesthetic perception, assessment, empathy and alignment between outputs and genuine human aesthetic preferences.

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Wei Yao, Wenkai Yang, Ziqiao Wang, Yankai Lin, Yong Liu

Rethinking Word-Level Auto-Completion in Computer-Aided Translation (2023.emnlp-main)

Challenge:	Weak-to-strong generalization is a promising approach to guide stronger systems, but its effectiveness is constrained by the inherent imperfections of weak model supervision.
Approach:	They propose a theoretically grounded approach that replaces forward KL divergence with reverse KL, which prioritizes high-confidence predictions.
Outcome:	The proposed approach replaces forward KL divergence with reverse KL, reducing the influence of unreliable weak supervision.

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Xingyu Chen, Lemao Liu, Guoping Huang, Zhirui Zhang, Mingming Yang, Shuming Shi, Rui Wang

TemplateGEC: Improving Grammatical Error Correction with Detection Template (2023.acl-long)

Challenge:	Existing models for word-level auto-completion (WLAC) do not meet the criterion of good auto-completes.
Approach:	They propose a measurable criterion to address the question: what kind of words are good auto-completions? they propose an approach to enhance WLAC performance by promoting adherence to the cri-terion.
Outcome:	The proposed approach outperforms the top-performing system submitted to the WLAC shared tasks in WMT2022 while using significantly smaller model sizes.

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Yinghao Li, Xuebo Liu, Shuo Wang, Peiyuan Gong, Derek F. Wong, Yang Gao, Heyan Huang, Min Zhang

Learning to Control the Fine-grained Sentiment for Story Ending Generation (P19-1)

Challenge:	Existing methods for grammatical error correction (GEC) have been developed.
Approach:	They propose a method which integrates the detection labels from a Seq2Edit model to construct a template as the input.
Outcome:	The proposed method can perform human-in-the-loop error correction tasks.

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Fuli Luo, Damai Dai, Pengcheng Yang, Tianyu Liu, Baobao Chang, Zhifang Sui, Xu Sun

Hierarchical Transformers for Multi-Document Summarization (P19-1)

Challenge:	Existing studies focus on controlling the sentiment of story endings.
Approach:	They propose a generic and novel framework which controls fine-grained sentiment intensity for automatic story ending generation without manually annotating sentiment labels.
Outcome:	The proposed framework can generate story endings which meet the given sentiment intensity better.

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Yang Liu, Mirella Lapata

MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset (2022.naacl-main)

Challenge:	Existing models for multidocument summarization have been developed that can process multiple documents in a hierarchical manner.
Approach:	They propose a neural summarization model which can process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner.
Outcome:	The proposed model improves on the WikiSum dataset and can process multiple documents in a hierarchical manner.

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Yahui Liu, Haoping Yang, Chen Gong, Qingrong Xia, Zhenghua Li, Min Zhang

Challenge:	Recent studies show that shallow semantic role labeling (SRL) performance drops under out-of-domain setting.
Approach:	They propose to annotate a multi-domain Chinese predicate-argument dataset using a frame-free annotation methodology and strict double annotation for improving data quality.
Outcome:	The proposed dataset is compared with a dataset from six different domains.

Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)

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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng

WantWords: An Open-source Online Reverse Dictionary System (2020.emnlp-demos)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge.
Approach:	They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy.
Outcome:	The proposed model protects private data while enhancing the model's knowledge.

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Fanchao Qi, Lei Zhang, Yanhui Yang, Zhiyuan Liu, Maosong Sun

StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs (2026.findings-acl)

Challenge:	Existing reverse dictionary systems only support English reverse dictionary queries . a reverse dictionary can help people who can't remember a word from memory .
Approach:	They propose an online reverse dictionary system that outperforms other reverse dictionary systems . it supports Chinese and English-Chinese as well as Chinese-English cross-lingual reverse dictionary queries .
Outcome:	The proposed reverse dictionary outperforms other reverse dictionary systems on performance . it supports Chinese and English-Chinese as well as Chinese-English queries .

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Yang Luo, Liu Xinran, TianTian Ji, Zhiyi Yin, Lingyun Peng, Shuyu Li

PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs (2026.findings-acl)

Challenge:	Prior work focused on typographic and pixel-level perturbations, leaving the study of SCO unexplored.
Approach:	They propose a framework that exploits MLLMs' diagrammatic reasoning capabilities to bypass safety guardrails.
Outcome:	The proposed framework exploits the model's reasoning capabilities to bypass safety guardrails.

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Zijing Wang, YongKang Liu, Mingyang Wang, Ercong Nie, Deyuan Chen, Zhengjie Zhao, Shi Feng, Daling Wang, Xiaocui Yang, Yifei Zhang, Hinrich Schuetze

CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)

Challenge:	Multimodal instruction fine-tuning degrades textual reasoning capability, undermining multimodal performance.
Approach:	They propose a plateau-guided model merging method that selectively injects base language model parameters into MLLMs to mitigate this degradation.
Outcome:	The proposed framework reduces multimodal instruction fine-tuning degradation by incorporating a plateau-guided model merging method into MLLMs.

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Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu

MetaMixSpeech: Meta Task Augmentation for Low-Resource Speech Recognition (2025.findings-emnlp)

Challenge:	Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions .
Approach:	They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers .
Outcome:	The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios.

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Yaqi Chen, Hao Zhang, Wenlin Zhang, XuKui Yang, Dan Qu, Yunpeng Liu

Aligning Large Multimodal Models with Factually Augmented RLHF (2024.findings-acl)

Challenge:	Meta-learning has proven to be a powerful paradigm for improving speech recognition performance . however, multilingual meta learning also faces challenges such as task overfitting and learner overfit .
Approach:	a new method is proposed to augment meta-training tasks with "more data" the method incorporates both support and query augmentations .
Outcome:	The proposed method achieves a 6.35% improvement in the word error rate on FLEURS and Common Voice datasets.

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Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liangyan Gui, Yu-Xiong Wang, Yiming Yang, Kurt Keutzer, Trevor Darrell

Inference Compute-Optimal Video Vision Language Models (2025.acl-long)

Challenge:	Large Multimodal Models (LMMs) are built across modalities and the misalignment between two modality can result in "hallucination" . developing LMMs faces challenges such as a lack of data and a limited number of data sets.
Approach:	They propose a new algorithm that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options.
Outcome:	The proposed approach improves on the LLaVA-Bench dataset with the 96% performance level of the text-only GPT-4 and an improvement of 60% on MMHAL-BENCH over other baselines.

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Peiqi Wang, ShengYun Peng, Xuewen Zhang, Hanchao Yu, Yibo Yang, Lifu Huang, Fujun Liu, Qifan Wang

DiffPO: Diffusion-styled Preference Optimization for Inference Time Alignment of Large Language Models (2025.acl-long)

Challenge:	Using video vision language models, inference costs are often more expensive than finetuning.
Approach:	They investigate the optimal allocation of inference compute across three key scaling factors in video vision language models.
Outcome:	The proposed model configurations are based on three key scaling factors . the results can be applied to real-world tasks and tasks with fixed inference budgets.

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Ruizhe Chen, Wenhao Chai, Zhifei Yang, Xiaotian Zhang, Ziyang Wang, Tony Quek, Joey Tianyi Zhou, Soujanya Poria, Zuozhu Liu

LTRS: Improving Word Sense Disambiguation via Learning to Rank Senses (2025.coling-main)

Challenge:	Inference-time alignment approaches still face limitations due to policy-specific value functions and latency during the inference phase.
Approach:	They propose an efficient and policy-agnostic preference optimization method that avoids time latency associated with token generation.
Outcome:	The proposed method achieves a favorable trade-off between alignment quality and inference-time latency.

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Hansi Wang, Yue Wang, Qiliang Liang, Yang Liu

Entity Resolution in Open-domain Conversations (2021.naacl-industry)

Challenge:	Conventional training strategies only consider predefined senses for target words and learn each of them from relatively limited instances, neglecting the influence of similar ones.
Approach:	They propose a method to rank senses to improve the task of word Sense Disambiguation (WSD) by ranking an expanded list of sense definitions.
Outcome:	The proposed method achieves a SOTA F1 score of 79.6% in Chinese WSD and shows faster convergence than previous methods.

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Mingyue Shang, Tong Wang, Mihail Eric, Jiangning Chen, Jiyang Wang, Matthew Welch, Tiantong Deng, Akshay Grewal, Han Wang, Yue Liu, Yang Liu, Dilek Hakkani-Tur

LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit (2024.emnlp-industry)

Challenge:	Recent work on incorporating external knowledge into the response generation models has attracted great interest.
Approach:	They propose a neural entity linking approach to incorporate external knowledge into the response generation models to improve the relevancy of retrieved knowledge.
Outcome:	The proposed approach outperforms the baseline model by 62.8% relative to the baseline.

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Ruihao Gong, Yang Yong, Shiqiao Gu, Yushi Huang, Chengtao Lv, Yunchen Zhang, Dacheng Tao, Xianglong Liu

PREME: Preference-based Meeting Exploration through an Interactive Questionnaire (2023.findings-eacl)

Challenge:	Existing quantization techniques have been categorized as 'simple' and 'highly efficient' however, their configurations vary from each other and cannot be fairly compared .
Approach:	They propose a plug-and-play compression toolkit to explore the impact of quantization.
Outcome:	The proposed toolkit explores the impact of quantization on large language models.

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Negar Arabzadeh, Ali Ahmadvand, Julia Kiseleva, Yang Liu, Ahmed Hassan Awadallah, Ming Zhong, Milad Shokouhi

Challenge:	Recent studies show that providing meeting summaries does not align with current approaches to document summarization.
Approach:	They propose a framework for generating questionnaires for preference-based meeting exploration . they measure how much questions are answerable to ensure factual correctness .
Outcome:	The proposed framework provides a list of suggested questions reflecting user preferences . it measures how much questions are answerable to ensure factual correctness .

UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset (2024.acl-long)

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Haoyu Wang, Shuo Wang, Yukun Yan, Xujia Wang, Zhiyu Yang, Yuzhuang Xu, Zhenghao Liu, Liner Yang, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun

Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems (2021.naacl-industry)

Challenge:	Open-source large language models (LLMs) have gained strength across diverse fields, but the majority of studies focus on English.
Approach:	They propose a knowledge-grounded data augmentation approach to elicit more language-specific knowledge of LLMs by enhancing their ability to serve users from different countries.
Outcome:	The proposed method can prune the language-agnostic supervised fine-tuning dataset without any performance degradation.

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Tong Wang, Jiangning Chen, Mohsen Malmir, Shuyan Dong, Xin He, Han Wang, Chengwei Su, Yue Liu, Yang Liu

Revisiting a Pain in the Neck: A Semantic Reasoning Benchmark for Language Models (2026.acl-long)

Challenge:	In dialog systems, the Natural Language Understanding component makes the interpretation decision before the mentioned entities are resolved.
Approach:	They propose to leverage Entity Resolution (ER) features in NLU reranking to learn model weights . they propose a score distribution matching method to ensure the models are calibrated .
Outcome:	The proposed approach outperforms the baseline model on multiple domain evaluations.

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Yang Liu, Hongming Li, Melissa Xiaohui Qin, Chao Huang, Qiankun Liu

Socratic Style Chain-of-Thoughts Help LLMs to be a Better Reasoner (2025.findings-acl)

Challenge:	Semantic phrases (SP) are lexical combinations whose meanings or usages may not be fully derived from their individual components.
Approach:	They propose to consolidate existing multiword expression resources into a unified testbed to assess language models in semantic phrase processing tasks.
Outcome:	The evaluation suite covers idiomatic expressions, noun compounds, and verbal constructions.

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Jiangbo Pei, Peiyu Liu, Xin Zhao, Aidong Men, Yang Liu

ECHO-LLaMA: Efficient Caching for High-Performance LLaMA Training (2025.emnlp-industry)

Challenge:	Synthetic data generation is a promising approach to enhance reasoning capabilities of large language models.
Approach:	They propose a multi-agent debate framework based on the Socratic questioning strategy . they use socratic questions to deepen the thinking process and broaden it to motivate self-reflection .
Outcome:	The proposed framework outperforms existing methods on math and code generation tasks while maintaining affordable costs.

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Maryam Dialameh, Rezaul Karim, Hossein Rajabzadeh, Omar Mohamed Awad, Boxing Chen, Hyock Ju Kwon, Walid Ahmed, Yang Liu

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)

Challenge:	ECHO-LLaMA transforms LLa MA models into shared KV caching across certain layers, significantly reducing KV computational complexity while maintaining or improving language performance.
Approach:	They propose an efficient LLaMA architecture that transforms LLama models into shared KV caching across certain layers, reducing computational complexity while maintaining or improving language performance.
Outcome:	ECHO-LLaMA achieves up to 77% higher token-per-second throughput during training, up to 16% higher Model FLOPs Utilization (MFU) and up to 14% lower loss when trained on an equal number of tokens.

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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu

Long Text Generation with Topic-aware Discrete Latent Variable Model (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering.
Approach:	They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow.
Outcome:	The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow.

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Erguang Yang, Mingtong Liu, Deyi Xiong, Yujie Zhang, Yufeng Chen, Jinan Xu

Enhancing Explainable Rating Prediction through Annotated Macro Concepts (2024.acl-long)

Challenge:	Recent work focuses on the modeling of discourse relation, resulting in discrete codes learning shallow semantics.
Approach:	They propose a topic-aware latent code-guided text generation model that encourages discrete codes to model information about topics.
Outcome:	The proposed model generates more topic-relevant and coherent texts.

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Huachi Zhou, Shuang Zhou, Hao Chen, Ninghao Liu, Fan Yang, Xiao Huang

Self-Knowledge Guided Retrieval Augmentation for Large Language Models (2023.findings-emnlp)

Challenge:	Existing models learn user and item embeddings and generate reasons based on these embedds.
Approach:	They propose a concept-based explanation framework that leverages macro concepts to bridge the gap between the user/item embeddings and the recommendation reasons.
Outcome:	Extensive experiments on three datasets prove the proposed model is superior to existing models.

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Yile Wang, Peng Li, Maosong Sun, Yang Liu

TInR: Exploring Tool-Internalized Reasoning in Large Language Models (2026.acl-long)

Challenge:	Large language models (LLMs) have shown superior performance without task-specific fine-tuning due to the computational costs.
Approach:	They propose a method which lets LLMs refer to the questions they have previously encountered and adaptively call for external resources when dealing with new questions.
Outcome:	The proposed method outperforms chain-of-thought based and fully retrieval-based methods on multiple datasets and outperformed chain- of-though, chatGPT and InstructGPT.

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Qiancheng Xu, Yongqi Li, Fan Liu, Hongru Wang, Min Yang, Wenjie Li

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)

Challenge:	Existing methods rely on external tool documentation during reasoning, leading to tool mastery difficulty, tool size constraints, and inference inefficiency.
Approach:	They propose a tool-internalized reasoning framework for unified reasoning and tool usage that integrates external tools into Large Language Models (LLMs) to address these issues, they propose 'tool-internet-based' reasoning.
Outcome:	The proposed method achieves superior performance across in-domain and out-of-domain settings, highlighting its effectiveness and efficiency.

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Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao

KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding (2025.findings-acl)

Challenge:	Existing benchmarks focus on well-defined or abstract reasoning and fail to capture real-world engineering problems.
Approach:	They propose a hierarchical benchmark to evaluate large language models on engineering problems.
Outcome:	The proposed model performs well under well-defined conditions and is based on three levels of difficulty and covers diverse engineering subfields.

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Zhangchen Xu, Yang Liu, Yueqin Yin, Mingyuan Zhou, Radha Poovendran

EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction (2024.findings-emnlp)

Challenge:	Existing code-focused resources typically fail to ensure either the breadth of coverage or verifiable correctness.
Approach:	They propose a synthetic dataset that provides high-quality, verifiable training data for Large Language Models for coding.
Outcome:	The proposed dataset surpasses Qwen2.5-Coder-32B-Instruct and DeepSeek-R1-Distill-Llama-70B in performance on coding benchmarks.

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Li Yang, Qifan Wang, Jianfeng Chi, Jiahao Liu, Jingang Wang, Fuli Feng, Zenglin Xu, Yi Fang, Lifu Huang, Dongfang Liu

Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives (2026.findings-eacl)

Challenge:	Existing methods to extract product attribute value require multiple extractions to obtain all corresponding values.
Approach:	They propose an Efficient product Attribute Value Extraction approach using lightweight sparse-layer interaction.
Outcome:	The proposed method achieves significant efficiency gains with neutral or marginal loss in performance when the context is long and number of attributes is large.

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Yajiao Liu, Congliang Chen, Junchi Yang, Ruoyu Sun

Understanding the Prompt Sensitivity (2026.acl-long)

Challenge:	Existing methods for data mixture improve the generalization capability of large language models (LLMs) on downstream tasks.
Approach:	They propose a fine-grained categorization of existing methods and propose three subtypes of offline and online methods.
Outcome:	The proposed methods extend beyond offline and online classifications and highlight key challenges in the field of data mixture.

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Yang Liu, Chenhui Chu

Towards Real-world Scenario: Imbalanced New Intent Discovery (2024.acl-long)

Challenge:	Prompt sensitivity is a measure of how strongly the output of a large language model (LLM) depends on the exact wording of its input prompt.
Approach:	They consider LLMs as multivariate functions and perform a first-order Taylor expansion to analyze the relationship between meaning-preserving prompts, their gradients, and log probabilities of the model’s next token.
Outcome:	The proposed model disperses meaning-preserving inputs, making it difficult to reduce to 0. The proposed models also dispersing prompt variants are more likely to introduce prompt sensitivity risks in LLMs.

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Shun Zhang, Yan Chaoran, Jian Yang, Jiaheng Liu, Ying Mo, Jiaqi Bai, Tongliang Li, Zhoujun Li

Hero-Gang Neural Model For Named Entity Recognition (2022.naacl-main)

Challenge:	Existing studies focus on detecting known and previously undefined categories of user intent . skewed and long-tailed distributions often encountered in open-world scenarios .
Approach:	They propose to use imbalanced new intent discovery task to identify familiar and novel intent categories within long-tailed distributions.
Outcome:	The proposed model outperforms the existing benchmark on three datasets to simulate the real-world long-tail distributions.

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Jinpeng Hu, Yaling Shen, Yang Liu, Xiang Wan, Tsung-Hui Chang

Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts (2026.tacl-1)

Challenge:	Named entity recognition (NER) is a fundamental and important task in natural language processing.
Approach:	They propose a novel Hero-Gang Neural structure to leverage both global and local information to promote NER by using a Transformer-based encoder and a Gang module.
Outcome:	The proposed model can extract local features and position information from the Hero and Gang modules, and it performs on multiple datasets.

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Hanhua Hong, Chenghao Xiao, Yang Wang, Yiqi Liu, Wenge Rong, Chenghua Lin

InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT (2023.findings-emnlp)

Challenge:	Evaluating natural language generation systems is challenging due to the diversity of valid outputs.
Approach:	They propose an inversion learning method that learns effective reverse mappings from model outputs back to their input instructions.
Outcome:	The proposed method requires only a single evaluation sample and eliminates manual prompt engineering.

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Yichong Xu, Ruochen Xu, Dan Iter, Yang Liu, Shuohang Wang, Chenguang Zhu, Michael Zeng

QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning (2026.acl-long)

Challenge:	Recent advances in large language models have revolutionized the way summarization is generated.
Approach:	They propose a summarization model derived from GPT-3.5 through distillation that is compact and has comparable summarizing capabilities to GPT-3.
Outcome:	The proposed model outperforms the established best small models in prefix-tuning and full-data fine-tuned scenarios.

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Songxin Qu, Tai-Ping Sun, Yun-Jie Wang, Huan-Yu Liu, Cheng Xue, Xiao-Fan Xu, Han Fang, Yang Yang, Yu-Chun Wu, Guo-Ping Guo, Zhao-Yun Chen

MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction (P19-1)

Challenge:	Large language models lack reliability in scientific domains that require strict adherence to physical constraints.
Approach:	They propose a large-scale dataset constructed via a task-adaptive strategy and a hybrid verification protocol that combines deterministic solvers with semantic auditing to guarantee scientific rigor.
Outcome:	The proposed model outperforms baselines and general-purpose preference models and is competitive with proprietary models.

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Pengcheng Yang, Fuli Luo, Peng Chen, Tianyu Liu, Xu Sun

Improving the Transformer Translation Model with Document-Level Context (D18-1)

Challenge:	Existing work shows that morphological variation is an intractable challenge for the unsupervised bilingual lexicon induction task.
Approach:	They propose a morphology-aware alignment model to alleviate the adverse effect of morphological variation by introducing grammatical information learned by the pre-trained denoising language model.
Outcome:	The proposed model outperforms state-of-the-art unsupervised systems and achieves competitive performance compared to supervised methods.

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Jiacheng Zhang, Huanbo Luan, Maosong Sun, Feifei Zhai, Jingfang Xu, Min Zhang, Yang Liu

Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning (2026.acl-long)

Challenge:	Existing models for document-level context translation ignore documentlevel context.
Approach:	They propose a document-level context encoder to represent document- level context and integrate it into the Transformer model.
Outcome:	Experiments on NIST Chinese-English and IWSLT French-English datasets show that the proposed translation model outperforms the Transformer model significantly.

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Junpeng Ding, Zichen Tang, Haihong E, Mengyuan Ji, Yang Liu, Haolin Tian, Haiyang Sun, Pengqi Sun, Yang Xu, Yichen Liu, Haocheng Gao, Zijie Xi, Ruomeng Jiang, Peizhi Zhao, Rongjin Li, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Jintong Chen, Siying Lin

DataLab: A Platform for Data Analysis and Intervention (2022.acl-demo)

Challenge:	Xu and Peng, 2025) . . SPUR is a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images.
Approach:	They propose to use 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images to evaluate the visual perception of multimodal large language models (MLLMs) . they also propose to utilize cross-panel relation understanding to evaluate MLLM’s ability to decipher intricate cross-panel relations.
Outcome:	The proposed model is based on 4,264 question-answering pairs derived from 1,084 expert-curated images.

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Yang Xiao, Jinlan Fu, Weizhe Yuan, Vijay Viswanathan, Zhoumianze Liu, Yixin Liu, Graham Neubig, Pengfei Liu

Self-Evaluation of Large Language Model based on Glass-box Features (2024.findings-emnlp)

Challenge:	Existing tools and research focus on how to interpret and manipulate data, despite its crucial role in machine learning, . existing tools and researchers focus on systems on top of existing data, rather than how to use it.
Approach:	They propose a unified data-oriented platform that allows users to interactively analyze the characteristics of data and provides a standard interface for many data processing operations.
Outcome:	The proposed platform allows users to analyze the characteristics of data and provides a standardized interface so that many data processing operations can be provided within a single interface.

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Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, Wenpeng Lu

OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment (2026.acl-long)

Challenge:	Existing evaluation methods rely on external evaluators, focusing on training and prompting strategies, but model-aware glass-box features are overlooked.
Approach:	They propose to use model-aware glass-box features to evaluate an LLM's output.
Outcome:	The proposed model-aware features are reliable quality indicators for self-evaluation on public benchmarks.

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Tianci Liu, Ran Xu, Tony Yu, Ilgee Hong, Carl Yang, Tuo Zhao, Haoyu Wang

Challenge:	Existing reward models rely on scalar or pairwise judgments that fail to capture multifaceted nature of human preferences.
Approach:	They propose a rubric-based reward model that uses a large collection of prompt, rubric pairs to generate a scalar score or preference label for each response.
Outcome:	The proposed model surpasses strong size-matched baselines by 8.4% across multiple benchmarks.

From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent (2026.acl-long)

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Yucheng Wang, Shen Yang, Jifan Yu, Haoxuan Li, Joy Jia Yin Lim, Daniel Zhang-Li, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu

PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models (2023.findings-acl)

Challenge:	Large language models produce content lacking pedagogical depth when asked to generate lessons .
Approach:	They propose a framework that allows teachers to select content according to pedagogical intent and sequence topics so foundations precede applications.
Outcome:	The framework achieves 67.8% win rate in human evaluation and 79.6% in LLM-based evaluation against eight baselines.

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Zhuocheng Gong, Jiahao Liu, Qifan Wang, Yang Yang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Rui Yan

Take Its Essence, Discard Its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect (2024.lrec-main)

Challenge:	Quantization is a viable solution for pre-trained language models, but most existing methods are task-specific and require customized training and quantization with a large number of trainable parameters.
Approach:	They propose a "quantize before fine-tuning" framework that allows for quantization with a large number of trainable parameters on each individual task.
Outcome:	The proposed framework is compatible with quantization-aware training and post-training quantization and corrects quantization errors.

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Junyu Lu, Bo Xu, Xiaokun Zhang, Kaiyuan Liu, Dongyu Zhang, Liang Yang, Hongfei Lin

Exploring Layer Activation Dynamic of CoT via Knowledge Probe (2026.acl-long)

Challenge:	Existing methods to mitigate lexical bias in toxic language detection (TLD) do not exploit the “useful” and “misleading” impact of the bias.
Approach:	They propose a counterfactual Causal Debiasing Framework to mitigate lexical bias in toxic language detection (TLD) it preserves the “useful impact” of lexical bias and eliminates the "misleading impact" they propose to use the same framework to analyze the causal effect of a sentence and bias tokens .
Outcome:	The proposed framework preserves the “useful impact” of lexical bias and eliminates the ‘misleading impact’ Empirical evaluations show that the proposed model outperforms current debiased models for out-of-distribution data.

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Chuanxin Zhang, Jiajun Liu, Yao He, Wenjun Ke, Peng Wang, Yankun Le, Sirui Liu, Zhaoyu Yang

Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer (2023.findings-acl)

Challenge:	Chain-of-thought reasoning has emerged as a crucial paradigm for multi-step reasoning tasks.
Approach:	They propose a multi-stage probing framework that enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution.
Outcome:	The proposed framework enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution.

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Shuang Li, Xuming Hu, Aiwei Liu, Yawen Yang, Fukun Ma, Philip S. Yu, Lijie Wen

NEAT: Neuron-Based Early Exit for Large Reasoning Models (2026.findings-acl)

Challenge:	Existing approaches to cross-lingual natural language inference lack annotated parallel corpora.
Approach:	They propose a new prompt learning framework with the Multilingual Verbalizer for XNLI that uses a multilingual verbalizer to align the representations of original and augmented multilingual questions into a unified semantic space with consistency regularization.
Outcome:	The proposed framework outperforms existing methods under few-shot and full-shot cross-lingual transfer settings.

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Kang Liu, YongKang Liu, Xiaocui Yang, Peidong Wang, Wen Zhang, Shi Feng, Yifei Zhang, Daling Wang

VecInfer: Efficient LLM Inference with Low-Bit KV Cache via Outlier-Suppressed Vector Quantization (2026.acl-long)

Challenge:	Existing approaches to reduce overthinking require additional rollout computation or externally labeled datasets.
Approach:	They propose a Neuron-based Early reAsoning exiT framework that monitors neuron-level activation dynamics to enable training-free early exits.
Outcome:	The proposed framework reduces the amount of reasoning steps generated by LRMs while maintaining accuracy.

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Dingyu Yao, Chenxu Yang, Zhengyang Tong, Zheng Lin, Wei Liu, Jian Luan, Weiping Wang

Think in Sentences: Explicit Sentence Boundaries Enhance Language Model’s Capabilities (2026.acl-long)

Challenge:	Existing quantization methods for large language models suffer performance degradation at ultra-low bit-widths due to key cache outliers.
Approach:	They propose a vector quantization method that suppresses outliers in the key cache and reduces memory access overhead.
Outcome:	The proposed method outperforms baseline quantization methods across long-context understanding and mathematical reasoning tasks while minimizing memory access overhead.

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Zhichen Liu, Yongyuan Li, Yang Xu

Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models (2024.findings-emnlp)

Challenge:	Existing studies focus on dummy tokens but fail to leverage the inherent sentence-level structure of natural language.
Approach:	They propose a method that inserts delimiters at sentence boundaries to enhance large language models' capabilities.
Outcome:	The proposed method improves performance on 7B LLMs to 600B Deepseek-V3 with 7.7% gains on GSM8k and 12.5% on DROP.

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Jiaming Li, Lei Zhang, Yunshui Li, Ziqiang Liu, Yuelin Bai, Run Luo, Longze Chen, Min Yang

WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metrics (2026.acl-long)

Challenge:	Large language models struggle to meet user’s needs when required to generate responses of a specific length due to their inherent difficulty in accurately perceiving numerical constraints.
Approach:	They propose a Target Length Generation Task and propose RULER, a model-agnostic approach that controls generated length for large language models.
Outcome:	The proposed model-agnostic approach improves instruction-following ability of large language models under length-constrained instructions and can generate appropriate MLT when length constraints are not explicitly provided.

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Chenxu Liu, Yingjie Fu, Wei Yang, Ying Zhang, Tao Xie

NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation (2023.acl-long)

Challenge:	Web applications (web apps) are a key arena for large language models to demonstrate their code generation capabilities and commercial potential.
Approach:	a new benchmark for large language models (LLMs) is designed to provide real-world user requirements and generalizable evaluation metrics.
Outcome:	a new benchmark for large language models (LLMs) provides a real-world, generalizable, and interpretable evaluation score . the benchmark measures user requirements, expression styles and human-preference-aligned weights . a web application can be used to demonstrate its commercial potential, authors say .

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Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Ming Gong, Lijuan Wang, Zicheng Liu, Houqiang Li, Nan Duan

AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models (2026.findings-acl)

Challenge:	Existing work generates long videos segment by segment sequentially, which is inefficient.
Approach:	They propose a Diffusion over Difference architecture for eXtremely Long video generation.
Outcome:	The proposed architecture reduces the average inference time from 7.55min to 26s (94.26%) and generates high-quality long videos with both global and local coherence.

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Feng Luo, Yu-Neng Chuang, Guanchu Wang, Hoang Anh Duy Le, Shaochen Zhong, Hongyi Liu, Jiayi Yuan, Yang Sui, Vladimir Braverman, Vipin Chaudhary, Xia Hu

Context Consistency between Training and Inference in Simultaneous Machine Translation (2024.acl-long)

Challenge:	Existing approaches to distilling large language models (LLMs) are inefficient and generate excessively long chain-of-thought reasoning even for inputs that admit concise solutions.
Approach:	They propose a distillation framework that empowers non-reasoning LLMs to think only when necessary.
Outcome:	The proposed framework reduces reasoning length up to 71% with minimal accuracy loss while preserving accuracy.

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Meizhi Zhong, Lemao Liu, Kehai Chen, Mingming Yang, Min Zhang

PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization (2022.coling-1)

Challenge:	Simultaneous machine translation (SiMT) aims to yield a partial translation with a monotonically growing source-side context.
Approach:	They propose a training approach that encourages consistent context usage between training and inference by optimizing translation quality and latency as bi-objectives and exposing the predictions to the model during the training.
Outcome:	The proposed system outperforms existing SiMT systems with context inconsistency for the first time.

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Xiaochen Liu, Yang Gao, Yu Bai, Jiawei Li, Yinan Hu, Heyan Huang, Boxing Chen

Bridging the Gap between Training and Inference for Neural Machine Translation (P19-1)

Challenge:	Experimental results show that our method outperforms full-model tuning in few-shot abstractive summarization tasks.
Approach:	They propose a soft prompts architecture with prompt pre-training and prompt fine-tuning paradigm to support few-shot abstractive summarization.
Outcome:	The proposed model outperforms Prompt Tuning and Profix-Tuning on CNN/DailyMail and XSum datasets and outperfies Profix Tuning by a large margin.

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Wen Zhang, Yang Feng, Fandong Meng, Di You, Qun Liu

COSMIC: Generalized Refusal Direction Identification in LLM Activations (2025.findings-acl)

Challenge:	Neural Machine Translation generates target words sequentially while at inference it has to generate the entire sequence from scratch.
Approach:	They propose to use ground truth and inference to generate target words sequentially while at inference it has to generate the entire sequence from scratch.
Outcome:	Experiments on Chinese->English and WMT’14 English->German translation tasks show that the proposed model can achieve significant improvements on multiple datasets.

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Vincent Siu, Nicholas Crispino, Zihao Yu, Sam Pan, Zhun Wang, Yang Liu, Dawn Song, Chenguang Wang

Challenge:	Existing methods depend on predefined refusal templates detectable in output tokens or manual review.
Approach:	They propose a framework that optimally identifies steering directions and target layers using cosine similarity, entirely independent of output text.
Outcome:	The proposed framework achieves comparable steering effectiveness without any prior knowledge or assumptions of a model’s refusal behavior such as the use of certain refusal tokens.

Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings (P18-1)

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Yang Xu, Jiawei Liu, Wei Yang, Liusheng Huang

LMGQS: A Large-scale Dataset for Query-focused Summarization (2023.findings-emnlp)

Challenge:	Existing word embedding methods learn semantic information at word level while neglecting meaningful inner structures of words like morphemes.
Approach:	They propose to use latent meanings of morphological compositions of words to train word embeddings.
Outcome:	The proposed models outperform baseline models on word similarity, syntactic analogy and text classification tasks.

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Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Pengcheng He, Chenguang Zhu, Michael Zeng

SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications (D18-1)

Challenge:	Lack of large-scale datasets for query-focused summarization hinders model development . lack of data limits the ability of QFS models to train robust neural models .
Approach:	They propose to generate a query for each summary sentence in a generic summarization annotation using a pretrained language model.
Outcome:	The proposed model achieves state-of-the-art zero-shot and supervised performance on multiple existing QFS benchmarks.

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Zexuan Zhong, Jiaqi Guo, Wei Yang, Jian Peng, Tao Xie, Jian-Guang Lou, Ting Liu, Dongmei Zhang

Towards Scalable Lightweight GUI Agents via Multi-role Orchestration (2026.findings-acl)

Challenge:	Existing approaches to generate programs from natural language do not address program aliasing . semantically equivalent programs may have many syntactically different forms .
Approach:	They propose a semantics-based approach to generate regular expressions from natural language.
Outcome:	The proposed approach improves on three public datasets.

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Ziwei Wang, Junjie Zheng, Leyang Yang, Sheng Zhou, Xiaoxuan Tang, Fang Zhouhua, Zhiwei Liu, Dajun Chen, Yong Li, Jiajun Bu

Challenge:	Advanced GUI agents suffer from prohibitive deployment costs on resource-constrained devices.
Approach:	They propose a lightweight GUI agent with GUI-specific knowledge and task scalability . LAMO-3B supports monolithic execution and MAS-style orchestration .
Outcome:	The proposed GUI agent LAMO-3B supports monolithic execution and MAS-style orchestration.

POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion (2025.emnlp-main)

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Yuan Liu, Zhongyin Zhao, Le Tian, Haicheng Wang, Xubing Ye, Yangxiu You, Zilin Yu, Chuhan Wu, Zhou Xiao, Yang Yu, Jie Zhou

OSCR-Attack: One-Shot Character Level Attacks through Self-Optimizing Continuous Relaxation (2026.findings-acl)

Challenge:	Existing approaches to training document conversion models with manual annotation are costly and time-consuming, and training student models by distilling outputs from teacher models can significantly limit their performance in real-world applications.
Approach:	They propose a fully automated framework for constructing high-quality document extraction datasets and models capable of handling diverse document formats and layouts.
Outcome:	The proposed model outperforms existing models and improves on annotated documents.

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Lingyi Kong, Zhuo Liu, Zhanghao Hu, Qilong Qiu, Yutao Yang, Jingjing Xue, Zheng Wang, Lin Gui, Feiping Nie

DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue (2023.acl-long)

Challenge:	Character-level adversarial attacks preserve semantics but are costly and inefficient . generative LLMs are gaining popularity due to their uncertainty and vulnerability to textual adversarials .
Approach:	They propose an end-to-end framework that transforms discrete choices into continuous representations and a conflict resolution strategy that maps them back into discrete insertion operations.
Outcome:	The proposed framework improves ASR by 21.45% points and accelerates the attack by 3.66 times compared to baselines.

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William Held, Christopher Hidey, Fei Liu, Eric Zhu, Rahul Goel, Diyi Yang, Rushin Shah

Alternated Training with Synthetic and Authentic Data for Neural Machine Translation (2021.findings-acl)

Challenge:	Existing studies show that multilingual models are less robust for semantic parsing compared to other tasks.
Approach:	They propose a constrained optimization technique to optimize multilingual parsing systems for multilingual use.
Outcome:	The proposed technique outperforms XLM-R and mT5-Large on three benchmarks and significantly outperformed other models.

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Rui Jiao, Zonghan Yang, Maosong Sun, Yang Liu

CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation (2023.emnlp-main)

Challenge:	Existing approaches to synthesizing data in NMT focus on leveraging monolingual data in training.
Approach:	They propose alternated training with synthetic and authentic data to improve NMT models' performance.
Outcome:	The proposed approach improves Chinese-English and German-English translation tasks over strong baselines.

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Minzhi Li, Taiwei Shi, Caleb Ziems, Min-Yen Kan, Nancy Chen, Zhengyuan Liu, Diyi Yang

Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping (2026.findings-acl)

Challenge:	Annotated data plays a critical role in training models and evaluating their performance.
Approach:	They propose a paradigm for Human-LLM co-annotation of unstructured texts at scale that utilizes uncertainty to estimate LLMs’ annotation capability.
Outcome:	The proposed model outperforms existing models on many text-annotation tasks with up to 21% performance improvement over random baseline.

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Yao Chen, Yilong Chen, Yinqi Yang, Junyuan Shang, Zhenyu Zhang, Zefeng Zhang, Shuaiyi Nie, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang, Tingwen Liu

Entropy-Based Vocabulary Substitution for Incremental Learning in Multilingual Neural Machine Translation (2022.emnlp-main)

Challenge:	Existing approaches to increasing effective depth of LLMs rely on parameter reuse, extending computation through recursive execution.
Approach:	They propose a training-time sparse depth allocation framework that progressively increases depth for a small subset of parameters as training evolves.
Outcome:	The proposed model outperforms existing approaches to increasing the effective depth of language models while reducing training FLOPs overhead from approximately 16–20% to only 1–3% relative to a standard Transformer backbone.

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Kaiyu Huang, Peng Li, Jin Ma, Yang Liu

Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation (2025.findings-emnlp)

Challenge:	Existing methods to update a multilingual model with new language pairs are expensive and time-consuming.
Approach:	They propose an entropy-based vocabulary substitution method that walks through new language pairs for incremental learning while remaining the size of the original vocabulary.
Outcome:	The proposed method achieves better performance and saves excess overhead in a multilingual machine translation task.

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Weitao Li, Xiangyu Zhang, Kaiming Liu, Xuanyu Lei, Weizhi Ma, Yang Liu

Calibrating LLM-Based Evaluator (2024.lrec-main)

Challenge:	RAG implementations face challenges in addressing retrieved noise and redundant content . current RAG methods lack the ability to exploit fine-grained inter-document relationships .
Approach:	They propose a retrieval-augmented generation framework that exploits latent inter-document relationships while removing irrelevant information and redundant content.
Outcome:	The proposed framework achieves consistent performance improvements on knowledge-QA and hallucination-Detection datasets.

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Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang

COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis (2025.findings-naacl)

Challenge:	Existing models for large language models lack the ability to calibrate their outputs towards human preference.
Approach:	They propose a multi-stage, gradient-free approach to calibrate an LLM-based evaluator toward human preference.
Outcome:	The proposed approach improves correlation with expert evaluation on multiple text quality evaluation datasets.

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Weiqing Yang, Hanbin Wang, Zhenghao Liu, Xinze Li, Yukun Yan, Shuo Wang, Yu Gu, Minghe Yu, Zhiyuan Liu, Ge Yu

Generative Entity Typing with Curriculum Learning (2022.emnlp-main)

Challenge:	Existing code debugging benchmarks focus on the Code Repair stage of the code generation process.
Approach:	They propose a framework to evaluate the debugging abilities of large language models by emulating the human debug process.
Outcome:	The proposed framework outperforms human-curated and GPT-4-generated training data, enabling 7B-scale LLMs to achieve comparable debugging performance to GPT-3.5.

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Siyu Yuan, Deqing Yang, Jiaqing Liang, Zhixu Li, Jinxi Liu, Jingyue Huang, Yanghua Xiao

Language Models Resist Alignment: Evidence From Data Compression (2025.acl-long)

Challenge:	Entity typing fails to assign an entity to the types beyond the predefined type set.
Approach:	They propose a generative entity typing paradigm that assigns types to entities . traditional classification-based approaches fail to assign entities to the types beyond the predefined set . they employ curriculum learning to train the model on heterogeneous data .
Outcome:	The proposed model outperforms the state-of-the-art model on heterogeneous training data.

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Jiaming Ji, Kaile Wang, Tianyi Alex Qiu, Boyuan Chen, Jiayi Zhou, Changye Li, Hantao Lou, Josef Dai, Yunhuai Liu, Yaodong Yang

Learning from Textual Radiology Reports: A Benchmark Dataset for Coronary CT Angiography (2026.acl-industry)

Challenge:	Large language models (LLMs) may exhibit undesirable behaviors due to the inevitable biases and harmful content present in training.
Approach:	They propose to investigate the elasticity of large language models by examining their performance.
Outcome:	The proposed model performance declines rapidly before reverting to the pre-training distribution, the authors show . the proposed model weight and code are available at pku-lm-res ist-alignment.github.io.

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Sudharshan Balaji, Zhiyu Liu, Zhengyuan Jiang, Shuo Lei, Yimin Chen, Yang Xiao, Shone O. Almeida, Mathew Joseph Karivelil, Christopher Malanga, Ning Wang

HeteroSpec: Leveraging Contextual Heterogeneity for Efficient Speculative Decoding (2026.acl-long)

Challenge:	CCTA reports provide an assessment of coronary disease severity to guide patient management.
Approach:	They propose a pipeline that decouples structuring from classification by an LLM-based parser . CCTA-RADS is the largest publicly available dataset of CCDA reports .
Outcome:	The proposed approach improves the F1-score by 6%-13% compared with direct methods.

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Siran Liu, Yang Ye, Qianchao Zhu, Zane Cao, Yongchao He

ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data (2025.acl-long)

Challenge:	Autoregressive decoding limits the inference throughput of Large Language Models due to its sequential dependency.
Approach:	They propose a framework that allocates verification effort in proportion to candidate uncertainty.
Outcome:	Speculative decoding achieves an average speedup over state-of-the-art methods . a small subset of high-confidence predictions accounts for most successful verifications .

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Yu Zhang, Ruijie Yu, Jidong Tian, Feng Zhu, Jiapeng Liu, Xiaokang Yang, Yaohui Jin, Yanyan Xu

Event Detection from Social Media for Epidemic Prediction (2024.naacl-long)

Challenge:	Existing methods for extracting chemical procedures from literature are insufficient and low-quality due to the inherent ambiguity of chemical language and the high cost of human annotation.
Approach:	They propose a fully fine-tuned large language model (LLM) as a chemical executor to convert between unstructured experimental procedures and structured action sequences.
Outcome:	The proposed model outperforms the baseline model on R2D and D2A tasks by 10%.

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Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, Kai-Wei Chang

HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs (2026.findings-acl)

Challenge:	Social media is an easy-to-access platform providing timely updates about societal trends and events.
Approach:	They propose a framework to extract epidemic-related events from social media posts to provide early warnings.
Outcome:	The proposed framework can detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue while existing models fail miserably.

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Ru Peng, Tianyu Zhao, Xijun Gu, Zhiting Fan, Haokai Xu, Jinyang Zhang, Yawen Zeng, Yihong Zhuang, Kexin Yang, Junyang Lin, Dayiheng Liu, Junbo Zhao

Challenge:	High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly.
Approach:	They define the first HSS domain system covering 14 mainstream fields and introduce HSS-Synth.
Outcome:	the proposed pipeline outperforms 14 leading baselines on 16 benchmarks.

Quantification of Large Language Model Distillation (2025.acl-long)

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Sunbowen Lee, Junting Zhou, Chang Ao, Kaige Li, Xeron Du, Sirui He, Haihong Wu, Tianci Liu, Jiaheng Liu, Hamid Alinejad-Rokny, Min Yang, Yitao Liang, Zhoufutu Wen, Shiwen Ni

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

Challenge:	Existing studies have revealed the robustness degra-dation caused by data distillation.
Approach:	They propose a framework to evaluate and quantify model distillation . they aim to identify identity cognition contradictions and analyse multi-granularity response similarities across models to measure the extent of homogenization.
Outcome:	The proposed framework addresses two key aspects: (1) Identifying identity cognition contradictions to assess discrepancies in how models perceive and represent identity-related information; (2) Analyzing multi-granularity response similarities across models to measure the extent of homogenization.

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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou

MQuinE: a Cure for “Z-paradox” in Knowledge Graph Embedding (2024.emnlp-main)

Challenge:	a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say .
Approach:	They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible .
Outcome:	The proposed framework achieves state-of-the-art performance among open-source projects.

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Yang Liu, Huang Fang, Yunfeng Cai, Mingming Sun

Efficiently Editing Mixture-of-Experts Models with Compressed Experts (2025.findings-emnlp)

Challenge:	Existing knowledge graph embedding models suffer from Z-paradox, a deficiency in expressiveness . Embedding-based models map each entity and relation into a vector or matrix .
Approach:	They propose a new knowledge graph embedding model that does not suffer from Z-paradox while preserves strong expressiveness to model various relation patterns with theoretical justification.
Outcome:	The proposed model outperforms existing models on link prediction tasks while maintaining strong expressiveness.

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Yifei He, Yang Liu, Chen Liang, Hany Hassan Awadalla

POS-Constrained Parallel Decoding for Non-autoregressive Generation (2021.acl-long)

Challenge:	Mixture-of-Experts models allow for efficient scaling of large language models . fewer experts reduce computational costs, while more experts improve performance .
Approach:	They propose to activate only a subset of experts during training and inference . they propose compressed experts that preserve the most important experts .
Outcome:	The proposed approach preserves the most important experts while replacing other auxiliary activated experts with compressed experts.

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Kexin Yang, Wenqiang Lei, Dayiheng Liu, Weizhen Qi, Jiancheng Lv

Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion (2025.coling-main)

Challenge:	Existing non-autoregressive generation systems face multimodality problem due to conditionally independent decoding.
Approach:	They propose to incorporate linguistic structure into NAG inference instead of teacher AG . they propose a method that provides a specific POS sequence to constrain the NAG model .
Outcome:	The proposed method improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation.

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Ben Liu, Jihai Zhang, Fangquan Lin, Cheng Yang, Min Peng

Weakly Supervised Vision-and-Language Pre-training with Relative Representations (2023.acl-long)

Challenge:	Empirical evidence suggests that LLMs perform worse than conventional KGC approaches.
Approach:	They propose a filter-then-generate paradigm and a multiple-choice question format to harness the capability of LLMs while mitigating the issue casused by hallucinations.
Outcome:	The proposed method achieves substantial performance gain compared to existing state-of-the-art methods.

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Chi Chen, Peng Li, Maosong Sun, Yang Liu

Annotating Interruption in Dyadic Human Interaction (2022.lrec-1)

Challenge:	Weakly supervised vision-and-language pre-training (WVLP) uses only local descriptions of images as cross-modal anchors to construct weakly-aligned image-text pairs for pre- training.
Approach:	They propose to take a small number of aligned image-text pairs as anchors and represent each unaligned image and text by its similarities to these anchors.
Outcome:	The proposed model reduces the cost of pre-training while maintaining decent performance on downstream tasks.

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Liu Yang, Catherine Achard, Catherine Pelachaud

ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval (2024.lrec-main)

Challenge:	Existing interruption and turn switch classification methods are not yet available.
Approach:	They propose a new interruption annotation schema that integrates existing interruption and turn switch classification methods to annotate different types of interruptions.
Outcome:	The proposed method can distinguish smooth turn exchange, backchannel and interruption (including interruption types) and to annotate dyadic conversation.

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Yuanhang Zheng, Peng Li, Wei Liu, Yang Liu, Jian Luan, Bin Wang

Outcome-Grounded Advantage Reshaping for Fine-Grained Credit Assignment in Mathematical Reasoning (2026.acl-long)

Challenge:	Recent studies have proposed tool learning, which augments LLMs with external tools.
Approach:	They propose an adaptive and hierarchy-aware reranking method to refine retrieval results by truncating the retrieval result related to seen and unseen tools at different positions.
Outcome:	The proposed method improves retrieval results, leading to better execution results generated by the LLM.

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Ziheng Li, Liu Kang, Feng Xiao, Luxi Xing, Qingyi Si, Zhuoran Li, Weikang Gong, Deqing Yang, Yanghua Xiao, Hongcheng Guo

Large Language Models Are Still Misled by Simple Bias Ensembles (2026.findings-acl)

Challenge:	Group Relative Policy Optimization (GRPO) uses a coarse-grained credit assignment mechanism that propagates group-level rewards uniformly to to every token in a sequence, neglecting the varying contribution of individual reasoning steps.
Approach:	They introduce Outcome-grounded Advantage Reshaping (OAR) which redistributes advantages based on how much each token influences the model’s final answer.
Outcome:	Empirical results show that OAR-G outperforms GRPO on a high-fidelity attribution signal and suppresses low-impact tokens while preserving the advantage mass.

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Zhouhao Sun, Zhiyuan Kan, Xiao Ding, Li Du, Bibo Cai, Yang Zhao, Bing Qin, Ting Liu

MCS-Bench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in Chinese Classical Studies (2025.acl-long)

Challenge:	Existing benchmarks for large language models are constrained to datasets where each sample is manually injected with only one type of bias.
Approach:	They propose a multi-bias benchmark where each sample contains multiple types of biases.
Outcome:	The proposed benchmark shows that existing LLMs and debiasing methods perform poorly on this benchmark, highlighting the challenge of eliminating compounded biases.

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Yang Liu, Jiahuan Cao, Hiuyi Cheng, Yongxin Shi, Kai Ding, Lianwen Jin

Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts (2023.emnlp-main)

Challenge:	Multimodal Large Language Models (MLLMs) have advanced visual and language understanding, but their potential in Chinese Classical Studies (CCS) remains underexplored due to the lack of specialized benchmarks.
Approach:	They propose to develop a multimodal benchmark specifically designed for Chinese Classical Studies across multiple subdomains to bridge this gap.
Outcome:	The proposed benchmark spans seven core subdomains with a total of 45 meticulously designed tasks.

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Tengxiao Liu, Qipeng Guo, Yuqing Yang, Xiangkun Hu, Yue Zhang, Xipeng Qiu, Zheng Zhang

A Corpus of Adpositional Supersenses for Mandarin Chinese (2020.lrec-1)

Challenge:	Existing methods for large language models (LLMs) have been used to prompt different reasoning thoughts, such as Chain of Thought and Program of Though.
Approach:	They propose a framework that prompts large language models with diverse reasoning thoughts by iterating between different prompting methods.
Outcome:	The proposed framework is able to generate multiple reasoning thoughts in 10 popular math reasoning datasets and is orthogonal to recent work that makes improvements on single reasoning methods and can generalise to logical reasoning domain.

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Siyao Peng, Yang Liu, Yilun Zhu, Austin Blodgett, Yushi Zhao, Nathan Schneider

Challenge:	Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language.
Approach:	They propose to annotate Chinese adpositions in a corpus with all aforementioned supersenses . they adapt a framework that defined a set of supersens according to ostensibly language-independent criteria .
Outcome:	The proposed corpus is the first to be broadly annotated with adposition semantics in Chinese . it shows that the supersense categories are well-suited to Chinese adepositions despite syntactic differences from English .

MADAWSD: Multi-Agent Debate Framework for Adversarial Word Sense Disambiguation (2025.emnlp-main)

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Kaiyuan Zhang, Qian Liu, Luyang Zhang, Chaoqun Zheng, Shuaimin Li, Bing Xu, Muyun Yang, Xinxiao Qiao, Wenpeng Lu

From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning (2026.acl-long)

Challenge:	Word sense disambiguation (WSD) is a fundamental yet challenging task in natural language processing.
Approach:	a novel multi-agent Debate framework for adversarial word Sense disambiguation is proposed . the framework simulates a real-world debate environment where multiple agents engage in discussions about ambiguous words in the context of adversarials.
Outcome:	The proposed framework integrates with existing LLMs and improves models in Chinese language . it shows that it can be used to improve models in the Chinese language and improve performance .

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Jiajun Zhang, Zeyu Cui, Jiaxi Yang, Lei Zhang, Yuheng Jing, Zeyao Ma, Tianyi Bai, Zilei Wang, Qiang Liu, Liang Wang, Binyuan Hui, Junyang Lin

CompTab: A Comprehensive Benchmark for Real-World TableQA with Complex Reasoning and Irregular Tables (2026.acl-long)

Challenge:	Fill-in-the-Middle (FIM) models suffer from performance degradation and prohibitive latency.
Approach:	They propose a search-and-replace infilling framework that integrates agentic verification and editing into a single-pass inference process.
Outcome:	The proposed framework harmonizes completion tasks with the instruction-following priors of Chat LLMs, extending the paradigm from static infilling to dynamic context-aware editing.

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Zhen Yang, Wei Du, Jie Wang, Wenze Zhou, Xiangfeng Meng, Zhengyang Wang, Suping Sun, Ziwei Du, Haodong Zou, Jie Chen, Yongbin Liu, Shicheng Tan, Jiahao Ying, Shu Zhao

O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning (2021.findings-acl)

Challenge:	Existing benchmarks focus on well-structured tables and fail to reflect irregular structures and complex reasoning commonly encountered in real-world scenarios.
Approach:	They propose a benchmark to evaluate TableQA under complex reasoning and irregular table conditions.
Outcome:	The proposed framework improves generalization and realism of large language models under complex and irregular table conditions.

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Fenglin Liu, Xuancheng Ren, Xian Wu, Bang Yang, Shen Ge, Xu Sun

Unsupervised Multi-Granularity Summarization (2022.findings-emnlp)

Challenge:	Existing methods for video captioning consider a sequence of frames and biases towards focused objects.
Approach:	They propose an Object-Oriented Non-Autoregressive approach to video captioning . it performs three steps: 1) identify the focused objects and predict their locations . 2) generate related attribute words and relation words of these focused objects to form a draft caption .
Outcome:	The proposed method achieves competitive results with the state-of-the-art methods but with higher diversity and faster inference speed.

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Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao, Yizhu Jiao, Xingxing Zhang, Yichong Xu, Chenguang Zhu, Michael Zeng, Jiawei Han

RWKV-CLIP: A Robust Vision-Language Representation Learner (2024.emnlp-main)

Challenge:	Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines.
Approach:	They propose to rank events by their salience and annotate a benchmark for GranuSum that contains multiple summaries at different granularities for each document cluster.
Outcome:	The proposed framework is capable of producing multi-granular summaries in unsupervised manner over strong baselines.

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Tiancheng Gu, Kaicheng Yang, Xiang An, Ziyong Feng, Dongnan Liu, Weidong Cai, Jiankang Deng

P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks (2022.acl-short)

Challenge:	Using large image-text datasets, large-scale image-data sets have been used for visionlanguage pre-training.
Approach:	They propose a framework that leverages Large Language Models to combine and refine information from web-based image-text pairs, synthetic captions, and detection tags.
Outcome:	The proposed framework can combine and refine information from web-based image-text pairs, synthetic captions, and detection tags.

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Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, Jie Tang

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)

Challenge:	Existing methods of prompt tuning cannot handle hard sequence labeling tasks.
Approach:	They propose to optimize prompt tuning to tune continuous prompts with a frozen language model.
Outcome:	The proposed method matches finetuning with prompt tuning while having only 0.1%-3% tuned parameters.

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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang

Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving (2026.acl-long)

Challenge:	Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns.
Approach:	They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users.
Outcome:	The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks.

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Xinyu Zhang, Yuchen Wan, Boxuan Zhang, Zesheng Yang, Lingling Zhang, Bifan Wei, Jun Liu

Text Clustering

Challenge:	Large Language Models (LLMs) struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but conflicting modeling paradigms, hindering effective solution generation.
Approach:	They propose to use memory to leverage historical solutions in a training-free manner to enhance performance by leveraging generalizable guidance knowledge.
Outcome:	The proposed agent achieves an average performance improvement of 11%-21% over previous agents.

Diversity and Consistency: Exploring Visual Question-Answer Pair Generation (2021.findings-emnlp)

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Sen Yang, Qingyu Zhou, Dawei Feng, Yang Liu, Chao Li, Yunbo Cao, Dongsheng Li

SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning (2026.acl-long)

Challenge:	Existing tasks to generate question-answer pairs from visual images are under-explored.
Approach:	They propose a task that targets question-answer pair generation from visual images.
Outcome:	The proposed model can generate diverse or consistent QAPs on two benchmarks.

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Peidong Wang, Zhiming Ma, Xin Dai, YongKang Liu, Shi Feng, Xiaocui Yang, Wenxing Hu, Zhihao Wang, Mingjun Pan, Li Yuan, Daling Wang

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

Challenge:	Existing methods for fraud detection rely on transcribed text, lacking acoustic cues . a proposed framework for audio-based slow-thinking fraud detection eliminates transcription errors .
Approach:	They propose a framework for audio-based slow-thinking fraud detection that eliminates transcription errors and rewards slow-thought reasoning by capturing fine-grained audio details.
Outcome:	The proposed method improves accuracy, inference efficiency, and real-time processing capabilities.

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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang

Towards Robust Neural Machine Translation (P18-1)

Challenge:	Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach:	They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome:	The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

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Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, Yang Liu

MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance (D19-1)

Challenge:	Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation models.
Approach:	They propose adversarial stability training to make encoder and decoder robust to perturbations by enabling them to behave similarly for the original input and its perturbed counterpart.
Outcome:	The proposed approach improves translation quality and robustness over strong models on Chinese-English, English-German and English-French translation tasks.

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Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, Steffen Eger

Advancing Large Language Model Attribution through Self-Improving (2024.emnlp-main)

Challenge:	Existing evaluation metrics are not capable of evaluating text quality.
Approach:	They propose a metric that compares system output against reference texts based on semantics rather than surface forms.
Outcome:	The proposed metric shows a high correlation with human judgment of text quality on a number of text generation tasks.

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Lei Huang, Xiaocheng Feng, Weitao Ma, Liang Zhao, Yuchun Fan, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin

AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts (2026.acl-long)

Challenge:	Teaching large language models to generate text with citations to evidence sources requires high-quality attribution data, which is costly and labor-intensive.
Approach:	They propose a framework for iteratively improving the attribution capability of large language models (LLMs) by attributing output to verifiable sources.
Outcome:	Experiments on three open-domain question-answering datasets show that START improves in aggregating information across multiple sources.

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Keyu Li, Junhao Shi, Yang Xiao, Mohan Jiang, Jie Sun, Yunze Wu, Dayuan Fu, Shijie Xia, Xiaojie Cai, Tianze Xu, Weiye Si, Wenjie Li, Dequan Wang, Pengfei Liu

Challenge:	Existing benchmarks focus on single agentic capability, failing to capture long-horizon real-world scenarios.
Approach:	They propose a benchmark that evaluates 6 agentic capabilities across 32 real-world scenarios.
Outcome:	Experiments show that closed-source models outperform open-source model (48.4% vs 32.1%) integrating models with advanced scaffolds to form autonomous agents is a paradigm shift.

M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)

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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng

Benchmarking Multi-National Value Alignment for Large Language Models (2025.findings-acl)

Challenge:	Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities .
Approach:	They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models.
Outcome:	The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree.

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Chengyi Ju, Weijie Shi, Chengzhong Liu, Jiaming Ji, Jipeng Zhang, Ruiyuan Zhang, Jiajie Xu, Yaodong Yang, Sirui Han, Yike Guo

CONSTRUCTURE: Benchmarking CONcept STRUCTUre REasoning for Multimodal Large Language Models (2024.findings-emnlp)

Challenge:	Existing studies on large language models focus on ethical reviews, failing to capture the diversity of national values.
Approach:	They propose a national value extraction pipeline to efficiently construct value assessment datasets and a model-based model with instruction tagging to process raw data sources.
Outcome:	The proposed benchmark evaluates the alignment of LLMs with the values of five major nations: China, the United States, the UK, France, and Germany.

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Zhiwei Zha, Xiangru Zhu, Yuanyi Xu, Chenghua Huang, Jingping Liu, Zhixu Li, Xuwu Wang, Yanghua Xiao, Bei Yang, Xiaoxiao Xu

UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models (2026.findings-acl)

Challenge:	Existing multimodal large language models lack the ability to perceive the visual world with a deep concept structure cognition.
Approach:	They propose a concept-level benchmark to assess MLLMs’ hierarchical concept understanding and reasoning abilities.
Outcome:	The proposed model outperforms state-of-the-art models in concept structure reasoning evaluation.

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Jiajun Wu, Jian Yang, Wei Zhang, Linzheng Chai, Yuchi Ma, Ensheng Shi, Yuqing Ma, Zhoujun Li, Xianglong Liu

Progressive Translation: Improving Domain Robustness of Neural Machine Translation with Intermediate Sequences (2023.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks, but their effectiveness relies on supervised training with extensive labeled data and computational resources.
Approach:	They propose an unsupervised method that leverages Internal Probing of Large language models for Code generation without any external corpus, even unlabeled code snippets.
Outcome:	The proposed method can achieve competitive performance compared to supervised approaches while reducing the dependency on labeled data and computational resources.

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Chaojun Wang, Yang Liu, Wai Lam

Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning (2025.findings-acl)

Challenge:	Existing studies show that intermediate supervision signals benefit various tasks such as math word problems and commonsense reasoning.
Approach:	They propose to introduce an inductive bias that reflects a domain-agnostic principle of translation and a full-permutation multi-task learning to alleviate spurious correlations.
Outcome:	The proposed signals reduce spurious correlations and spurious hallucinations on out-of-domain translation, and are especially promising in low-resource scenarios.

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Haoyu Han, Yaochen Xie, Hui Liu, Xianfeng Tang, Sreyashi Nag, William Headden, Yang Li, Chen Luo, Shuiwang Ji, Qi He, Jiliang Tang

Extending Context Window of Large Language Models from a Distributional Perspective (2024.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks, however, they still face challenges in reasoning tasks that require understanding and inferring relationships between distinct pieces of information within text sequences.
Approach:	They propose to construct explicit graphs from context and leverage them to enhance LLM reasoning performance on reasoning tasks.
Outcome:	Extensive experiments show that the proposed method improves both logical reasoning and multi-hop question answering tasks.

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Yingsheng Wu, Yuxuan Gu, Xiaocheng Feng, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin

SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent (2024.findings-emnlp)

Challenge:	Existing scaling methods for extending context window rely on empirical approaches and lack understanding of the internal distribution within RoPE resulting in suboptimal performance.
Approach:	They propose to optimize the context window extending task from the view of rotary angle distribution by minimizing disturbance between rotary angles to maintain consistency with the pre-training phase.
Outcome:	The proposed approach reduces by up to 72% of the distributional disturbance when extending LLaMA2’s context window to 8k, and reduces it by up 32% when extending to 16k.

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Jiarui Ji, Yang Li, Hongtao Liu, Zhicheng Du, Zhewei Wei, Qi Qi, Weiran Shen, Yankai Lin

NewsDialogues: Towards Proactive News Grounded Conversation (2023.findings-acl)

Challenge:	Existing research on the allocation of public scarce resources has limitations due to data scarcity and data scariness.
Approach:	They propose a framework that integrates Large Language Models into economic simulations . they conduct extensive policy simulation experiments to verify the framework's effectiveness .
Outcome:	The proposed framework bridges the gap between theoretical models and real-world dynamics by integrating large language models into economic simulations.

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Siheng Li, Yichun Yin, Cheng Yang, Wangjie Jiang, Yiwei Li, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang

DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms (2025.findings-emnlp)

Challenge:	Hot news is one of the most popular topics in daily conversations.
Approach:	They propose a task where a dialogue system can lead the conversation based on key topics of the news.
Outcome:	The proposed method can lead conversations based on key topics of the news . it can also be used in information-seeking and chit-chat scenarios .

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Xiaojun Bi, Shuo Li, Junyao Xing, Ziyue Wang, Fuwen Luo, Weizheng Qiao, Lu Han, Ziwei Sun, Peng Li, Yang Liu

MedDCR: Learning to Design Agentic Workflows for Medical Coding (2026.findings-acl)

Challenge:	Dongba pictographic is the only pictograph script still in use in the world.
Approach:	DongbaMIE is the first dataset focusing on multimodal information extraction of Dongbe pictographs.
Outcome:	The dataset contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs.

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Jiyang Zheng, Islam Nassar, Thanh Vu, Xu Zhong, Yang Lin, Tongliang Liu, Long Duong, Yuan-Fang Li

Challenge:	Medical coding is the process of translating unstructured clinical notes into standardized diagnostic and procedural codes.
Approach:	They propose a closed-loop framework that treats workflow design as a learning problem.
Outcome:	The proposed framework outperforms state-of-the-art workflows on benchmark datasets and produces interpretable, adaptable workflows that better reflect real coding practice.

MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings (2026.acl-long)

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Haonan Chen, Hong Liu, Yuping Luo, Liang Wang, Nan Yang, Furu Wei, Zhicheng Dou

Into the Gray Zone: Domain Contexts Can Blur LLM Safety Boundaries (2026.acl-long)

Challenge:	Existing approaches to embed multimodal models face limitations such as suboptimal causal attention in VLMs and limited diversity in training objectives and data.
Approach:	They propose a framework for transforming pre-trained VLMs into bidirectional multimodal embedding models.
Outcome:	The proposed model improves performance across MMEB and ViDoRe-v2 benchmarks and exhibits strong scalability with both model size and training data on MMEF.

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Ki Sen Hung, Xi Yang, Chang Liu, Haoran Li, Kejiang Chen, Changxuan Fan, Tsun On Kwok, Weiming Zhang, Xiaomeng Li, Yangqiu Song

Task Compass: Scaling Multi-task Pre-training with Task Prefix (2022.findings-emnlp)

Challenge:	a goal of LLM alignment is to balance usefulness with harmlessness, but this conflictes when knowledge serves both legitimate and malicious purposes.
Approach:	They propose a framework that combines safety-research contexts with adversarial interactions to exploit a vulnerability in Jargon queries.
Outcome:	a framework outperforms existing methods in analyzing Jargon queries, a study shows . it achieves 93% of attacks across seven models, while remaining useful, the authors say .

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Zhuosheng Zhang, Shuohang Wang, Yichong Xu, Yuwei Fang, Wenhao Yu, Yang Liu, Hai Zhao, Chenguang Zhu, Michael Zeng

M2Edit: Locate and Edit Multi-Granularity Knowledge in Multimodal Large Language Model (2025.emnlp-main)

Challenge:	Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks.
Approach:	They propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks.
Outcome:	The proposed model can be used as a foundation backbone for a wide range of tasks and as augmentation tool for data augmentation with complementary tasks.

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Yang Zhou, Pengfei Cao, Yubo Chen, Qingbin Liu, Dianbo Sui, Xi Chen, Kang Liu, Jun Zhao

Pre-trained Personalized Review Summarization with Effective Salience Estimation (2023.findings-acl)

Challenge:	Existing knowledge editing methods for MLLMs lack multi-granularity knowledge . existing knowledge editing approaches lack multimodality knowledge and generalize to multimodal data.
Approach:	They propose a multimodal knowledge editing method which integrates key knowledge layers within MLLMs and collaboratively edits them.
Outcome:	The proposed method improves visual generality performance on knowledge data of different granularities.

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Hongyan Xu, Hongtao Liu, Zhepeng Lv, Qing Yang, Wenjun Wang

IGT2P: From Interlinear Glossed Texts to Paradigms (2020.emnlp-main)

Challenge:	Pretrained language models (PLMs) are a new paradigm in text generation for the strong ability of natural language comprehension.
Approach:	They propose a pre-trained personalized review summarization method that incorporates personalized information into the salience estimation of input reviews.
Outcome:	The proposed method performs better than the state-of-the-art methods on real-world datasets.

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Sarah Moeller, Ling Liu, Changbing Yang, Katharina Kann, Mans Hulden

An Experimental Study on the Influence of Culture on Cross-Lingual Sentiment Transfer (2026.acl-long)

Challenge:	Existing systems for learning morphology have limited their use to languages with publicly available structured data, such as online dictionaries like Wiktionary.
Approach:	They propose a task that generates entire morphological paradigms from IGT input and a language expert cleaning noisy IGT data.
Outcome:	The proposed task speeds up the process and generates entire morphological paradigm tables from IGT input.

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Ahao Liu, Haitong Yang, Wang Chuanrong

Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information (2023.eacl-main)

Challenge:	Identical linguistic expressions can convey different sentiments across cultural contexts . current multilingual models often reduce language to symbolic representation . cultural misalignment is a structural bottleneck, authors say .
Approach:	They conduct an empirical study to quantify the influence of culture on cross-lingual sentiment transfer across 7 common SMLMs and 5 linguistically diverse languages.
Outcome:	The proposed model disentangles cultural factors from confounding variables and shows cultural distance is a negative predictor of transfer performance.

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Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Sungjin Lee, Devamanyu Hazarika, Mahdi Namazifar, Di Jin, Yang Liu, Dilek Hakkani-Tur

Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically (2021.emnlp-main)

Challenge:	Intent detection is a fundamental element in task-oriented dialogue systems, usually occurring within the Natural Language Understanding component.
Approach:	They propose an in-context data augmentation approach that fine-tunes a pre-trained language model and synthesizes new datapoints that correspond to given intents.
Outcome:	The proposed method produces training data that achieves state-of-the-art on three challenging intent detection datasets and performs on par with the state- of-the art in full-shot settings.

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Xin Liu, Qingcai Chen, Junying Chen, Wenxiu Zhou, Tingyu Liu, Xinlan Yang, Weihua Peng

SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)

Challenge:	Existing methods to integrate knowledge into text can confuse the representation and import unexpected noises.
Approach:	They propose to leverage capsule routing to associate knowledge with medical literature hierarchically . they extract two fragments from medical literature and encode them into fragment representations .
Outcome:	The proposed method can more accurately associate knowledge with medical literature than mainstream methods.

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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song

CoTJudger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRMs (2026.findings-acl)

Challenge:	Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used.
Approach:	They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark.
Outcome:	The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history.

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Siyi Li, Jiajun Shi, Shiwen Ni, Ge Zhang, Shuaimin Li, Shijian Wang, Zhoufutu Wen, Yizhi LI, Hamid Alinejad-Rokny, Jiaheng Liu, Min Yang, Wenhao Huang

Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering (2025.naacl-long)

Challenge:	Existing evaluations emphasize final accuracy or coarse token counts, and lack automated tools to separate essential logic from structural redundancy.
Approach:	They propose a graph-driven framework that quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path needed to reach a correct solution.
Outcome:	Evaluating 21 LRMs, the proposed framework quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path (SEP) needed to reach a correct solution.

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Chenglei Si, Yanzhe Zhang, Ryan Li, Zhengyuan Yang, Ruibo Liu, Diyi Yang

Bayes-enhanced Lifelong Attention Networks for Sentiment Classification (2020.coling-main)

Challenge:	Generative AI has made rapid advances in multimodal understanding and code generation.
Approach:	They construct a first real-world benchmark for multimodal large language models that directly convert visual designs into code implementations by manually curating 484 diverse real-life webpages as test cases.
Outcome:	The proposed model can generate code implementations that directly render into the given reference webpages, given the screenshots as input.

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Hao Wang, Shuai Wang, Sahisnu Mazumder, Bing Liu, Yan Yang, Tianrui Li

CodeV: Issue Resolving with Visual Data (2025.findings-acl)

Challenge:	Existing deep learning paradigms focus on learning a model from training data of a single task and the learned model is also tested on the same task.
Approach:	They propose a Bayes-enhanced lifelong attention network to learn attention knowledge from a sequence of sentiment classification tasks and build lifelong ones.
Outcome:	The proposed model is able to learn attention knowledge from a set of sentiment classification tasks and build lifelong attentions.

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Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Huang Pengjie, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang

How Sememic Components Can Benefit Link Prediction for Lexico-Semantic Knowledge Graphs? (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have expanded to more complex repository-level tasks.
Approach:	They propose a first approach to leveraging visual data to enhance the issue-resolving capabilities of Large Language Models (LLMs) they demonstrate the effectiveness of CodeV and provide valuable insights into leveraging visualization to resolve GitHub issues.
Outcome:	The proposed approach improves the issue-resolving capabilities of Large Language Models (LLMs) by using visual data.

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Hansi Wang, Yue Wang, Qiliang Liang, Yang Liu

Enhancing Perception: Refining Explanations of News Claims with LLM Conversations (2024.findings-naacl)

Challenge:	Existing methods to predict missing triples in Knowledge Graphs are limited by semantic information.
Approach:	They propose a method to leverage sememe knowledge to enhance LP . LP is a technique that integrates structural and textual information into a Knowledge Graph .
Outcome:	The proposed method improves LP performance in English and Chinese . it improves on WN18RR, HN7 and CWN5, respectively .

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Yi-Li Hsu, Jui-Ning Chen, Yang Fan Chiang, Shang-Chien Liu, Aiping Xiong, Lun-Wei Ku

Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing (2021.findings-acl)

Challenge:	a new framework for Large Language Models (LLMs) streamlines the task of crafting explanations for fake news . a study compared refinement conversations between human and LLMs to enhance the effectiveness of LLM explanations .
Approach:	They propose a framework for Large Language Models to streamline the task of crafting fake news explanations.
Outcome:	The proposed framework enhances the process of crafting explanations for fake news claims through conversational refinement.

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Qian Liu, Dejian Yang, Jiahui Zhang, Jiaqi Guo, Bin Zhou, Jian-Guang Lou

Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation (2021.naacl-main)

Challenge:	Recent years pretrained language models (PLMs) have shown their power on modeling language . however, few efforts have been made to explore grounding capabilities of PLMs .
Approach:	They propose to use pretrained language models to explore syntactic structures . they propose to combine their approach with an erasingthen-awakening approach . their results show that the approach can awaken latent grounding, which is understandable to humans .
Outcome:	Empirical studies show that the proposed approach can awaken latent grounding . it shows great potential to benefit downstream semantic parsing models, it says .

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Hua Zheng, Damai Dai, Lei Li, Tianyu Liu, Zhifang Sui, Baobao Chang, Yang Liu

Challenge:	Existing definition generation methods take the source word as an indecomposable semantic unit, but in parataxis languages like Chinese, word meanings can be composed using the word formation process.
Approach:	They propose to use word formation features to enhance Definition Generation (DG) in Chinese to generate an explanatory text.
Outcome:	The proposed model enhances Definition Generation (DG) in Chinese by decomposing the word meaning into different semantic components.

CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation (2025.emnlp-main)

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Ziyue Liu, Ruijie Zhang, Zhengyang Wang, Mingsong Yan, Zi Yang, Paul D. Hovland, Bogdan Nicolae, Franck Cappello, Sui Tang, Zheng Zhang

LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

Challenge:	Large foundation models have become huge, but they consume computational resources in pretraining.
Approach:	They propose to replace full-size layers with compute-efficient auto-encoders that enforce low-rank activations throughout training.
Outcome:	The proposed method reduces the computing cost by 2pmbtimes and improves training throughput by 1.86pmtime.

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang

A Template-based Method for Constrained Neural Machine Translation (2022.emnlp-main)

Challenge:	Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs.
Approach:	They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding.
Outcome:	The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities.

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Shuo Wang, Peng Li, Zhixing Tan, Zhaopeng Tu, Maosong Sun, Yang Liu

Whether LLMs Know If They Know: Identifying Knowledge Boundaries via Debiased Historical In-Context Learning (2025.findings-acl)

Challenge:	Existing methods to solve this problem can not satisfy the following three desiderata: (1) high translation quality, (2) high match accuracy, and (3) low latency.
Approach:	They propose a template-based method that can provide high translation quality and match accuracy and a low latency inference.
Outcome:	The proposed method outperforms baselines in lexically and structurally constrained translation tasks and can be used in a variety of applications.

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Bo Lv, Nayu Liu, Yang Shen, Xin Liu, Ping Luo, Yue Yu

SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning (2024.naacl-long)

Challenge:	Existing methods for active retrieval (AR) rely on training classification models or using the confidence of the model’s answer to determine knowledge boundaries.
Approach:	They propose a method to identify knowledge boundaries in active retrieval by retrieving historical queries as high-confidence in-context examples.
Outcome:	Experiments on four QA benchmarks show that DH-ICL achieves performance comparable to full retrieval on LLaMA with only half the number of retrievals, without any additional training.

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Bin Wang, Zhengyuan Liu, Xin Huang, Fangkai Jiao, Yang Ding, AiTi Aw, Nancy Chen

Context-Situated Pun Generation (2022.emnlp-main)

Challenge:	a new benchmark for multilingual foundation models is being developed . brittleness of foundation models in the dimensions of semantics and multilinguality is a key limitation .
Approach:	They propose a benchmark for multilingual foundation models, SeaEval . they examine how well these models comprehend cultural practices, nuances, and values .
Outcome:	The proposed model can be used to evaluate multilingual and multicultural scenarios.

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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs (2022.naacl-main)

Challenge:	a new task for context-situated pun generation uses a given context to generate puns . human evaluation shows that 69% of top retrieved pun words can be used to generate context-based puns.
Approach:	They propose a task where puns are generated based on contextual keywords and pun words.
Outcome:	The proposed system generates successful puns 31% of the time given a plausible tuple of context words and pun pairs.

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Sha Li, Mahdi Namazifar, Di Jin, Mohit Bansal, Heng Ji, Yang Liu, Dilek Hakkani-Tur

Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models (2025.acl-long)

Challenge:	Existing conversation models treat knowledge selection as a sentence ranking problem where each sentence is handled individually, ignoring the internal semantic connection between sentences.
Approach:	They propose to automatically convert background knowledge documents into document semantic graphs and perform knowledge selection over such graphs.
Outcome:	The proposed model improves on the knowledge selection task and the response generation task on HollE and generalizes on unseen topics in WoW.

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Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang, Yifei Ming, Qing Yu, Go Irie, Yixuan Li, Hai Helen Li, Ziwei Liu, Kiyoharu Aizawa

When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors (2026.acl-long)

Challenge:	Multiple-choice question answering (MCQA) is widely used to assess the understanding capability of Large Multimodal Models (LMMs).
Approach:	They propose a task to evaluate the robust understanding capability of Large Multimodal Models (LMMs) they introduce a benchmark to assess performance across various ability dimensions .
Outcome:	The proposed model can withhold answers when encountering unsolvable problems of MCQA, proving it understands the answer.

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Chenghao Yang, Yuning Zhang, Zhoufutu Wen, Tao Gong, Jiaheng Liu, Qi Chu, Nenghai Yu

Polymorphic Universal Transformer (2026.acl-long)

Challenge:	Existing metrics fail to distinguish mandatory behaviors required for task success from non-mandatory patterns that reflect a model’s autonomous preferences.
Approach:	They propose to use response pattern similarity and action graph similarity to isolate non-mandatory behaviors from mandatory behaviors.
Outcome:	Evaluating 18 models from 8 providers on -Bench and 2-Bench against Claude Sonnet 4.5, the authors find that within-family model pairs score 5.9 pp higher in response pattern similarity and action graph similarity .

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Yilong Chen, Zitian Gao, Yihao Xiao, Jason Klein Liu, Xinyu Yang, Yifan Luo, Haoming Luo, Zhengmao Ye, Tingwen Liu, Ran Tao, Bryan Dai

Safety-Aware Dialogue System for Postoperative Oral Cancer Care with Structured Clarification and a Clinically Curated Dataset (2026.findings-acl)

Challenge:	Compute Distribution Skew is a pathological phenomenon in ultra-deep recurrent models . it causes over-smoothing, representation rank collapse, and degraded reasoning performance.
Approach:	They propose a dynamic architecture that redefines recursive computation by decoupling parameter count from depth.
Outcome:	The proposed model significantly improves representation rank and reasoning robustness while reducing computation by 64.7%.

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Tzu-Chi Liu, Hui-Ying Yang, Shiow-Ching Shun, Yu-Chi Chen, Lu-Yen Anny Chen, Yong-Sheng Chen

The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation (2025.acl-long)

Challenge:	Clinical dialogue systems can enhance patient education and follow-up care by providing brief and subjective messages that lack critical clinical context.
Approach:	They propose a safety-aware dialogue system that applies information-gain guided clarification before RAG-based response generation and screens user utterances for emotional distress and suicidal ideation.
Outcome:	The proposed system improves quality and clinical appropriateness relative to strong baselines while aligning with expert judgments on clinically concerning utterances.

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Xiaoyu Zhang, Juan Zhai, Shiqing Ma, Qingshuang Bao, Weipeng Jiang, Qian Wang, Chao Shen, Yang Liu

Learnable Privacy Neurons Localization in Language Models (2024.acl-short)

Challenge:	Large Language Models (LLMs) have emerged as the new recommendation engines, surpassing traditional methods in both capability and scope, particularly in code generation.
Approach:	They propose to use a dataset to investigate a new type of bias in Large Language Models for code generation, provider bias, to determine whether the model favors specific providers.
Outcome:	The proposed model favors services from Google and Amazon, but without explicit directives, and can modify input code to incorporate their preferred providers without user requests.

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Ruizhe Chen, Tianxiang Hu, Yang Feng, Zuozhu Liu

EquiBench: Benchmarking Large Language Models’ Reasoning about Program Semantics via Equivalence Checking (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) memorize and disclose private information, especially Personally Identifiable Information (PII) concerns regarding privacy and security within human society remain poorly understood.
Approach:	They propose to use learnable binary weight masks to localize PII-sensitive neurons within LLMs by deactivating localized privacy neurons.
Outcome:	The proposed method localizes PII-sensitive neurons across all layers and shows the property of PI I specificity.

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Anjiang Wei, Jiannan Cao, Ran Li, Hongyu Chen, Yuhui Zhang, Ziheng Wang, Yuan Liu, Thiago S. F. X. Teixeira, Diyi Yang, Ke Wang, Alex Aiken

Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering (2026.findings-eacl)

Challenge:	EquiBench is a new benchmark to evaluate large language models' ability to reason about program semantics . Unlike natural language, code is executable.
Approach:	They propose a benchmark to evaluate large language models through equivalence checking . EquiBench consists of 2400 program pairs across four languages and six categories .
Outcome:	The proposed benchmark consists of 2400 program pairs across four languages and six categories.

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Haiyan Zhao, Xuansheng Wu, Fan Yang, Bo Shen, Ninghao Liu, Mengnan Du

VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)

Challenge:	Existing methods for steering concept vectors suffer from noisy features in diverse datasets that undermine steering robustness.
Approach:	They propose a Sparse Autoencoder-Denoised Concept Vector (SDCV) which selectively keeps the most discriminative SAE latents while reconstructing hidden representations.
Outcome:	The proposed method improves steering success rates by 4-16% across six challenging concepts while maintaining topic relevance.

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Yang Tan, Chen Liu, Jingyuan Gao, Wu Banghao, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou

SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026.findings-acl)

Challenge:	Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges.
Approach:	They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs.
Outcome:	The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs.

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Jie Sun, Yu Liu, Lu Han, Qiwen Deng, Xiang Shu, Yang Xiao, Lintao Ma, Xingyu Lu, Jun Zhou, Pengfei Liu, Jiancan Wu, Xiang Wang

KILM: Knowledge Injection into Encoder-Decoder Language Models (2023.acl-long)

Challenge:	Existing large-scale large-context models suffer from performance degradation when processing long numerical sequences.
Approach:	They propose a framework to mitigate attention dispersion by strategically inserting separator tokens into the model to recalibrat attention to local segments while preserving global context.
Outcome:	The proposed framework improves accuracy and reduces inference token consumption by 16.4% on 9 widely-adopted LLMs.

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Yan Xu, Mahdi Namazifar, Devamanyu Hazarika, Aishwarya Padmakumar, Yang Liu, Dilek Hakkani-Tur

Knowledge Transfer in Incremental Learning for Multilingual Neural Machine Translation (2023.acl-long)

Challenge:	Large pre-trained language models retain implicit knowledge within their parameters, but are susceptible to memorizing the pretraining corpora rather than capturing the knowledge within them.
Approach:	They propose to inject entity-related knowledge into encoder-decoder PLMs via a generative knowledge infilling objective through continued pre-training.
Outcome:	The proposed approach outperforms state-of-the-art models on general NLU and NLG tasks while maintaining their original performance.

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Kaiyu Huang, Peng Li, Jin Ma, Ting Yao, Yang Liu

Inverting the Shield: Systematically Generating Safety Tests from Policy Specifications (2026.acl-long)

Challenge:	Existing studies focus on overcoming catastrophic forgetting on original language pairs while lacking encouragement to learn new knowledge from incremental learning.
Approach:	They propose a knowledge transfer method that can adapt original MNMT models to diverse incremental language pairs by flexibly introducing knowledge from external models into original models, which encourages the models to learn new language pairs.
Outcome:	The proposed method outperforms baselines on multiple languages while maintaining performance on original language pairs.

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Xiaoyue Lu, Xianglin Yang, Haijun Liu, Jiahao Liu, Kuntai Cai, Yan Xiao, Jin Song Dong

Allies: Prompting Large Language Model with Beam Search (2023.findings-emnlp)

Challenge:	Existing safety evaluation paradigms rely on constructed benchmarks or dynamic red-teaming to probe potential vulnerabilities.
Approach:	They propose a framework that integrates specification-based software testing with AI safety.
Outcome:	The proposed framework achieves higher coverage and attack success counts compared to baselines.

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Hao Sun, Xiao Liu, Yeyun Gong, Yan Zhang, Daxin Jiang, Linjun Yang, Nan Duan

Shanks: Simultaneous Hearing and Thinking for Spoken Language Models (2026.acl-long)

Challenge:	Existing methods to build LLMs with stacking are limited by their information coverage and low fault tolerance.
Approach:	They propose a method that leverages large language models to iteratively generate new queries from an input query.
Outcome:	The proposed method outperforms baselines on open-domain question answering benchmarks.

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Cheng-Han Chiang, Xiaofei Wang, Linjie Li, Chung-Ching Lin, Kevin Lin, Shujie Liu, Zhendong Wang, Zhengyuan Yang, Hung-yi Lee, Lijuan Wang

G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment (2023.emnlp-main)

Challenge:	Existing large language models and spoken language models (SLMs) begin thinking and taking actions only after the user has finished their turn.
Approach:	They propose a general inference framework that enables SLMs to generate unspoken chain-of-thought reasoning while listening to user input.
Outcome:	The proposed framework enhances real-time user–SLM interaction in two scenarios.

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Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, Chenguang Zhu

DavIR: Data Selection via Implicit Reward for Large Language Models (2025.acl-long)

Challenge:	Conventional reference-based metrics have low correlation with human judgments, especially for open-ended generation tasks.
Approach:	They propose to use large language models as reference-free NLG evaluators to assess the quality of NLG outputs.
Outcome:	The proposed framework outperforms all previous methods in two generation tasks, and has a Spearman correlation of 0.514 with human on summarization task, and a large variance in human judgments.

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Haotian Zhou, Tingkai Liu, Qianli Ma, Yufeng Zhang, Jianbo Yuan, Pengfei Liu, Yang You, Hongxia Yang

Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (P19-1)

Challenge:	6% of Alpaca dataset selected with DavIR can steer both LLaMA and Gemma models to produce superior performance compared to the same models trained on the full 52K dataset.
Approach:	They propose a model-based data selection method for post-training Large Language Models . they generalize Reducible Holdout Loss to core-set selection problem of causal language modeling .
Outcome:	The proposed method can steer both LLaMA and Gemma models to superior performance compared to the same models trained on the full 52K dataset.

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An Yang, Quan Wang, Jing Liu, Kai Liu, Yajuan Lyu, Hua Wu, Qiaoqiao She, Sujian Li

Towards User-Driven Neural Machine Translation (2021.acl-long)

Challenge:	Recent results show pre-trained language models (LMs) can improve machine reading comprehension (MRC) Experimental results indicate that KT-NET offers significant and consistent improvements over BERT .
Approach:	They propose a method that leverages external knowledge bases to improve machine reading comprehension (MRC) KT-NET employs an attention mechanism to select desired knowledge from KBs and fuses selected knowledge with BERT to enable context- and knowledge-aware predictions.
Outcome:	The proposed model outperforms baseline models on ReCoRD and SQuAD1.1 benchmarks and ranks 1st on the ReCoDR and SQUAD1.1 leaderboards.

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Huan Lin, Liang Yao, Baosong Yang, Dayiheng Liu, Haibo Zhang, Weihua Luo, Degen Huang, Jinsong Su

Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization (2021.findings-acl)

Challenge:	a good translation should implicitly mirror user traits rather than translate the original content semantically.
Approach:	They propose a framework that captures user traits from historical inputs . they propose 'user-driven' NMT to model user behavior under a zero-shot learning fashion .
Outcome:	The proposed framework can capture user traits from historical inputs under zero-shot learning fashion.

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Shuaiqi Liu, Jiannong Cao, Ruosong Yang, Zhiyuan Wen

Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks (2023.emnlp-main)

Challenge:	Existing models do not consider key phrases in determining attention weights of self-attention . Existing work does not consider the importance of key phrases when determining weights .
Approach:	They propose a model with highlighting mechanism to assign greater attention weights to key phrases . they propose two structures of highlighting attention for each head and the multihead highlighting . experimental results show that their proposed model significantly outperforms the baseline model .
Outcome:	The proposed model outperforms the baseline models on a multi-news dataset.

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Zhaohui Yan, Songlin Yang, Wei Liu, Kewei Tu

Mining Effective Features Using Quantum Entropy for Humor Recognition (2023.findings-eacl)

Challenge:	Entity and Relation Extraction (ERE) is an important task in information extraction.
Approach:	They propose a hypergraph neural network for ERE built upon the PL-marker . they use a pruner mechanism to transfer the burden of entity identification to the joint module .
Outcome:	The proposed model improves on three widely used benchmarks on ERE task . it uses a pruner mechanism to transfer the burden of entity identification to the joint module .

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Yang Liu, Yuexian Hou

Logic Traps in Evaluating Attribution Scores (2022.acl-long)

Challenge:	Existing studies on humor recognition do not understand the mechanisms that generate humor.
Approach:	They propose to use quantum entropy to represent the semantic uncertainty of the setup and punchline as features for humor recognition.
Outcome:	The proposed features are more effective than baselines for recognizing humorous and non-humorous texts on the SemEval2021 task 7 dataset.

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Yiming Ju, Yuanzhe Zhang, Zhao Yang, Zhongtao Jiang, Kang Liu, Jun Zhao

CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information (2025.coling-main)

Challenge:	Modern deep learning models are notoriously opaque, which has motivated the development of methods for interpreting how deep models predict.
Approach:	They propose to review existing methods for evaluating attribution scores and summarize the logic traps in these methods.
Outcome:	The proposed methods show that they do not contain logic traps and that they are not reliable.

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Yuxin Wang, MingHua Ma, Zekun Wang, Jingchang Chen, Shan Liping, Qing Yang, Dongliang Xu, Ming Liu, Bing Qin

The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification (D18-1)

Challenge:	Existing LLM pruning works focus on unstructured pruning, which typically requires special hardware support for a practical speed-up.
Approach:	They propose a network pruning framework that leverages both coarse and fine-grained activation information as an importance criterion to guide pruning.
Outcome:	The proposed framework outperforms existing pruning methods on diverse models across sparsity budgets.

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Jing Chen, Qingcai Chen, Xin Liu, Haijun Yang, Daohe Lu, Buzhou Tang

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)

Challenge:	Bank Question corpus is a corpus for sentence semantic equivalence identification (SSEI) because of rich expressions in natural languages, SSEI is really a challenging task.
Approach:	They propose to cluster 120,000 question pairs from 1-year online bank custom service logs into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation algorithm to achieve questions with the same intent.
Outcome:	The proposed method achieves questions with the same intent by clustering deduplicated questions into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation (AP) algorithm.

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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang

FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation (2025.acl-long)

Challenge:	Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS).
Approach:	They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features.
Outcome:	The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization.

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Junyu Luo, Zhizhuo Kou, Liming Yang, Xiao Luo, Jinsheng Huang, Zhiping Xiao, Jingshu Peng, Chengzhong Liu, Jiaming Ji, Xuanzhe Liu, Sirui Han, Ming Zhang, Yike Guo

Multimodal Context Carryover (2022.emnlp-industry)

Challenge:	Multimodal Large Language Models (MLLMs) have experienced rapid development in recent years, but there is a notable lack of effective and specialized multimodal evaluation datasets in the financial domain.
Approach:	They introduce FinMME, a multimodal large language model with 11,000 financial research samples and 20 annotators.
Outcome:	The proposed model performs better than state-of-the-art models, highlighting its challenging nature.

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Prashan Wanigasekara, Nalin Gupta, Fan Yang, Emre Barut, Zeynab Raeesy, Kechen Qin, Stephen Rawls, Xinyue Liu, Chengwei Su, Spurthi Sandiri

Transferring General Multimodal Pretrained Models to Text Recognition (2023.findings-acl)

Challenge:	Existing voice-only dialogue systems lack multimodality support, which can lead to costly system redesigns.
Approach:	They propose to augment existing voice-only dialogue systems with additional multimodal components to facilitate quick delivery of visual modality support with minimal changes.
Outcome:	The proposed framework improves visual modality support with minimal changes on an in-house multi-modal visual navigation data set.

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Junyang Lin, Xuancheng Ren, Yichang Zhang, Gao Liu, Peng Wang, An Yang, Chang Zhou

Toward Annotator Group Bias in Crowdsourcing (2022.acl-long)

Challenge:	Existing methods for text recognition rely on large-scale pretraining on human-annotated or synthetic data.
Approach:	They propose a method to transfer multimodal pretrained models to text recognition using image captioning.
Outcome:	The proposed method outperforms the baselines and achieves state-of-the-art performance in the Chinese text recognition benchmark.

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Haochen Liu, Joseph Thekinen, Sinem Mollaoglu, Da Tang, Ji Yang, Youlong Cheng, Hui Liu, Jiliang Tang

MedThink: A Rationale-Guided Framework for Explaining Medical Visual Question Answering (2025.findings-naacl)

Challenge:	Annotator group bias is a common problem in crowdsourcing, but is often overlooked .
Approach:	They propose a probabilistic framework to capture annotator group bias using an extended Expectation Maximization algorithm.
Outcome:	The proposed model can model annotator group bias over competitive datasets and demonstrate that it is effective over multiple datasets.

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Xiaotang Gai, Chenyi Zhou, Jiaxiang Liu, Yang Feng, Jian Wu, Zuozhu Liu

Can LLMs See Without Pixels? Benchmarking Spatial Intelligence from Textual Descriptions (2026.findings-acl)

Challenge:	Existing models for medical visual question answering are limited in their interpretation and interpretation . a semi-automated annotation process is used to streamline data preparation and build new benchmark datasets .
Approach:	They propose a semi-automated annotation process to streamline data preparation and build new benchmark Med-VQA datasets.
Outcome:	The proposed method achieves an accuracy of 83.5% on R-RAD, 86.3% on RSLAKE and 87.2% on RPath.

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Zhongbin Guo, Zhen Yang, Yushan Li, Xinyue Zhang, Wenyu Gao, Jiacheng Wang, Chengzhi Li, Xiangrui Liu, Ping Jian

Ambiguous Learning from Retrieval: Towards Zero-shot Semantic Parsing (2023.acl-long)

Challenge:	Existing advances in Spatial Intelligence rely on vision-Language Models . however, a critical question remains: does spatial understanding originate from visual encoders?
Approach:	They propose to evaluate the SI performance of Large Language Models without pixel-level input.
Outcome:	The proposed benchmark challenges large language models to perform symbolic reasoning rather than visual pattern matching.

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Shan Wu, Chunlei Xin, Hongyu Lin, Xianpei Han, Cao Liu, Jiansong Chen, Fan Yang, Guanglu Wan, Le Sun

Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation (2025.findings-acl)

Challenge:	Existing neural semantic parsers require a large amount of training data which is expensive and difficult to obtain.
Approach:	They propose a framework for a supervised retrieval system based on pretrained language models . they propose ambiguous supervision to improve the precision and coverage of the task .
Outcome:	The proposed approach outperforms state-of-the-art zero-shot parsing methods in ambiguous supervision.

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Junhong Wu, Yang Zhao, Yangyifan Xu, Bing Liu, Chengqing Zong

Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models (2023.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated impressive performance across numerous NLP tasks, but fine-tuning them for Machine Translation (MT) often introduces catastrophic forgetting, compromising the broad general abilities of LLMs and introducing potential security risks.
Approach:	They propose a method that harnesses the strong generative capabilities of Large Language Models to create rationales for training data, which are then "replayed" to prevent forgetting.
Outcome:	The proposed approach harnesses the strong generative capabilities of LLMs to create rationales for training data, which are then “replayed” to prevent forgetting.

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Qinhong Zhou, Zonghan Yang, Peng Li, Yang Liu

Modeling Semantic Compositionality with Sememe Knowledge (P19-1)

Challenge:	Existing knowledge distillation methods require access to internal information of teachers . however, such information is not always accessible for large pre-trained language models .
Approach:	They propose a method to estimate logits from the decision distributions using logits theoretically and empirically.
Outcome:	The proposed method outperforms baselines on natural language understanding and machine reading comprehension datasets.

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Fanchao Qi, Junjie Huang, Chenghao Yang, Zhiyuan Liu, Xiao Chen, Qun Liu, Maosong Sun

PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs (2024.findings-acl)

Challenge:	Semantic compositionality (SC) is defined as the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents.
Approach:	They propose to incorporate sememes into SC models and employ them in learning multiword expressions.
Outcome:	The proposed models achieve significant performance boost compared to baseline methods without sememe knowledge.

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An Liu, Zonghan Yang, Zhenhe Zhang, Qingyuan Hu, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu

Challenge:	Large language models have demonstrated considerable capabilities across various tasks . however, they often fall short of the performance achieved by domain-specific state-of-the-art models .
Approach:	They propose a tuning-free method to augment domain-specific abilities of Large language models . they leverage insights from the response preference of expert models to augment LLMs .
Outcome:	The proposed method outperforms the expert model on 4 ScienceWorld tasks.

How Does In-Context Learning Help Prompt Tuning? (2024.findings-eacl)

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Simeng Sun, Yang Liu, Dan Iter, Chenguang Zhu, Mohit Iyyer

Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data (2022.acl-long)

Challenge:	a growing number of parameter-efficient adaptation methods are needed to fine-tune large language models.
Approach:	They propose a method that combines prompt tuning and in-context learning to improve prompt tuning by concatenating a natural language demonstration with learned prompt embeddings.
Outcome:	The proposed method outperforms prompt tuning and prompt tuning on five language generation tasks.

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Shuohang Wang, Yichong Xu, Yuwei Fang, Yang Liu, Siqi Sun, Ruochen Xu, Chenguang Zhu, Michael Zeng

Fisher-Driven Adaptive Locating for Knowledge Editing in Large Language Models (2026.acl-long)

Challenge:	Experimental results show that REtrieving from the traINing datA only can lead to significant gains on multiple NLG and NLU tasks.
Approach:	They propose to retrieve training instances from traINing datA and concatenate them with input to generate output.
Outcome:	The proposed method achieves state-of-the-art results on XSum, BigPatent, and CommonsenseQA.

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Chenghao Xu, Jiexi Yan, Guangtao Lyu, Qi Liu, Muli Yang, Cheng Deng

HTCCN: Temporal Causal Convolutional Networks with Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs (2024.naacl-long)

Challenge:	Existing methods for locating and editing static knowledge are costly and risk catastrophic forgetting or error.
Approach:	They propose a Fisher-driven adaptation-aware locating strategy that dynamically identifies which model components should be edited for a given knowledge update.
Outcome:	Experiments on standard benchmarks show that FiDAL improves editing effectiveness and knowledge preservation across multiple editing methods.

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Tingxuan Chen, Jun Long, Liu Yang, Zidong Wang, Yongheng Wang, Xiongnan Jin

Knowledge Representation

Challenge:	Temporal knowledge graphs (TKGs) are powerful tools for storing and modeling dynamic facts.
Approach:	They propose a Hawkes process-based temporal causal convolutional network for temporal reasoning under extrapolation settings.
Outcome:	The proposed network is based on Hawkes process-based temporal causal convolutional network and captures the temporal evolution of facts.

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)

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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun

NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application (2021.findings-emnlp)

Challenge:	Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains.
Approach:	They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries.
Outcome:	The proposed system outperforms baselines in the open domain task-solving benchmark.

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Chuhan Wu, Fangzhao Wu, Yang Yu, Tao Qi, Yongfeng Huang, Qi Liu

GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation (2025.findings-acl)

Challenge:	Existing language models are pre-trained and distilled on general corpus like Wikipedia, which has gaps with the news domain and may be suboptimal for news intelligence.
Approach:	They propose a method to distill existing language models on Wikipedia to enable efficient news intelligence.
Outcome:	The proposed model can be used to build and test a news intelligence application on Wikipedia and Wikipedia.

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Jie He, Jennifer Neville, Mengting Wan, Longqi Yang, Hui Liu, Xiaofeng Xu, Xia Song, Jeff Z. Pan, Pei Zhou

BPID: A Benchmark for Personal Identity Deduplication (2024.emnlp-industry)

Challenge:	Large Language Models (LLMs) can expand their capabilities by integrating external tools.
Approach:	They propose a training framework that prepares LLMs for diverse generalization challenges in tool utilization.
Outcome:	The proposed framework improves the tool-usage capabilities of LLMs by up to 8B parameters, surpassing GPT-4o.

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Runhui Wang, Yefan Tao, Adit Krishnan, Luyang Kong, Xuanqing Liu, Yuqian Deng, Yunzhao Yang, Henrik Johnson, Andrew Borthwick, Shobhit Gupta, Aditi Gundlapalli, Davor Golac

VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator (2022.findings-acl)

Challenge:	Data deduplication is a critical task in data management and mining, focused on consolidating duplicate records that refer to the same entity.
Approach:	They propose to use a dataset with 1,000,000 unlabeled synthetic PII profiles and a subset of 10,000 pairs curated and labeled as matches or non-matches.
Outcome:	The proposed datasets contain synthetic profiles built from publicly available sources that do not represent real individuals.

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Ayush Shrivastava, Karthik Gopalakrishnan, Yang Liu, Robinson Piramuthu, Gokhan Tur, Devi Parikh, Dilek Hakkani-Tur

Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling (2023.emnlp-main)

Challenge:	Interactive robots navigating photo-realistic environments need to be trained to handle dynamic nature of dialogue and vision-and-language navigation (VLN).
Approach:	They propose a Transformer-based multi-modal navigator that is better suited to the interactive regime inherent to Cooperative Vision-and-Dialog Navigation (CVDN).
Outcome:	The proposed model is trained to identify and associate object-level concepts and semantics between the environment and dialogue history and identify when to interact vs. navigate via imitation learning of a binary classification head.

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Yuanhang Yang, Shiyi Qi, Chuanyi Liu, Qifan Wang, Cuiyun Gao, Zenglin Xu

Challenge:	Recent studies suggest that transformer-based models perform cross-attention over input pairs, leading to computational cost.
Approach:	They propose a lightweight cross-attention mechanism that performs query encoding only once while modeling the query-candidate interaction in parallel.
Outcome:	The proposed model speeds up sentence pairing by over 113x while achieving comparable performance as the more expensive models.

CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models (2026.findings-acl)

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Zhenhong Zhou, Zherui Li, Jie Zhang, Yuanhe Zhang, Kun Wang, Yang Liu, Qing Guo