Papers by Zhou Liu

Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning (2024.findings-eacl)

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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Lifu Tu, Jin Qu, Semih Yavuz, Shafiq Joty, Wenhao Liu, Caiming Xiong, Yingbo Zhou

Course-Correction: Safety Alignment Using Synthetic Preferences (2024.emnlp-industry)

Challenge:	Cross-lingual transfer of language models trained on high-resource languages such as English has been limited due to the high cost of obtaining non-English conversational data.
Approach:	They introduce a parallel and large-scale multilingual conversation dataset that is used for cross-lingual alignment pretraining by translating the English-only Schema-Guided Dialogue dataset into 105 other languages.
Outcome:	The proposed model performs well on slot-filling and intent classification tasks, and is able to perform well in other languages.

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Rongwu Xu, Yishuo Cai, Zhenhong Zhou, Renjie Gu, Haiqin Weng, Liu Yan, Tianwei Zhang, Wei Xu, Han Qiu

Recyclable Tuning for Continual Pre-training (2023.findings-acl)

Challenge:	Recent studies show that large language models generate harmful content, but the potential for generating harmful content is an escalating concern.
Approach:	They propose to fine-tune LLMs with preference learning to emphasize the preference for timely course-correction by using an automated pipeline.
Outcome:	The proposed model improves course-correction skills without affecting general performance and resists jailbreak attacks.

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Yujia Qin, Cheng Qian, Xu Han, Yankai Lin, Huadong Wang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou

Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization (2022.acl-long)

Challenge:	Continual pre-training is the paradigm where pre-trained language models acquire fresh knowledge and gradually get upgraded.
Approach:	They propose to use adapted weights to recycle old PLMs for continual pre-training . they propose to combine initialization and distillation methods to achieve better performance .
Outcome:	The proposed method improves the convergence and performance of the upgraded PLM.

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Juncai Guo, Jin Liu, Yao Wan, Li Li, Pingyi Zhou

Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading (2021.findings-acl)

Challenge:	Existing approaches to describe the syntax structure of code are lacking in retaining the semantic structure of source code.
Approach:	They propose to use a triplet position to model hierarchical syntax structure of code by introducing a graph neural network and Transformer to preserve the structural and sequential information of code.
Outcome:	The proposed model preserves the structural and sequential information of code and a pointer-generator network that pays attention to both the structure and sequential tokens of code for a better summary generation.

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Zhihan Zhou, Liqian Ma, Han Liu

WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)

Challenge:	Existing models that use textual features and sentiments to make stock predictions are poor explainability and low signal-to-noise ratio.
Approach:	They propose a bi-level event detection model that detects corporate events from news articles and an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark.
Outcome:	The proposed strategy outperforms baselines in winning rate, excess returns over the market, and the average return on each transaction.

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Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei

Literature Meets Data: A Synergistic Approach to Hypothesis Generation (2025.acl-long)

Challenge:	Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions.
Approach:	They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach .
Outcome:	The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size.

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Haokun Liu, Yangqiaoyu Zhou, Mingxuan Li, Chenfei Yuan, Chenhao Tan

Challenge:	Existing methods for hypothesis generation are theory-driven and data-driven, but they lack the computational power to complement each other.
Approach:	They develop a method that combines literature-based insights with data to perform LLM-powered hypothesis generation.
Outcome:	The proposed method outperforms baseline methods on five datasets and shows human accuracy improves on deception detection and AI generated content detection tasks.

Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models (2025.emnlp-main)

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Kaiyan Chang, Yonghao Shi, Chenglong Wang, Hang Zhou, Chi Hu, Xiaoqian Liu, Yingfeng Luo, Yuan Ge, Tong Xiao, JingBo Zhu

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

Challenge:	Recent training-based TTS methods, such as continued reinforcement learning, have surged in popularity, while training-free TTS approaches are gradually fading from prominence.
Approach:	They propose a fine-grained sequential scaling method guided by process verification that integrates training-free TTS methods with other classical parallel scaling methods at the step level.
Outcome:	Experiments on five instruction-tuned large language models (LLMs) show that training-free TTS methods can extend reasoning performance boundaries.

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

CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction (2025.acl-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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Jingheng Ye, Zishan Xu, Yinghui Li, Linlin Song, Qingyu Zhou, Hai-Tao Zheng, Ying Shen, Wenhao Jiang, Hong-Gee Kim, Ruitong Liu, Xin Su, Zifei Shan

A Self-verified Method for Exploring Simile Knowledge from Pre-trained Language Models (2024.lrec-main)

Challenge:	Existing studies have focused on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, but the interpretabilty of these metrics has been neglected.
Approach:	They propose a reference-based metric that describes four aspects of GEC systems: hit-correction, wrong-corrections, under-correcties, and over-corrects.
Outcome:	The proposed metric reveals critical qualities and locates drawbacks of GEC systems.

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Longxuan Ma, Changxin Ke, Shuhan Zhou, Churui Sun, Wei-Nan Zhang, Ting Liu

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

Challenge:	Pre-trained language models (PLMs) have succeeded in natural language processing because they learn generic knowledge from a large corpus.
Approach:	They propose a method that allows pre-trained language models to explore simile knowledge from PLMs . they enhance PLM models with a multi-level simile recognition task that evaluates similes aplenty .
Outcome:	The proposed method can explore more accurate simile knowledge for PLMs.

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

Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation (2020.acl-main)

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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Bo Pang, Erik Nijkamp, Wenjuan Han, Linqi Zhou, Yixian Liu, Kewei Tu

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

Challenge:	Existing methods of open-domain dialogue evaluation are labor-intensive and inefficient.
Approach:	They propose to use open-domain dialogues to evaluate different aspects of dialogues using holistic evaluation metrics.
Outcome:	The proposed metrics show strong correlations with human judgments.

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

CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-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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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 Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder (2024.lrec-main)

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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Kedi Chen, Jie Zhou, Qin Chen, Shunyu Liu, Liang He

Enhancing Cross-Tokenizer Knowledge Distillation with Contextual Dynamical Mapping (2025.findings-acl)

Challenge:	Existing methods for information extraction (IE) focus on training task-specific models, while common knowledge among different IE tasks is not explicitly modeled.
Approach:	They propose a regularization-based transfer learning method for IE via an instructed graph decoder which decodes various complex structures into a graph uniformly based on corresponding instructions.
Outcome:	The proposed method can learn common knowledge from existing datasets and transfer it to a new dataset with new labels.

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Yijie Chen, Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

The Open-World Lottery Ticket Hypothesis for OOD Intent Classification (2024.lrec-main)

Challenge:	Knowledge distillation (KD) approaches focus on homogeneous architectures with identical tokenizers, constraining their applicability in cross-architecture scenarios.
Approach:	They propose a framework that uses contextual information to enhance sequence alignment precision and dynamically improves vocabulary mapping.
Outcome:	The proposed framework shows significant advantages over existing methods for model compression . it can be used across multiple model families and across multiple benchmarks .

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Yunhua Zhou, Pengyu Wang, Peiju Liu, Yuxin Wang, Xipeng Qiu

The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)

Challenge:	Existing methods of Out-of-Domain intent classification lack confidence in In- and Out- of-domain intents.
Approach:	They propose to prune overparameterized models to provide better confidence . they extend the Lottery Ticket Hypothesis to open-world scenarios .
Outcome:	The proposed model can be calibrated to distinguish In- and Out-of-domain intents . the model can also improve on open-world scenarios .

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Meng Chen, Ruixue Liu, Lei Shen, Shaozu Yuan, Jingyan Zhou, Youzheng Wu, Xiaodong He, Bowen Zhou

Alignment-Enhanced Decoding: Defending Jailbreaks via Token-Level Adaptive Refining of Probability Distributions (2024.emnlp-main)

Challenge:	Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations.
Approach:	They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words.
Outcome:	The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words.

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Quan Liu, Zhenhong Zhou, Longzhu He, Yi Liu, Wei Zhang, Sen Su

Enhancing Adversarial Transferability in Visual-Language Pre-training Models via Local Shuffle and Sample-based Attack (2025.findings-naacl)

Challenge:	Existing defenses against jailbreaks focus on perturbing or inspecting inputs, but ignore competing objectives, the underlying cause of alignment failures.
Approach:	They propose a novel defense that employs adaptive decoding to address the root causes of jailbreak issues.
Outcome:	The proposed defense improves safety alignment while maintaining helpfulness.

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Xin Liu, Aoyang Zhou, Kun He

TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency (2025.acl-long)

Challenge:	Visual-Language Pre-training (VLP) models are vulnerable to adversarial examples . previous studies have focused on improving adversariality of models .
Approach:	They propose a local shuffle and sample-based attack that randomly shufts one of the local image blocks and generates adversarial images and samples around them.
Outcome:	The proposed attack outperforms other advanced attacks on Large Vision-Language Models and outperformed previous attacks on Visual-Langue Pre-training models.

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Henry Peng Zou, Zhengyao Gu, Yue Zhou, Yankai Chen, Weizhi Zhang, Liancheng Fang, Yibo Wang, Yangning Li, Kay Liu, Philip S. Yu

Dual-Axis Generative Reward Model Toward Semantic and Turn-taking Robustness in Interactive Spoken Dialogue Models (2026.acl-long)

Challenge:	Test-time computing approaches that leverage additional computational resources during inference have been proven effective in enhancing large language model performance.
Approach:	They propose a linearly scaling approach that leverages local consistency of neighboring unlabeled data to improve test-time predictions.
Outcome:	The proposed approach outperforms baseline methods such as prompting and self-consistency across eight datasets and performs robustly across embedding models.

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Yifu Chen, Shengpeng Ji, Zhengqing Liu, Qian Chen, Wen Wang, Ziqing Wang, Yangzhuo Li, Tianle Liang, Zhou Zhao

MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs (2026.acl-long)

Challenge:	Reinforcement learning (RL) has improved text- and vision-language models, but its application in SDMs is hindered.
Approach:	They propose a dual-axis Generative Reward Model that provides semantic quality and interaction timing for SDMs.
Outcome:	The proposed model achieves state-of-the-art performance on interaction-quality assessment across a wide spectrum of datasets.

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Xiangyu Zhao, Wanghan Xu, Bo Liu, Yuhao Zhou, Fenghua Ling, Ben Fei, Xiaoyu Yue, Lei Bai, Wenlong Zhang, Xiao-Ming Wu

Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction (2024.findings-acl)

Challenge:	Existing datasets often rely on synthetic data or figure-caption pairs, failing to capture the depth and complexity of geoscientific reasoning.
Approach:	They propose a multimodal scientific dataset and benchmark curated from open-access publications.
Outcome:	MSEarth features over 289K figures with captions enriched by contextual discussions and reasoning from original papers.

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Wanlong Liu, Li Zhou, DingYi Zeng, Yichen Xiao, Shaohuan Cheng, Chen Zhang, Grandee Lee, Malu Zhang, Wenyu Chen

Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)

Challenge:	mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring correlations among multiple events.
Approach:	They propose a multi-event argument argument extraction model which extracts arguments from all events simultaneously.
Outcome:	The proposed model performs better on four public datasets while saving time.

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Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei

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

Challenge:	MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness .
Approach:	They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization.
Outcome:	The proposed model achieves superior performance across multiple metrics and is more streamlined.

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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

ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (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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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao

RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation (2026.acl-long)

Challenge:	ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge.
Approach:	They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks .
Outcome:	ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering.

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Bingxian Wu, Yu Zhang, Zonghao Guo, Tang Liu, Chen Qian, Yuxiang Lu, Xingbo Du, Yanghao Li, Yidan Zhang, Chi Chen, Ling Yao, Chenghu Zhou, Maosong Sun

The Elephant in the Room: Exploring the Role of Neutral Words in Language Model Group-Agnostic Debiasing (2025.findings-acl)

Challenge:	Existing RS agents built on general-purpose LLMs are domain-agnostic, resulting in brittle and error-prone workflows.
Approach:	They propose a knowledge-enhanced memory evolution mechanism that bootstraps RS agents with pre-distilled domain knowledge and iteratively integrates online experience for robust multi-step tool execution.
Outcome:	Experiments show that the new model improves tool-use performance and accuracy . iteratively, iteration of the model integrates online experience for robust multi-step tool execution .

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Xinwei Guo, Jiashi Gao, Junlei Zhou, Jiaxin Zhang, Guanhua Chen, Xiangyu Zhao, Quanying Liu, Haiyan Wu, Xin Yao, Xuetao Wei

Flames: Benchmarking Value Alignment of LLMs in Chinese (2024.naacl-long)

Challenge:	Large Language Models (LLMs) are increasingly integrated into our daily lives, raising ethical concerns, especially about perpetuating stereotypes.
Approach:	They propose a method that incorporates a neutral word semantics-based loss function to alleviate the deterioration of the LMS during debiasing.
Outcome:	The proposed method alleviates the deterioration of the Language Modeling Score (LMS) by incorporating a neutral word semantics-based loss function.

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Kexin Huang, Xiangyang Liu, Qianyu Guo, Tianxiang Sun, Jiawei Sun, Yaru Wang, Zeyang Zhou, Yixu Wang, Yan Teng, Xipeng Qiu, Yingchun Wang, Dahua Lin

Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction (2024.emnlp-main)

Challenge:	Existing benchmarks for large language models (LLMs) do not accurately uncover safety vulnerabilities in LLMs.
Approach:	They propose a value alignment benchmark called Flames that encompasses both harmlessness principles and a unique morality dimension that integrates specific Chinese values such as harmony.
Outcome:	The proposed model performs poorly on Flames, particularly in safety and fairness dimensions.

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Jinchuan Zhang, Yan Zhou, Yaxin Liu, Ziming Li, Songlin Hu

Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models (2022.coling-1)

Challenge:	Existing approaches focus on improving attack success rates while overlooking the need for comprehensive test case coverage.
Approach:	They propose a top-down approach to automated red teaming that scales up the diversity of test cases using an extensible, fine-grained risk taxonomy.
Outcome:	The proposed approach scales up the diversity of test cases using a top-down approach based on an extensible, fine-grained risk taxonomy and leverages reinforcement learning techniques to facilitate multi-turn adversarial probing in a human-like manner.

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Zichun Yu, Tianyu Gao, Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Maosong Sun, Jie Zhou

Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding (2023.findings-emnlp)

Challenge:	Prompting has shown to be sample efficient compared to fine-tuning with pre-trained models.
Approach:	They propose a fully automatic prompting method that uses natural language prompts on sequence-to-sequence models and a beam search method to generate a large amount of label sequence candidates.
Outcome:	The proposed method significantly outperforms other no-manual-design methods on single label words and generates large amount of label sequence candidates.

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Xiang Fang, Daizong Liu, Wanlong Fang, Pan Zhou, Yu Cheng, Keke Tang, Kai Zou

Cross Copy Network for Dialogue Generation (2020.emnlp-main)

Challenge:	Existing work on temporal sentence grounding rely on expensive video-query paired annotations . despite this, there are no ground-truth annotations in the current work .
Approach:	They propose to use paired video-query and segment boundary annotations to generate temporal sentence grounding without training.
Outcome:	The proposed model outperforms existing unsupervised methods and beats supervised ones on two challenging datasets.

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Changzhen Ji, Xin Zhou, Yating Zhang, Xiaozhong Liu, Changlong Sun, Conghui Zhu, Tiejun Zhao

Misalignment Attack on Text-to-Image Models via Text Embedding Optimization and Inversion (2025.findings-emnlp)

Challenge:	Despite the success of sequence-to-sequence models, dialogue logics are often ignored.
Approach:	They propose a network architecture to explore the current dialog context and similar dialogue instances’ logical structure simultaneously.
Outcome:	The proposed network architecture is superior to existing state-of-the-art models.

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Zhijie Du, Daizong Liu, Pan Zhou

Sparse Low-rank Adaptation of Pre-trained Language Models (2023.emnlp-main)

Challenge:	Text embedding is a key component of modern NLP models but also poses additional risks.
Approach:	They propose a framework that optimizes embeddings and inverts them to obtain misaligned prompts.
Outcome:	The proposed framework exploits the continuity and distribution characteristics of text embeddings to obtain misaligned prompts of discrete tokens.

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Ning Ding, Xingtai Lv, Qiaosen Wang, Yulin Chen, Bowen Zhou, Zhiyuan Liu, Maosong Sun

MARS2: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation (2026.acl-long)

Challenge:	Existing methods for fine-tuning pre-trained large language models in a parameter-efficient manner are gaining traction within the research community.
Approach:	They propose a method of low-rank adaptation that enables dynamic adjustments to the intrinsic rank during the adaptation process.
Outcome:	The proposed approach outperforms the current method with a fixed and unalterable intrinsic rank and a low-rank adaptation process.

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Pengfei Li, Shijie Wang, Fangyuan Li, Yikun Fu, Kaifeng Liu, Kaiyan Zhang, Dazhi Zhang, Yuqiang Li, Biqing Qi, Bowen Zhou

Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models (2026.acl-long)

Challenge:	Existing approaches to reinforcement learning are decoupled from structured search due to limited trajectory diversity.
Approach:	They propose a unified RL framework that integrates multiple agents within a shared tree-structured search environment.
Outcome:	Experiments show that MARS2 improves performance across diverse model combinations and training settings.

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Yanbin Yin, Kun Zhou, Zhen Wang, Xiangdong Zhang, Yifei Shao, Shibo Hao, Yi Gu, Jieyuan Liu, Somanshu Singla, Tianyang Liu, Eric P. Xing, Zhengzhong Liu, Haojian Jin, Zhiting Hu

Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptions (2025.emnlp-industry)

Challenge:	closed-ended question-based benchmarks struggle with saturation as newer models emerge . crowd-sourced leaderboards rely on costly and slow human judges .
Approach:	They propose a framework that leverages collective intelligence from all large language models to evaluate each other.
Outcome:	a new framework enables a democratic, pairwise evaluation of all large language models . it achieves 97% correlation with human judgements, while significantly reducing the cost.

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Jihang Li, Bing Xu, Zulong Chen, Chuanfei Xu, Minping Chen, Suyu Liu, Ying Zhou, Zeyi Wen

ReFreeKV: Towards Threshold-Free KV Cache Compression (2026.findings-acl)

Challenge:	Existing talent search approaches fail to capture nuanced job-specific preferences and mitigate noise from subjective human judgments.
Approach:	They propose a framework that extracts fine-grained recruitment signals from job descriptions and historical hiring data and employs a role-aware multi-gate MoE network to capture behavioral differences across recruiter roles.
Outcome:	The proposed framework improves talent search effectiveness and delivers substantial business value.

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Xuanfan Ni, Liyan Xu, Chenyang Lyu, Longyue Wang, Mo Yu, Lemao Liu, Fandong Meng, Jie Zhou, Piji Li

FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)

Challenge:	Towards the KV cache efficiency, we propose a new objective that lifts the threshold constraints for robust KV compression.
Approach:	They propose a method that adjusts KV cache budgets while preserving full-cache performance.
Outcome:	The proposed method can reduce memory consumption while preserving full-cache performance.

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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev

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

Challenge:	Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity.
Approach:	They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models.
Outcome:	The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models.

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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.

Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge (2025.naacl-long)

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Li Zhou, Taelin Karidi, Wanlong Liu, Nicolas Garneau, Yong Cao, Wenyu Chen, Haizhou Li, Daniel Hershcovich

LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.acl-long)

Challenge:	Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet lack a robust methodology to dissect these phenomena comprehensively.
Approach:	They propose a multilingual dataset centered on food-related cultural facts and variations in food practices.
Outcome:	The proposed model incorporates cultural context significantly and improves its ability to access cultural knowledge.

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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment (2025.acl-long)

Challenge:	Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM.
Approach:	They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks.
Outcome:	The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting.

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Zekun Moore Wang, Shenzhi Wang, King Zhu, Jiaheng Liu, Ke Xu, Jie Fu, Wangchunshu Zhou, Wenhao Huang

Challenge:	Typical approaches to training large language models rely on limited contrasting patterns . contrasting data is limited and models are susceptible to harmful response tendencies .
Approach:	They propose a framework that integrates contrasting patterns across the prompt, model, and pipeline levels.
Outcome:	The proposed framework outperforms existing methods in the comparison of RQ1 and RQ2 . the proposed framework significantly outperformed existing methods, leading to more comprehensive alignment.

Reflection on Knowledge Graph for Large Language Models Reasoning (2025.findings-acl)

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Yigeng Zhou, Wu Li, Yifan Lu, Jing Li, Fangming Liu, Meishan Zhang, Yequan Wang, Daojing He, Honghai Liu, Min Zhang

AdDriftBench: A Benchmark for Detecting Data Drift and Label Drift in Short Video Advertising (2025.findings-emnlp)

Challenge:	Existing methods for supplementing Large Language Models (LLMs) with knowledge graphs often introduce noise in the retrieval and reasoning pipeline, hindering their ability to integrate external knowledge for complex multi-hop question answering.
Approach:	They propose a framework to enhance LLMs' reasoning capabilities through reflective engagement with knowledge graphs by Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction.
Outcome:	The proposed framework integrates external knowledge into LLMs and trains them to leverage this knowledge for answering questions.

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Yinghao Song, Xiangji Zeng, Shuai Cui, Lu Sun, Zhaowei Liu, Yuan Yuan, Yulu Wang, Hai Zhou, Zhaohan Gong

Unified Contextual Query Rewriting (2023.acl-industry)

Challenge:	Short video advertising scenarios present unique challenges due to data drift (DD) and label drift (LD).
Approach:	They propose to use data drift and label drift to evaluate models under rapidly shifting content distributions and labeling scenarios to assess their generalization capabilities.
Outcome:	The proposed model performs moderately in short video advertising contexts, particularly in handling fine-grained semantics and adapting to shifting instructions.

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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.

Cross-Domain Fake News Detection based on Dual-Granularity Adversarial Training (2025.coling-main)

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Wenjie Wei, Yanyue Zhang, Jinyan Li, Panfei Liu, Deyu Zhou

SDC-LoRA: Singular-Subspace Drift Controlled LoRA to Mitigate Knowledge Forgetting (2026.findings-acl)

Challenge:	Existing approaches to detect fake news in unseen domains are limited by domain-specific training.
Approach:	They propose a cross-domain fake news detection method based on adversarial training . they use a document-level and entity-level model to generate domain-independent representations .
Outcome:	The proposed method can detect fake news in unseen domains with the help of pre-trained language models.

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Geyuan Zhang, Xiaofei Zhou, Shihao Liu, Jingyuan Tian, Jizheng Ma

Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation (2024.lrec-main)

Challenge:	Existing approaches to adapt LLMs to new tasks focus on limiting knowledge forgetting . et al., 2023b) suggest a solution to this problem by limiting update energy in the principal singular subspace of W0 .
Approach:	They propose a low-rank Adaptation (LoRA) that steers early updates away from principal directions and mitigates forgetting by constraining update energy in the principal singular subspace of W0.
Outcome:	The proposed model mitigates forgetting on MMLU, TruthfulQA, and HellaSwag while keeping minor-subspace updates unchanged.

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Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp, Bing Liu, Nikolaos Agadakos

An Evaluation Mechanism of LLM-based Agents on Manipulating APIs (2024.findings-emnlp)

Challenge:	Health coaching is a patient-centered clinical practice that aims to help patients achieve personalized and lifestyle-related goals to enhance their health behaviors.
Approach:	They propose a neuro-symbolic goal summarizer to support health coaches in keeping track of the goals and a text-units-text dialogue generation model that converses with patients and helps them create and accomplish specific goals for physical activities.
Outcome:	The proposed model outperforms existing state-of-the-art models while eliminating the need for predefined schema and corresponding annotations.

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Bing Liu, Zhou Jianxiang, Dan Meng, Haonan Lu

Challenge:	Large Language Models (LLMs) have remarkable capabilities across a variety of tasks, such as language, mathematics, coding, and etc.
Approach:	They propose to decompose tool use capability into seven aspects and form a thorough evaluation schema for generic agents.
Outcome:	The proposed agent acts like a super-APP and can manipulate API-based tools.

SimulSpeech: End-to-End Simultaneous Speech to Text Translation (2020.acl-main)

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Yi Ren, Jinglin Liu, Xu Tan, Chen Zhang, Tao Qin, Zhou Zhao, Tie-Yan Liu

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

Challenge:	SimulSpeech is an end-to-end simultaneous speech to text translation system . conventional approaches to simultaneous speech translation divide the translation process into two stages .
Approach:	They develop an end-to-end simultaneous speech to text translation system which translates speech in source language to text in target language concurrently.
Outcome:	The proposed system achieves reasonable BLEU scores and lower delay compared to full-sentence translation model.

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

CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training (2022.findings-naacl)

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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Xin Wang, Yasheng Wang, Yao Wan, Jiawei Wang, Pingyi Zhou, Li Li, Hao Wu, Jin Liu

RubricBench: Aligning Model-Generated Rubrics with Human Standards (2026.acl-long)

Challenge:	Recent studies have focused on code representation learning, which aims to represent the semantics of source code into distributed vectors.
Approach:	They propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training.
Outcome:	The proposed model outperforms state-of-the-art models on three downstream tasks over five datasets.

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Junyi Zhou, Qiyuan Zhang, Yufei Wang, Fuyuan Lyu, Yidong Ming, Can Xu, Qingfeng Sun, Kai Zheng, Peng Kang, Xue Liu, Chen Ma

Challenge:	Existing benchmarks lack discriminative complexity and ground-truth rubric annotations required for rigorous evaluation.
Approach:	They propose a curated benchmark with 1,147 pairwise comparisons to assess the reliability of rubric-based evaluation.
Outcome:	The proposed benchmarks show that they support diverse domains, exhibit discriminative ability, provide high-quality annotations, and include human-authored rubrics.

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

Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation (2025.findings-emnlp)

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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Dongwon Jung, Qin Liu, Tenghao Huang, Ben Zhou, Muhao Chen

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

Challenge:	Retrieval-augmented generation (RAG) improves large language models by incorporating non-parametric knowledge through evidence retrieved from external sources.
Approach:	They propose a training-free evidence compression technique that makes retrieved evidence more familiar to the target model while seamlessly integrating parametric knowledge from the model.
Outcome:	The proposed technique outperforms the most recent evidence compression baselines across open-domain QA datasets while achieving high compression rates.

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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

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

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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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

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

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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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

Cluster-Former: Clustering-based Sparse Transformer for Question Answering (2021.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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Shuohang Wang, Luowei Zhou, Zhe Gan, Yen-Chun Chen, Yuwei Fang, Siqi Sun, Yu Cheng, Jingjing Liu

Text Clustering

Challenge:	Existing models for encoding long sequences in deep learning suffer from high latency and memory demands.
Approach:	They propose a clustering-based sparse Transformer framework to perform attention across chunked sequences.
Outcome:	The proposed framework achieves state-of-the-art on several major QA benchmarks.

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

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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

Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)

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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Yujia Qin, Cheng Qian, Jing Yi, Weize Chen, Yankai Lin, Xu Han, Zhiyuan Liu, Maosong Sun, Jie Zhou

PRISM: Probabilistic Reward Model with Inherent Structural Modeling (2026.acl-long)

Challenge:	Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found.
Approach:	They investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path.
Outcome:	The proposed model can be used to find low-loss paths between two minima, and to understand how their mode connectivity affects their task knowledge.

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Yuhang Zhou, Yixin Cao, Yuchen Ni, Shihan Dou, Xutian Chen, Ge Zhang, Xiang Liu, Guangnan Ye

Cognitive Modeling

Challenge:	Existing evaluators compress diverse human judgments into a single scalar, leading to brittle alignment and reward hacking.
Approach:	They propose a Gaussian-based reinterpretation of reward evaluation as a conditional distribution and a mixture of Gaussians to capture conflicting preference dimensions.
Outcome:	The proposed model outperforms scalar baselines in accuracy and generalization.

Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation (2022.acl-long)

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Songming Zhang, Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jian Liu, Jie Zhou

CogKTR: A Knowledge-Enhanced Text Representation Toolkit for Natural Language Understanding (2022.emnlp-demos)

Challenge:	Existing approaches to improve neural machine translation use token-level adaptive training . however, standard models make predictions on condition of previous contexts .
Approach:	They propose a target-context-aware metric which can be supplemented by statistical metrics . they propose an adaptive training approach based on token- and sentence-level CBMI .
Outcome:	The proposed model outperforms the Transformer baseline and other similar approaches on English-German and Chinese-English tasks.

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Zhuoran Jin, Tianyi Men, Hongbang Yuan, Yuyang Zhou, Pengfei Cao, Yubo Chen, Zhipeng Xue, Kang Liu, Jun Zhao

Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)

Challenge:	Existing knowledge-enhanced methods are limited to knowledge-intensive tasks.
Approach:	They propose a knowledge-enhanced text representation toolkit for natural language understanding . it combines knowledge acquisition, knowledge representation, knowledge injection and knowledge application .
Outcome:	The proposed toolkit supports knowledge acquisition, knowledge representation, knowledge injection, and knowledge application.

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Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

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

Challenge:	Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available.
Approach:	They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs.
Outcome:	The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer.

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

MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)

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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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou

Knowledge-augmented Financial Market Analysis and Report Generation (2024.emnlp-industry)

Challenge:	Existing datasets exhibit data scarcity and limited coverage of general-domain events.
Approach:	They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types.
Outcome:	The proposed dataset shows that existing methods cannot achieve promising results on the small datasets.

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Yuemin Chen, Feifan Wu, Jingwei Wang, Hao Qian, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Meng Wang

Chain-of-Relations: Faithful and Efficient LLM Reasoning over Knowledge Graphs via Relation-Centric Exploration (2026.findings-acl)

Challenge:	Existing methods to generate financial market analysis text require extensive financial knowledge and skill of financial analysts.
Approach:	They propose a task to generate financial market analysis reports using financial market data using a financial knowledge graph.
Outcome:	The proposed framework outperforms large-scale language models and retrieval-augmented baselines in the financial market analysis generation task.

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Chenhui Liu, Jianpeng Zhou, Jiahai Wang

Knowledge Representation

Challenge:	Existing methods adopt entity-centric exploration that incrementally constructs reasoning paths by selecting and connecting intermediate entities.
Approach:	They propose to use relation-centric exploration to construct reasoning paths by selecting and connecting intermediate entities and to reduce the dependence on entity completeness.
Outcome:	The proposed method outperforms baselines on three benchmark datasets in both F1 score and KG-grounded Rate.

A Compressive Memory-based Retrieval Approach for Event Argument Extraction (2025.coling-main)

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Wanlong Liu, Enqi Zhang, Shaohuan Cheng, Dingyi Zeng, Li Zhou, Chen Zhang, Malu Zhang, Wenyu Chen

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

Challenge:	Existing retrieval-based EAE methods have input length constraints and the gap between the retriever and the inference model.
Approach:	They propose a retrieval-based retrieval mechanism that overcomes input length constraints . they use compressive memory to cache retrieved information and support continuous updates .
Outcome:	The proposed method outperforms retrieval-based methods on three public datasets.

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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

MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)

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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Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li

Revisiting Over-Smoothness in Text to Speech (2022.acl-long)

Challenge:	Existing datasets for event understanding have limited coverage due to complexity of tasks.
Approach:	They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation .
Outcome:	The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction.

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Yi Ren, Xu Tan, Tao Qin, Zhou Zhao, Tie-Yan Liu

C-World: A Computer Use Agent Environment Creator (2026.acl-long)

Challenge:	Non-autoregressive text to speech models ignore correlation in time and frequency domains, causing blurry results.
Approach:	They revisit the problem of over-smoothness in non-autoregressive text to speech models . they use methods that reduce complexity of data distributions and improve modeling methods .
Outcome:	The proposed models achieve better voice quality and faster inference speed than autoregressive models.

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Ziqiao Xi, Shuang Liang, Qi Liu, Jiaqing Zhang, Letian Peng, Fang Nan, Meshal Nayim, Tianhui Zhang, Rishika Mundada, Lianhui Qin, Biwei Huang, Kun Zhou

Challenge:	C-World enables users to build agent environments on demand.
Approach:	They propose a system that enables users to build agent environments on demand.
Outcome:	The proposed system outperforms baselines on 119k samples and achieves Spearman = 0.883 ranking correlation with real execution.

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

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

A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation (2020.acl-main)

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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Shuo Ren, Yu Wu, Shujie Liu, Ming Zhou, Shuai Ma

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

Challenge:	Recent work shows successful methods for unsupervised machine translation (UMT) initialization stage is important since bad initialization may wrongly squeeze the search space and too much noise may hurt the final performance.
Approach:	They propose a retrieval and rewriting based method to better initialize unsupervised translation models.
Outcome:	The proposed method improves translation performance by over 4 BLEU scores.

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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

Weakly-supervised Text Classification Based on Keyword Graph (2021.emnlp-main)

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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Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu, Shuigeng Zhou

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

Challenge:	Existing methods for text classification ignore keyword correlation, thus ignoring it . existing methods treat keywords independently, thus not exploiting correlation between them .
Approach:	They propose a framework to explore keyword-keyword correlation on keyword graph by GNN . they use a self-supervised task to pretrain annotators and fine-tune them .
Outcome:	The proposed method outperforms existing methods on long- and short-text datasets.

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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

MT3: A Synergistic Multi-Task RL Framework for Specializing MLLMs in Text Image Machine Translation (2026.acl-long)

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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Zhaopeng Feng, Yupu Liang, Shaosheng Cao, Jiayuan Su, Jiahan Ren, Zhijie Zhou, Wenxuan Huang, Jian Wu, Zuozhu Liu

ProQA: Structural Prompt-based Pre-training for Unified Question Answering (2022.naacl-main)

Challenge:	Text Image Machine Translation (TIMT) is a critical subfield of machine translation . it requires accurate optical character recognition, robust visual-text reasoning, and high-quality translation a challenge .
Approach:	They propose a multi-task optimization framework to specialize MLLMs into expert TIMT models.
Outcome:	The proposed model outperforms baselines on the latest in-domain MIT-10M benchmark.

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Wanjun Zhong, Yifan Gao, Ning Ding, Yujia Qin, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan

RedCoder: Automated Multi-Turn Red Teaming for Code LLMs (2026.acl-long)

Challenge:	Existing QA research on question answering is focused on specific question types, knowledge domains, or reasoning skills.
Approach:	They propose a unified QA paradigm that solves various tasks through a single model.
Outcome:	The proposed model improves QA-centric ability on 11 QA benchmarks.

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Wenjie Jacky Mo, Qin Liu, Xiaofei Wen, Dongwon Jung, Hadi Askari, Wenxuan Zhou, Zhe Zhao, Muhao Chen

AMO-Bench: Large Language Models Still Struggle in High School Math Competitions (2026.findings-acl)

Challenge:	Existing red-teaming approaches for code generation rely on extensive human effort and are prone to generating malicious code under adversarial environments.
Approach:	They propose a red-teaming agent that engages victim models in multi-turn conversations to elicit vulnerable code.
Outcome:	Experiments show that RedCoder outperforms red-teaming methods in inducing vulnerabilities in code generation.

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Junlin Liu, Shengnan An, Shuang Zhou, Dan Ma, Yehao Lin, Xinxuan Lv, Xuanlin Wang, Xiaoyu Li, Ziwen Wang, Xuezhi Cao, Xunliang Cai

Rhetorically Controlled Encoder-Decoder for Modern Chinese Poetry Generation (P19-1)

Challenge:	Existing benchmarks for mathematical reasoning are becoming less effective due to performance saturation.
Approach:	They propose to use a mathematical reasoning benchmark with Olympiad difficulty to evaluate top-tier LLMs.
Outcome:	The proposed benchmarks are cross-validated by experts to meet IMO difficulty standards and entirely original problems to prevent performance leakages from data memorization.

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Zhiqiang Liu, Zuohui Fu, Jie Cao, Gerard de Melo, Yik-Cheung Tam, Cheng Niu, Jie Zhou

Scalable Prompt Generation for Semi-supervised Learning with Language Models (2023.findings-eacl)

Challenge:	Rhetoric is a vital element in modern Chinese poetry, and plays an essential role in improving its aesthetics. however, to date, it has not been considered in research on automatic poetry generation.
Approach:	They propose a rhetorically controlled encoder-decoder for modern Chinese poetry generation . their model captures various rhetorical patterns in an encoder and incorporates mixtures .
Outcome:	The proposed model outperforms state-of-the-art methods in terms of fluency, coherence, meaningfulness, and rhetorical aesthetics.

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Yuhang Zhou, Suraj Maharjan, Beiye Liu

Rethinking Personality Assessment from Human-Agent Dialogues: Fewer Rounds May Be Better Than More (2025.findings-emnlp)

Challenge:	Prompt-based learning methods in semi-supervised learning (SSL) settings have been shown to be effective on multiple natural language understanding datasets and tasks.
Approach:	They propose to use a set of prompt tokens to create diverse prompt models and a varying number of soft prompt token to encourage language models to learn different prompts.
Outcome:	The proposed method achieves the best average accuracy of 71.5% in different few-shot learning settings.

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Baiqiao Zhang, Zhifeng Liao, Xiangxian Li, Chao Zhou, Juan Liu, Xiaojuan Ma, Yulong Bian

Mitigating Inconsistencies in Multimodal Sentiment Analysis under Uncertain Missing Modalities (2022.emnlp-main)

Challenge:	Existing personality assessment datasets based on natural language do not consider interactivity.
Approach:	They propose to use a Chinese dataset to study the effects of different interaction rounds and agent personalities on personality assessment.
Outcome:	The proposed dataset contains 1260 interaction rounds between humans and agents with different personalities.

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Jiandian Zeng, Jiantao Zhou, Tianyi Liu

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

Challenge:	Existing studies ignore the inconsistency phenomenon of missing modality in multimodal sentiment analysis . neglect of missing modalities may lead to incorrect semantic results .
Approach:	They propose an ensemble-based Missing Modality Reconstruction network to detect and recover missing modality features.
Outcome:	The proposed method is superior to existing methods on CMU-MOSI and IEMOCAP datasets.

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

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

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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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

Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.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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Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun

Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective (2024.findings-acl)

Challenge:	Current language model-driven agents lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions.
Approach:	They propose a benchmark to inspect users’ implicit intentions through explicit queries and a model expert as the upstream in agent design to enhance user-agent interaction.
Outcome:	The proposed approach excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency.

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Yijie Chen, Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

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

Challenge:	Existing studies decompose complex tasks into intermediate steps by prompting strategies, such as Chain-of-Thought and its variants.
Approach:	They propose to use code comments as natural logic pivot between natural language and code language to boost the code generation ability of code LLMs.
Outcome:	The proposed method significantly improves the code pass rate on humanEval and MBPP, while the robustness of the logical comment decoding strategy is higher than the Chain-of-thoughts prompting.

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

DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)

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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Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, Dacheng Tao

PokeMQA: Programmable knowledge editing for Multi-hop Question Answering (2024.acl-long)

Challenge:	Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models .
Approach:	They propose a Dual-Binarization method that takes 2-bit-width and binarization into account . they propose DB-LLM, which uses a 2-bit binarized weighted model to represent weights efficiently .
Outcome:	The proposed method surpasses the current State-of-the-Art in ultra-low bit quantization and achieves 20% reduction in computational consumption compared to the SOTA method under the same bit-width.

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Hengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu, Ruobing Wang, Xin Wang

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

Challenge:	Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine’s comprehension and reasoning abilities, where large language models (LLMs) have widely achieved the human-comparable performance.
Approach:	They propose a framework to edit multi-hop question models to update model with up-to-date facts while avoiding expensive re-training or fine-tuning.
Outcome:	The proposed framework outperforms all competitors in multi-hop question answering tasks and consistently produces reliable reasoning process.

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

Reinforcement Learning with Semantic Rewards Enables Low-Resource Language Expansion without Alignment Tax (2026.findings-acl)

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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Zeli Su, Ziyin Zhang, Zhou Liu, Xuexian Song, Zhankai Xu, Longfei Zheng, Xiaolu Zhang, Rong Fu, Guixian Xu, Wentao Zhang

DARM: Distribution-Aware Reward Modeling by Alleviating Biases from Low Preference-Context Dependency Data (2026.acl-long)

Challenge:	Extending large language models to low-resource languages often incurs an "alignment tax" token-level fine-tuning enforces token-level surface imitation on narrow and biased data distributions.
Approach:	They propose a semantic-space alignment paradigm powered by group-level semantic rewards instead of likelihood maximization.
Outcome:	The proposed model acquires low-resource capa- bilities while mitigating alignment tax on Tibetan–Chinese machine translation and Ti- betan headline generation.

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Shaofan Liu, Guoqiang Zhang, Shihan Dou, Huiyuan Zheng, Yiming Zhou, Junjie Ye, Shaowen Wang, Shichun Liu, Jiazheng Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Existing methods for training reward models are vulnerable to context neglect and degraded accuracy.
Approach:	They propose distribution-aware reward modeling that augments the RM objective with a conditional mutual information regularizer that maximizes context and the predicted reward conditioned on the response.
Outcome:	The proposed model improves performance in RLHF and improves accuracy in other settings.

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

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

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

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

LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)

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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Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun

Challenge:	Generating high-quality long-form survey articles poses significant challenges to AI Agent systems.
Approach:	They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines .
Outcome:	The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning.

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)

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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science (2025.findings-emnlp)

Challenge:	Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output.
Approach:	They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities.
Outcome:	The proposed model can be trained stably without any alterations to existing models or training paradigms.

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An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding

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

Challenge:	Large language models (LLMs) have advanced the automation of data science workflows, yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice.
Approach:	They propose a benchmark to evaluate how large language models handle external domain knowledge in tabular prediction tasks.
Outcome:	The proposed model evaluates whether it can critically leverage external domain knowledge as human data scientists do in practice.

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

Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023.emnlp-demo)

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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Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, Jiawei Han

Explore Spurious Correlations at the Concept Level in Language Models for Text Classification (2024.acl-long)

Challenge:	Reaction Miner is a system designed to extract chemical reactions from raw scientific PDFs.
Approach:	They propose a system that extracts chemical reactions directly from raw scientific PDFs.
Outcome:	The proposed system can extract chemical reactions from raw scientific PDFs.

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Yuhang Zhou, Paiheng Xu, Xiaoyu Liu, Bang An, Wei Ai, Furong Huang

DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)

Challenge:	Language models have demonstrated remarkable performance in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods.
Approach:	They propose a method to assess concept bias in models during fine-tuning and in-context learning using ChatGPT.
Outcome:	The proposed method outperforms token removal approaches and is validated through extensive testing.

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Wenting Zhao, Ye Liu, Tong Niu, Yao Wan, Philip Yu, Shafiq Joty, Yingbo Zhou, Semih Yavuz

AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models (2024.findings-emnlp)

Challenge:	Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources.
Approach:	They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation .
Outcome:	The proposed model outperforms previous approaches by a significant margin in QA tasks over text.

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Xiyang Wu, Tianrui Guan, Dianqi Li, Shuaiyi Huang, Xiaoyu Liu, Xijun Wang, Ruiqi Xian, Abhinav Shrivastava, Furong Huang, Jordan Boyd-Graber, Tianyi Zhou, Dinesh Manocha

Learning to Ask Conversational Questions by Optimizing Levenshtein Distance (2021.acl-long)

Challenge:	Large vision-language models are prone to hallucinations, where contextual cues in an image can trigger the language module to produce overconfident and incorrect reasoning about abnormal or hypothetical objects.
Approach:	They propose to automate the generation of hallucination-related questions using images . they propose to use three image manipulation strategies to induce hallucinosity .
Outcome:	The proposed approach reduces human bias in crafting such examples and improves accuracy.

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Zhongkun Liu, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Maarten de Rijke, Ming Zhou

Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy (2024.findings-emnlp)

Challenge:	Existing methods for estimating maximum likelihood are limited by easily learned tokens . Existing systems that generate questions based on dialogue context are limited in their ability to learn tokens.
Approach:	They propose a framework that optimizes the minimum Levenshtein distance through explicit editing actions.
Outcome:	The proposed framework outperforms state-of-the-art methods on two benchmark datasets and generalizes well on unseen data.

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Liyan Xu, Zhenlin Su, Mo Yu, Jin Xu, Jinho Choi, Jie Zhou, Fei Liu

Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges (2025.acl-long)

Challenge:	Existing studies have focused on specialized BERT-variants and recent LLMs to reason inconsistencies.
Approach:	They propose to incorporate task-specific taxonomy into inferences to facilitate both zero-shot and supervised paradigms.
Outcome:	The proposed model outperforms specialized non-LLM and recent LLM models in a number of domains.

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Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank

P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains (2024.findings-emnlp)

Challenge:	linguistics studies how context influences meaning of language and how people use it to convey implied meanings, emotions, and intentions.
Approach:	They analyze task designs, data collection methods, evaluation approaches and their relevance to real-world applications.
Outcome:	The findings highlight emerging trends, challenges, and gaps in existing benchmarks . the findings will contribute to more nuanced and context-aware NLP models .

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Simeng Han, Aaron Yu, Rui Shen, Zhenting Qi, Martin Riddell, Wenfei Zhou, Yujie Qiao, Yilun Zhao, Semih Yavuz, Ye Liu, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Dragomir Radev, Rex Ying, Arman Cohan

Target-Sensitive Memory Networks for Aspect Sentiment Classification (P18-1)

Challenge:	Existing methods on understanding the capabilities of LLMs in logical reasoning rely on binary entailment classification or synthetically derived rationales.
Approach:	They propose to annotate a human-annotated dataset consisting of diverse and complex reasoning chains for a set of realistic logical reasoning stories also written by humans.
Outcome:	The proposed model outperforms existing methods on understanding the capabilities of LLMs in logical reasoning by 10% or more.

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Shuai Wang, Sahisnu Mazumder, Bing Liu, Mianwei Zhou, Yi Chang

Cocktail: A Comprehensive Information Retrieval Benchmark with LLM-Generated Documents Integration (2024.findings-acl)

Challenge:	Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis.
Approach:	They propose to use memory networks to deal with ASC using aspect and sentence terms and use them to classify the sentiment polarity.
Outcome:	The proposed techniques can be implemented in a variety of contexts and their effectiveness is evaluated.

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Sunhao Dai, Weihao Liu, Yuqi Zhou, Liang Pang, Rongju Ruan, Gang Wang, Zhenhua Dong, Jun Xu, Ji-Rong Wen

Zero-Shot Dialogue State Tracking via Cross-Task Transfer (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) have led to an influx of AI-generated content on the internet, transforming corpus of Information Retrieval (IR) systems from human-written to a coexistence with LLM-generated contents.
Approach:	They propose a benchmark named Cocktail that compares IR models with LLMs to find relevant documents and passages from a corpus.
Outcome:	The proposed benchmark aims to evaluate IR models in the mixed-sourced data landscape of the LLM era.

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Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Zhenpeng Zhou, Paul Crook, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung

Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (P18-1)

Challenge:	Existing approaches to training a dialogue state tracking model require extensive annotated dialogue data.
Approach:	They propose to transfer cross-task knowledge from general question answering corpora to QA model that can handle zero-shot DST.
Outcome:	The proposed model improves existing zero-shot and few-shot results on MultiWoz and shows better generalization ability in unseen domains.

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Xiangyang Zhou, Lu Li, Daxiang Dong, Yi Liu, Ying Chen, Wayne Xin Zhao, Dianhai Yu, Hua Wu

Small Pre-trained Language Models Can be Fine-tuned as Large Models via Over-Parameterization (2023.acl-long)

Challenge:	Existing models for matching dialogue responses rely on semantic and functional dependencies . a recent study only uses the last utterance in context for matching a reply .
Approach:	They propose a model that matches a response with its multi-turn context using attention.
Outcome:	The proposed model outperforms the state-of-the-art models on two large-scale multi-turn response selection tasks.

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Ze-Feng Gao, Kun Zhou, Peiyu Liu, Wayne Xin Zhao, Ji-Rong Wen

Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding (2026.acl-long)

Challenge:	Large pre-trained language models (PLMs) have shown remarkable performance in various natural language processing tasks, outperforming small PLMs by a large margin.
Approach:	They propose to scale up parameters of pre-trained language models only during fine-tuning to benefit from over-parameterization.
Outcome:	The proposed approach can significantly boost the fine-tuning performance of small PLMs and even help small PDMs outperform 3 parameterized larger ones.

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Yuhang Zhou, Mingrui Zhang, Ke Li, Mingyi Wang, Qiao Liu, Qifei Wang, Jiayi Liu, Fei Liu, Serena Li, Weiwei LI, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang

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

Challenge:	Large language models (LLMs) have shown promise on understanding and reasoning over tables, but current approaches remain limited.
Approach:	They propose a multi-agent framework that decomposes table reasoning into three specialized roles: planning, coding, and answering.
Outcome:	The proposed framework decomposes table reasoning into three specialized roles: planning, coding, and answering.

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

UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models (2026.acl-demo)

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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Qundong Shi, Jie Zhou, Biyuan Lin, Junbo Cui, Guoyang Zeng, Yixuan Zhou, Ziyang Wang, Xin Liu, Zhen Luo, Yudong Wang, Zhiyuan Liu

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

Challenge:	Existing evaluation frameworks for audio foundation models are heavily reliant on English, making it difficult to objectively assess models’ performance on Chinese.
Approach:	They propose a unified framework that supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.
Outcome:	The proposed framework supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.

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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

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

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

Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones? (2024.emnlp-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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Zhe Yang, Yichang Zhang, Tianyu Liu, Jian Yang, Junyang Lin, Chang Zhou, Zhifang Sui

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

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

SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation (2026.findings-acl)

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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Long Li, Zhijian Zhou, Jiangxuan Long, Peiyang Liu, Weidi Xu, Zhe Wang, Shirui Pan, Chao Qu

Transparentize the Internal and External Knowledge Utilization in LLMs with Trustworthy Citation (2025.findings-acl)

Challenge:	Agentic SQL is a framework for multiturn agent learning, but it is limited to single-turn paradigms.
Approach:	They propose a framework that provides a universal two-tiered reward mechanism for credit assignment . they propose 'Aggregated Trajectory Reward' to resolve multi-turn credit assignment.
Outcome:	The proposed framework outperforms SOTA Arctic-Text2SQL-R1-7B on BIRD and Spider 2.0 using identical models.

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Jiajun Shen, Tong Zhou, Yubo Chen, Delai Qiu, Shengping Liu, Kang Liu, Jun Zhao

Conditional Dichotomy Quantification via Geometric Embedding (2025.acl-long)

Challenge:	citation generation and retrieval-augmented generation are still lacking in large language models due to hallucinations.
Approach:	They propose a retrieval-augmented citation generation task that requires models to generate citations considering both external and internal knowledge while providing trustworthy references.
Outcome:	The proposed method achieves better performance across scenarios compared to baselines . retrieval quality, question types, and model knowledge influence trustworthiness .

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Shaobo Cui, Wenqing Liu, Yiyang Feng, Jiawei Zhou, Boi Faltings

LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback (2025.findings-acl)

Challenge:	Existing methods that rely on semantic similarity fail to capture the nuanced oppositional dynamics essential for these applications.
Approach:	They propose a task that formalizes the measurement of conditional dichotomy by using a dichotomian framework.
Outcome:	The proposed framework provides carefully constructed datasets covering debate, defeasible inference, and causal reasoning scenarios.

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Bofei Gao, Zefan Cai, Runxin Xu, Peiyi Wang, Ce Zheng, Runji Lin, Keming Lu, Dayiheng Liu, Chang Zhou, Wen Xiao, Tianyu Liu, Baobao Chang

Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension (2020.coling-main)

Challenge:	Existing mathematical verifiers are trained with binary classification labels, which are not informative enough for the model to accurately assess the solutions.
Approach:	They propose a natural language feedback-enhanced verifier that can validate the correctness of response generated by policy models by constructing automatically generated training data and a two-stage training paradigm.
Outcome:	The proposed verifier significantly improves in verification and reinforcement learning and alleviates data-demanding problems of the reward model.

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Xuemiao Zhang, Kun Zhou, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, Junfei Liu

MusKGC: A Flexible Multi-source Knowledge Enhancement Framework for Open-World Knowledge Graph Completion (2025.emnlp-main)

Challenge:	Existing approaches to filter noise for machine reading comprehension (MRC) are difficult to control and introduce noisy data.
Approach:	They propose a hierarchical loss correction strategy to avoid fitting noise and enhance clean supervision signals by using an unsupervisedly fitted Gaussian mixture model and a hard bootstrapping loss method.
Outcome:	The proposed methods can help improve models significantly on weakly supervised machine reading comprehension datasets.

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Xin Song, Liu Haiyan, Haiyang Wang, Ye Wang, Kai Chen, Bin Zhou

CXR Data Annotation and Classification with Pre-trained Language Models (2022.coling-1)

Challenge:	Open-world knowledge graph completion (KGC) aims to infer novel facts by enriching existing graphs with external knowledge sources while maintaining semantic consistency under the open-world assumption (OWA).
Approach:	They propose a multi-source knowledge enhancement framework based on an open-world assumption (OWA) that integrates external knowledge sources and a new evaluation strategy to validate new facts.
Outcome:	The proposed model achieves SOTA performance across benchmarks and the evaluation strategy effectively assesses new facts under OWA.

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Nina Zhou, Ai Ti Aw, Zhuo Han Liu, Cher heng Tan, Yonghan Ting, Wen Xiang Chen, Jordan sim zheng Ting

RMSSinger: Realistic-Music-Score based Singing Voice Synthesis (2023.findings-acl)

Challenge:	Existing tools for clinical data annotation are limited to specific institutions due to differences in writing style, structure, language use and label definition.
Approach:	They propose a weak supervision annotation framework with two improvements over existing ones . the framework provides an efficient form of sample selection and data auto-annotation .
Outcome:	The proposed framework provides better results for clinical data annotation tasks compared to existing frameworks.

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Jinzheng He, Jinglin Liu, Zhenhui Ye, Rongjie Huang, Chenye Cui, Huadai Liu, Zhou Zhao

Not The End of Story: An Evaluation of ChatGPT-Driven Vulnerability Description Mappings (2023.findings-acl)

Challenge:	Existing methods for singing voice synthesis are limited to fine-grained music scores . manual adjustment destroys regularity of note durations, making fine-grain music scores "crushed"
Approach:	They propose a method to synthesize singing voices given realistic music scores . they use real-music-score-based Singing Voice Synthesis to generate high-quality voices .
Outcome:	The proposed method eliminates manual annotation and simplifies phoneme-level mel-note alignment.

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Xin Liu, Yuan Tan, Zhenghang Xiao, Jianwei Zhuge, Rui Zhou

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

Challenge:	Existing data proves that ChatGPT performs no less than humans in text generation and knowledge Q&A.
Approach:	They propose to use ChatGPT to map vulnerabilities to common weakness enumeration (CWE), common attack pattern ennumeration and classification (ATT&CK) techniques and other classifications.
Outcome:	The proposed method performs better than human experts on many tasks, but it can't replace professional security engineers in vulnerability analysis.

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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

DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check (2025.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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Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

Let’s Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models (2024.lrec-main)

Challenge:	Chinese spelling check (CSC) tasks require that incorrect characters are usually similar to the correct ones in either phonetics or glyph.
Approach:	They propose a plug-and-play decoding intervention with similarity of characters module for Chinese spelling check (CSC) they propose to incorporate phonetic and glyph similarities only during the inference phase.
Outcome:	The proposed method significantly improves Chinese spelling check models on benchmarks and on benchmark datasets.

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Shunyu Liu, Jie Zhou, Qunxi Zhu, Qin Chen, Qingchun Bai, Jun Xiao, Liang He

EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning (2021.emnlp-main)

Challenge:	Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models.
Approach:	They propose a diffusion model which extracts aspects step by step and learns a denoising process that progressively restores them in a reverse manner.
Outcome:	Empirical evaluations on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models.

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Hao Zhou, Minlie Huang, Yong Liu, Wei Chen, Xiaoyan Zhu

Dissecting Human and LLM Preferences (2024.acl-long)

Challenge:	Existing knowledge-grounded conversation models lack knowledge that occurs in training data, resulting in incomplete knowledge generation.
Approach:	They propose an Entity-Agnostic Representation Learning method to introduce knowledge graphs to informative conversation generation using context of conversations and relational structure of knowledge graph.
Outcome:	The proposed model generates more informative, coherent, and natural responses than baseline models.

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Junlong Li, Fan Zhou, Shichao Sun, Yikai Zhang, Hai Zhao, Pengfei Liu

AdaFuse: Adaptive Ensemble Decoding for Large Language Models (2026.acl-long)

Challenge:	a recent study shows that human and Large Language Model preferences are important for model fine-tuning and evaluation.
Approach:	They dissect the preferences of human and 32 different Large Language Models to understand their quantitative composition.
Outcome:	The proposed model is compared with 32 different large language models using real-world user-model conversations.

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Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He

Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement (2023.findings-emnlp)

Challenge:	Existing ensemble approaches to large language models lack flexibility for mid-generation adaptation.
Approach:	They propose an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation.
Outcome:	The proposed framework outperforms existing ensemble frameworks on open-domain QA, arithmetic reasoning, and machine translation tasks.

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Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Jia Liu, Tao Gui, Qi Zhang, Xuanjing Huang

Self-Guided Alignment: Adaptive Preference Sensing for Multi-Objective Generation (2026.acl-long)

Challenge:	Existing prompting methods have been used to enhance multistep reasoning capabilities of large language models, but they have overlooked the potential of formulating higher-quality problems.
Approach:	They propose a method that starts from the problem side and refines problems to be more comprehensible and solvable for models.
Outcome:	The proposed method achieves notable and consistent effectiveness on five reasoning benchmarks across different models.

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Ning Wang, Zhanyang Liu, Taotao Zhou, Xinrui Zhang, Zongru Shao, Haojie Zhou

AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels (2025.findings-emnlp)

Challenge:	Existing approaches to align LLMs with diverse human values rely on ground-truth scores . existing approaches implicitly approximate an average-user preference, thereby failing to capture heterogeneity of human values or accommodate conflicting user needs.
Approach:	They propose a framework that transforms passive reward dependency into an intrinsic adaptive sensing capability.
Outcome:	The proposed framework outperforms state-of-the-art models in multiple model scales and improves preference alignment.

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Lei Li, Xiangxu Zhang, Xiao Zhou, Zheng Liu

FedGUI: Benchmarking Federated GUI Agents across Heterogeneous Platforms, Devices, and Operating Systems (2026.findings-acl)

Challenge:	Effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data.
Approach:	They propose a framework that leverages large language models to generate hypothetical documents . they also propose 'CMIRB' to provide a rigorous evaluation suite .
Outcome:	The proposed framework outperforms HyDE in retrieval accuracy and generalization . it leverages large language models to generate hypothetical documents conditioned on a query .

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WenHao Wang, Haoting Shi, Mengying Yuan, Yiquan Lin, Panrong Tong, Hanzhang Zhou, Guangyi Liu, Pengxiang Zhao, Yue Wang, Siheng Chen

Incorporating Global Information in Local Attention for Knowledge Representation Learning (2021.findings-acl)

Challenge:	a lack of benchmarks capture real-world, cross-platform heterogeneity in GUI training . traditional methods to train GUI agents rely on centralized data collection and manual labeling .
Approach:	They propose a benchmark for developing and evaluating federated GUI agents across mobile, web and desktop platforms.
Outcome:	The proposed benchmarks show that cross-platform collaboration improves performance and identify platform and OS as the most influential factors.

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Yu Zhao, Han Zhou, Ruobing Xie, Fuzhen Zhuang, Qing Li, Ji Liu

FocalPO: Enhancing Preference Optimizing by Focusing on Correct Preference Rankings (2025.acl-short)

Challenge:	Graph Attention Networks (GATs) are a promising model that takes advantage of localized attention mechanism to perform knowledge representation learning (KRL) on graph-structure data.
Approach:	They propose to incorporate global information into the GAT family of models by using an attention-based global random walk algorithm.
Outcome:	Experimental results on KG entity prediction against the state-of-the-arts demonstrate the effectiveness of the proposed model.

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Tong Liu, Xiao Yu, Wenxuan Zhou, Jindong Gu, Volker Tresp

Challenge:	Efficient preference optimization algorithms such as Direct Preference Optimization (DPO) have become a popular approach in aligning large language models with human preferences.
Approach:	They propose a preference optimization variant that instead down-weighs misranked preference pairs and prioritizes enhancing the model’s understanding of pairs that it can already rank correctly.
Outcome:	The proposed model outperforms DPO on benchmarks like Alpaca Eval 2.0 and Arena-Hard using mistral-base-7B and Llama-3-Instruct-8B with the introduced hyperparameter fixed.

Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment (2024.emnlp-main)

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Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Zexu Sun, Bowen Sun, Huimin Chen, Ruobing Xie, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun

Challenge:	Existing algorithms for achieving optimal alignment are mostly unidirectional . a recent study suggests that large language models can be ground with evident preferences .
Approach:	They propose to ground large language models with evident preferences . they propose to use controllable preference optimization to specify different objectives .
Outcome:	The proposed models can provide responses that match various preferences among the ”3H” desiderata.

APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs (2025.acl-long)

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Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Sun Ao, Hao Zhou, Jie Zhou, Zhiyuan Liu, Maosong Sun

Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems (2022.findings-acl)

Challenge:	Long-context inference is crucial for advancing large language models, but its prefill speed remains a bottleneck.
Approach:	They propose an efficient long-context inference framework that leverages multi-host approximate attention to enhance prefill speed.
Outcome:	The proposed framework achieves speedups of 9.2, 4.2, and 1.6 without any degradation in performance.

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Zhongli Li, Wenxuan Zhang, Chao Yan, Qingyu Zhou, Chao Li, Hongzhi Liu, Yunbo Cao

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

Challenge:	Existing models memorize procedures from context and rely on shallow heuristics to solve MWPs.
Approach:	They propose a contrastive learning approach where the neural network perceives the divergence of patterns.
Outcome:	The proposed method greatly improves performance in monolingual and multilingual settings.

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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

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

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

VIGIL: Defending LLM Agents Against Tool-Stream Injection via Verify-Before-Commit (2026.acl-long)

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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Junda Lin, Zhaomeng Zhou, Zhi Zheng, Shuochen Liu, Tong Xu, Yong Chen, Enhong Chen

Generative Bridging Network for Neural Sequence Prediction (N18-1)

Challenge:	Existing defenses for indirect prompt injection are limited by static protection mechanisms . existing models prioritize injected rules due to strict alignment, whereas static protections sever the feedback loop required for adaptive reasoning.
Approach:	They propose a framework that shifts the paradigm from restrictive isolation to a verify-before-commit protocol.
Outcome:	The proposed framework outperforms state-of-the-art dynamic defenses by reducing the attack success rate by over 22% while more thandoubling utility under attack compared to static baselines.

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Wenhu Chen, Guanlin Li, Shuo Ren, Shujie Liu, Zhirui Zhang, Mu Li, Ming Zhou

Traffic Light or Light Traffic? Investigating Phrasal Semantics in Large Language Models (2024.findings-emnlp)

Challenge:	Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing.
Approach:	They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence.
Outcome:	The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden.

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Rui Meng, Ye Liu, Lifu Tu, Daqing He, Yingbo Zhou, Semih Yavuz

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

Challenge:	Phrases are fundamental linguistic units through which humans convey semantics.
Approach:	They assess the capacity of API-based large language models to comprehend phrase semantics . they use three human-annotated datasets to analyze their results .
Outcome:	The proposed model outperforms embedding-based methods in phrase semantic reasoning tasks . the proposed model does not show significant advantage over fine-tuned methods .

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

PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension (2025.acl-long)

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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Kun Ouyang, Yuanxin Liu, Shicheng Li, Yi Liu, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

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

Challenge:	Existing benchmarks on punchline comprehension suffer from language shortcuts that allow models to rely on text, lack of question diversity, and narrow focus on a specific domain of multimodal content.
Approach:	They propose a multimodal punchline comprehension benchmark to assess models' ability to comprehend punchlines.
Outcome:	The proposed model surpasses in-context learning and chain-of-thought in punchline comprehension.

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

ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training (2020.findings-emnlp)

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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Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou

ProcessBench: Identifying Process Errors in Mathematical Reasoning (2025.acl-long)

Challenge:	Existing sequence-to-sequence models are optimized for future n-gram prediction and n stream self-attention mechanism.
Approach:	They propose a self-supervised objective called future n-gram prediction and the proposed n stream self-attention mechanism to optimize the model for sequence-to-sequence learning.
Outcome:	The proposed model achieves state-of-the-art on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks compared to the models using the same scale pre-training corpus.

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Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin

A Study of Non-autoregressive Model for Sequence Generation (2020.acl-main)

Challenge:	Existing models fail to generalize to more challenging math problems, authors say . existing benchmarks related to assessing language models' reasoning process are limited .
Approach:	They propose a tool to measure language models' ability to identify erroneous steps in reasoning . they use two types of models: process reward models and critic models .
Outcome:	The proposed model outperforms existing models in evaluating language models' reasoning process . the best open-source model has demonstrated the critique capability competitive with the proprietary model .

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Yi Ren, Jinglin Liu, Xu Tan, Zhou Zhao, Sheng Zhao, Tie-Yan Liu

Beyond Single-View Detection: A Dual-Space Reasoning Framework for Interpretable Harmful Meme Understanding (2026.acl-long)

Challenge:	Non-autoregressive (NAR) models generate all tokens in parallel, resulting in faster generation speed compared to autoregressive models.
Approach:	They propose to use knowledge distillation and source-target alignment to bridge the gap between NAR and autoregressive models in various tasks.
Outcome:	The proposed techniques can speed up NAR models in some tasks but not all . the proposed techniques reduce target token dependency while allowing for faster inference .

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Wenqing Hou, Hongkui Tu, Ye Wang, Yue Zhang, Yuying Liu, Dong Zhu, Liqun Gao, Bin Zhou

Reliable Use of Lemmas via Eligibility Reasoning and Section-Aware Reinforcement Learning (2026.acl-short)

Challenge:	Existing methods for identifying harmful memes rely on modal alignment or black-box classifiers . BPDMoE-Hate provides visual explanations for viewpoint selection and hierarchical structuring .
Approach:	They propose a framework that conceptualizes harmful meme detection as a process of "viewpoint decoupling and hierarchical fusion" they propose BPDMoE-Hate, which generates adversarial binary perspectives via VLMs and incorporates an adaptive viewpoint gating to facilitate viewpoint selection.
Outcome:	The proposed framework surpasses existing methods in performance and provides visual explanations for viewpoint selection and hierarchical structuring.

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Zhikun Xu, Xiaodong Yu, Ben Zhou, Jiang Liu, Jialian Wu, Ze Wang, Ximeng Sun, Hao Chen, Zicheng Liu

GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent (2025.acl-long)

Challenge:	Recent large language models (LLMs) perform strongly on mathematical benchmarks but often import conclusions without validating assumptions.
Approach:	They propose a model that encodes a lemma specification and trains with reinforcement learning and section-aware loss masking to assign penalty to the section responsible for errors.
Outcome:	The proposed model performs well on benchmarks but often misapplyes lemmas . the model is able to encode the specification and train with reinforcement learning .

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Bin Xie, Rui Shao, Gongwei Chen, Kaiwen Zhou, Yinchuan Li, Jie Liu, Min Zhang, Liqiang Nie

Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)

Challenge:	GUI automation is a key challenge in dynamic environments.
Approach:	They propose a training-free GUI agent that integrates two mechanisms to explore trajectories in GUIs.
Outcome:	The proposed GUI-explorer shows significant improvements over existing agents.

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Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, Junbo Zhao

MotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM Ideation (2025.findings-emnlp)

Challenge:	Autoregressive (AR) language models dominate modern natural language processing due to strong likelihood-based training objectives and reliable left-to-right decoding.
Approach:	They characterize MDLM behavior along two dimensions: parallelism strength and generation order . authors propose a Generate-then-Edit paradigm that mitigates dependency loss .
Outcome:	The proposed model improves on tasks that require "backward information" the Generate-then-Edit paradigm improves parallel decoding efficiency while reducing dependency loss.

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Xinping Lei, Tong Zhou, Yubo Chen, Kang Liu, Jun Zhao

From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have limitations in grounding ideas and mitigating confirmation bias during refinement.
Approach:	They propose a framework that integrates a Motivational Knowledge Graph with a Q-Driven Socratic Ideator to enhance LLM ideation.
Outcome:	The proposed framework enhances LLM ideation by integrating a Motivational Knowledge Graph with a Q-Driven Socratic Ideator.

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Guo Tang, Ke Cheng, Huiming Fan, Heng Chang, Wenxiang Zheng, Xianhao Ou, Junjia Xiang, Ming Liu, Yujun Zhou, Li Lanyu, Bing Qin

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)

Challenge:	Existing Large Language Models (LLMs) struggle with implicit modality alignment and suboptimal graph linearization.
Approach:	They propose a training-free, test-time adaptive framework that reframes TKG prediction as explicit evidence-driven reasoning.
Outcome:	ExE-LLM outperforms fully trained graph neural networks on four benchmarks . it achieves SOTA performance in inductive settings, significantly outperforming fully trained neural networks .

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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin

M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation (2026.acl-long)

Challenge:	rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research.
Approach:	They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication.
Outcome:	The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication.

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Hao Wang, Linlong Xu, Heng Liu, Yangyang Liu, Xiaohu Zhao, Bo Zeng, Liangying Shao, Yichen Dong, Xinwei Wu, Jiang Zhou, Tianyu Dong, Xiangxiang Zeng, Longyue Wang, Weihua Luo

FastDiff 2: Revisiting and Incorporating GANs and Diffusion Models in High-Fidelity Speech Synthesis (2023.findings-acl)

Challenge:	prevailing methods for machine translation are often hindered by misleading reward signals.
Approach:	They propose a framework that aligns large language models to human preferences . they propose 'M2PO' to correct the bias towards partial errors .
Outcome:	The proposed framework outperforms open-source models and achieves parity with proprietary models.

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Rongjie Huang, Yi Ren, Ziyue Jiang, Chenye Cui, Jinglin Liu, Zhou Zhao

Neural Gibbs Sampling for Joint Event Argument Extraction (2020.aacl-main)

Challenge:	Experimental results show that Generative adversarial networks sacrifice sample diversity for quality and speed, while diffusion models exhibit outperformed sample quality and diversity at a high computational cost.
Approach:	They propose to combine GANs and diffusion probabilistic models to achieve better sample quality and diversity.
Outcome:	The proposed models outperform GANs and diffusion models in speech synthesis . the proposed models enjoy an efficient 4-step sampling process and exhibit better sample diversity .

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Xiaozhi Wang, Shengyu Jia, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Jie Zhou

LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation (2024.findings-acl)

Challenge:	Existing methods for event argument extraction cannot adequately model the correlation between event arguments and their roles.
Approach:	They propose a Bayesian model to jointly extract event arguments using Gibbs sampling . they train two neural networks to model prior distribution and conditional distribution over event arguments .
Outcome:	The proposed model can achieve comparable results to existing methods on two widely-used datasets.

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Zengkui Sun, Yijin Liu, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou

MetaScale: Test-Time Scaling with Evolving Meta-Thoughts (2026.findings-acl)

Challenge:	Existing LT strategies cannot indicate the desired target language on zero-shot translation, i.e., the off-target issue.
Approach:	They propose a language converter strategy that embeds the target language into the top encoder layers to mitigate confusion in the encoder and ensures stable language indication for the decoder.
Outcome:	The proposed language converter strategy significantly mitigates off-target issue on multiUN, TED, and OPUS-100 datasets.

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Qin Liu, Wenxuan Zhou, Nan Xu, James Y. Huang, Fei Wang, Sheng Zhang, Hoifung Poon, Muhao Chen

PLACES: Prompting Language Models for Social Conversation Synthesis (2023.findings-eacl)

Challenge:	Existing approaches impose fixed cognitive structures that enhance performance in specific tasks but lack adaptability across diverse scenarios.
Approach:	They propose a test-time scaling framework based on meta-thoughts to improve performance . meta-thinkts are adaptive thinking strategies tailored to a given task .
Outcome:	Experimental results show that MetaScale outperforms standard inference approaches . it can scale more effectively with increasing sampling budgets and produces more structured responses .

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Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu, Dilek Hakkani-Tur

ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding (2026.acl-long)

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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Yuhang Li, Chenchen Zhang, Ruilin Lv, Ao Liu, Ken Deng, Yuanxing Zhang, Jiaheng Liu, Bo Zhou

Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)

Challenge:	Large Language Models (LLMs) excel at algorithmic code generation, but front-end development is lacking in visual fidelity and interaction.
Approach:	They propose an agentic, vision-grounded reinforcement learning framework that closes a loop by invoking a multimodal LLM as a tool.
Outcome:	The proposed framework outperforms baselines in front-end code generation.

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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

VENUS: A VLLM-driven Video Content Discovery System for Real Application Scenarios (2025.emnlp-industry)

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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Minyi Zhao, Yi Liu, Jianfeng Wen, Boshen Zhang, Hailang Chang, Zhiheng Ouyang, Jie Wang, Wensong He, Shuigeng Zhou

The Role of Visual Modality in Multimodal Mathematical Reasoning: Challenges and Insights (2025.acl-long)

Challenge:	Video Content Discovery (VCD) is to identify specific videos defined by a pre-specified text policy.
Approach:	They propose a Vision-Language Large Model-driven video content discovery system called VENUS to solve these problems.
Outcome:	The proposed system generates high-quality, VCD-specific data for model training and extends it to support it better.

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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

DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding (2026.findings-acl)

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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Jiaming Zhou, Xuxin Cheng, Shiwan Zhao, Yuhang Jia, Cao Liu, Ke Zeng, Xunliang Cai, Yong Qin

Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning (2026.acl-long)

Challenge:	Autoregressive (AR) large audio language models are expensive in data and computation . prior work shows diffusion-based LALMs can improve audio understanding under matched settings .
Approach:	They propose a diffusion-based LALM that upgrades the speech encoder and employs dual semantic and acoustic adapters.
Outcome:	a new model improves over existing autoregressive large language models and is competitive to strong AR models . the proposed model can make use of limited training data and improve inference efficiency . a recent study shows that diffusion-based models can improve audio understanding .

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Yifan Li, YuKai Gu, Yingqian Min, Zikang Liu, Yifan Du, Kun Zhou, Min Yang, Xin Zhao, Minghui Qiu

Multi-Level Cross-Modal Alignment for Speech Relation Extraction (2024.emnlp-main)

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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Liang Zhang, Zhen Yang, Biao Fu, Ziyao Lu, Liangying Shao, Shiyu Liu, Fandong Meng, Jie Zhou, Xiaoli Wang, Jinsong Su

BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data (2025.acl-long)

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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Wenkai Li, Jiarui Liu, Andy Liu, Xuhui Zhou, Mona T. Diab, Maarten Sap

CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs (2026.findings-acl)

Challenge:	Existing methods for embedding human personality traits into LLMs are limited by realism and validity issues.
Approach:	They propose to use a large-scale dataset to embed human personality traits into LLMs . they use supervised fine-tuning and direct preference optimization to train LLM models .
Outcome:	The proposed methods outperform prompting on personality assessments and IPIP-NEO, and show higher conscientiousness, agreeableness, lower extraversion, and lower neuroticism on reasoning tasks.

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Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu

MuTual: A Dataset for Multi-Turn Dialogue Reasoning (2020.acl-main)

Challenge:	Existing approaches to constraint-aware planning fail to enhance the model’s intrinsic focus on constraints.
Approach:	They propose a constraint-aware reinforcement learning framework that encourages constraint focus and penalizes neglect of LLMs.
Outcome:	The proposed framework outperforms existing frameworks and state-of-the-art reasoning models in a number of real-world applications.

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Leyang Cui, Yu Wu, Shujie Liu, Yue Zhang, Ming Zhou

K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)

Challenge:	Existing non-task oriented dialogue systems can yield a relevant and fluent response, but sometimes make logical mistakes because of weak reasoning capabilities.
Approach:	They propose a dataset for multi-turn dialogue reasoning that uses annotated dialogues to train a machine to handle various reasoning problems.
Outcome:	Empirical results show that state-of-the-art methods only reach 71%, far behind human performance of 94%.

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Song Xu, Haoran Li, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Ying Liu, Bowen Zhou

Generating Temporally-ordered Event Sequences via Event Optimal Transport (2022.coling-1)

Challenge:	Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios.
Approach:	They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks.
Outcome:	The proposed model significantly outperforms baselines across the board in e-commerce scenarios.

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Bo Zhou, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, Xiaojian Jiang, Qiuxia Li

Challenge:	Existing methods for temporal event ordering and event infilling ignore the global semantics of events, and the model adopts a word-level objective to model events in texts.
Approach:	They propose a temporal event ordering and event infilling task using a model that uses maximum likelihood estimation to model events in texts.
Outcome:	The proposed model outperforms existing models on all evaluation datasets.

Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)

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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

BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models (2020.findings-emnlp)

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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Bin He, Di Zhou, Jinghui Xiao, Xin Jiang, Qun Liu, Nicholas Jing Yuan, Tong Xu

MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation (2026.acl-long)

Challenge:	Existing knowledge representation learning methods do not use graph contextualized knowledge.
Approach:	They propose to model subgraphs in a medical KG and integrate it with a pre-trained language model to do knowledge generalization.
Outcome:	The proposed model achieves state-of-the-art on several medical NLP tasks . it improves on MedERNIE, and the proposed model is effective .

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Dawei Wang, Di Zhao, Xinyuan Liu, Marci Chi Ma, Xiaoyang Liu, Chengming Zhou, Gary Ushaw, Richard Davison

PEPE: Long-context Extension for Large Language Models via Periodic Extrapolation Positional Encodings (2025.findings-emnlp)

Challenge:	Embodied AI systems are open, where agents may leave or enter mid-task due to hardware failures or task-related errors.
Approach:	They propose a framework that reformulates credit assignment as a rank aggregation problem using contribution-based pairwise comparisons among agents generated by large multimodal models.
Outcome:	The proposed framework can guide agents toward effective cooperation in complex tasks of different types.

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Jikun Hu, Dongsheng Guo, Yuli Liu, Qingyao Ai, Lixuan Wang, Xuebing Sun, Qilei Zhang, Quan Zhou, Cheng Luo

Hybrid Alignment Training for Large Language Models (2024.findings-acl)

Challenge:	Long-context extension attempts to extend contextual window in pre-trained LLMs . primary method involves expanding initial positional encodings, disrupting positional learning .
Approach:	They propose a new extension strategy based on Rotary Position Embedding to extend contextual window in pre-trained large language models.
Outcome:	The proposed method can extend the contextual window in pre-trained large language models . expansion disrupts positional encodings learned during pre-training, authors show .

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Chenglong Wang, Hang Zhou, Kaiyan Chang, Bei Li, Yongyu Mu, Tong Xiao, Tongran Liu, JingBo Zhu

Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning (2026.findings-acl)

Challenge:	Existing approaches to align large language models with instructions and preferences are conflicting . et al., 2023b) show that hybrid alignment training can outperform baselines .
Approach:	They propose a hybrid alignment training approach based on alternating alignment and modified elastic weight consolidation methods to achieve better collaboration between different alignment tasks.
Outcome:	The proposed approach outperforms baseline alignment training methods on summarization and dialogue tasks.

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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

Unsupervised Paraphrasing by Simulated Annealing (2020.acl-main)

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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Xianggen Liu, Lili Mou, Fandong Meng, Hao Zhou, Jie Zhou, Sen Song

Neural Relation Classification with Text Descriptions (C18-1)

Challenge:	Existing approaches to generate accurate and different-appearing paraphrases require massive parallel samples for training.
Approach:	They propose a novel approach that accomplishes Unsupervised Paraphrasing by Simulated Annealing by performing local editing.
Outcome:	The proposed approach outperforms existing models in automatic and human evaluations on Quora, Wikianswers, MSCOCO, and Twitter.

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Feiliang Ren, Di Zhou, Zhihui Liu, Yongcheng Li, Rongsheng Zhao, Yongkang Liu, Xiaobo Liang

RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions (2025.emnlp-main)

Challenge:	State-of-the-art methods for relation classification suffer from data sparsity issue greatly.
Approach:	They propose a new neural relation classification method which integrates entities’ text descriptions into deep neural networks models.
Outcome:	The proposed method achieves much better experimental results than other state-of-the-art methods on the SemEval 2010 dataset.

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Wanlong Liu, Junying Chen, Ke Ji, Li Zhou, Wenyu Chen, Benyou Wang

Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning (2023.findings-acl)

Challenge:	Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models by incorporating external knowledge.
Approach:	They propose a method for synthesizing diverse and high-quality RAG instruction data based on any source corpus.
Outcome:	The proposed method outperforms existing methods in multiple tasks and achieves strong zero-shot performance.

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Weize Chen, Xu Han, Yankai Lin, Zhiyuan Liu, Maosong Sun, Jie Zhou

Verb Metaphor Detection via Contextual Relation Learning (2021.acl-long)

Challenge:	Existing methods for tuning pre-trained language models ignore the running cost and only optimize the terminal cost.
Approach:	They propose to use stochastic bridges to regularize intermediate states and use regularization as running cost of PETs.
Outcome:	The proposed methods can be used to tune large pre-trained language models . they can be compared to full-parameter fine-tuning by tuning a small number of parameters .

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Wei Song, Shuhui Zhou, Ruiji Fu, Ting Liu, Lizhen Liu

FlashAudio: Rectified Flow for Fast and High-Fidelity Text-to-Audio Generation (2025.acl-long)

Challenge:	Recent work on verb metaphor detection focuses on analyzing restricted forms of linguistic context.
Approach:	They propose a model which explicitly models the relation between a verb and its various contexts.
Outcome:	The proposed model gets competitive results compared with state-of-the-art approaches on the VUA, MOH-X and TroFi datasets.

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Huadai Liu, Jialei Wang, Rongjie Huang, Yang Liu, Heng Lu, Zhou Zhao, Wei Xue

Confidence-Aware Scheduled Sampling for Neural Machine Translation (2021.findings-acl)

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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Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

MegaPairs: Massive Data Synthesis for Universal Multimodal Retrieval (2025.acl-long)

Challenge:	Neural machine translation models are trained to maximize the likelihood of the next token given previous golden tokens as inputs, but at the inference stage, golden token is unavailable.
Approach:	They propose a scheduled sampling method that randomly replaces groundtruth tokens with predicted ones during training, ignoring real-time model competence.
Outcome:	The proposed method outperforms the Transformer and vanilla scheduled sampling on large-scale translations.

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Junjie Zhou, Yongping Xiong, Zheng Liu, Ze Liu, Shitao Xiao, Yueze Wang, Bo Zhao, Chen Jason Zhang, Defu Lian

Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis (2022.naacl-main)

Challenge:	despite the growing demand for multimodal retrieval, there is a lack of training data.
Approach:	They propose a data synthesis method that leverages vision language models and open-domain images to generate high-quality data.
Outcome:	The proposed method outperforms baseline models on 70 more datasets and can scale up.

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Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi

Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models (2026.acl-long)

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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Zhiqing Cui, Binwu Wang, Qingxiang Liu, Yeqiang Wang, Zhengyang Zhou, Yuxuan Liang, Yang Wang

InterIntent: Investigating Social Intelligence of LLMs via Intention Understanding in an Interactive Game Context (2024.emnlp-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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Ziyi Liu, Abhishek Anand, Pei Zhou, Jen-tse Huang, Jieyu Zhao

Challenge:	Large language models (LLMs) have demonstrated the potential to mimic human social intelligence, but most studies focus on static self-report or performance-based tests.
Approach:	They propose a framework to assess LLMs' ability to understand and manage intentions by mapping their ability to infer the intentions of others in a game setting.
Outcome:	The proposed framework assesses LLMs' ability to understand and manage intentions in a game setting.

CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

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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 .

Inference-Time Language Model Alignment via Integrated Value Guidance (2024.findings-emnlp)

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Zhixuan Liu, Zhanhui Zhou, Yuanfu Wang, Chao Yang, Yu Qiao

Prevent the Language Model from being Overconfident in Neural Machine Translation (2021.acl-long)

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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Mengqi Miao, Fandong Meng, Yijin Liu, Xiao-Hua Zhou, Jie Zhou

Multi-Dimensional Evaluation of Text Summarization with In-Context Learning (2023.findings-acl)

Challenge:	Neural Machine Translation models are based on partial translation and a language model that predicts the next token based only on partial.
Approach:	They propose a Margin-based Token-level Objective and a Sentence-level Goal to maximize the Margin . they propose to model the next token based on partial translation .
Outcome:	The proposed approach improves translation adequacy and fluency on English-to-German, Chinese-to English and French translation tasks.

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Sameer Jain, Vaishakh Keshava, Swarnashree Mysore Sathyendra, Patrick Fernandes, Pengfei Liu, Graham Neubig, Chunting Zhou

Disentangle-based Continual Graph Representation Learning (2020.emnlp-main)

Challenge:	In-context learning-based evaluators are competitive with learned evaluation frameworks for text summarization tasks.
Approach:	They propose to use large language models as multi-dimensional evaluators using in-context learning to evaluate text summarization tasks.
Outcome:	The proposed frameworks are competitive with existing frameworks on relevance and factual consistency, the authors show .

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Xiaoyu Kou, Yankai Lin, Shaobo Liu, Peng Li, Jie Zhou, Yan Zhang

SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation (2026.findings-acl)

Challenge:	Existing graph embedding methods overlook streaming nature of incoming data in real-world applications.
Approach:	They propose a disentangle-based continual graph representation learning framework inspired by the human’s ability to learn procedural knowledge.
Outcome:	The proposed framework outperforms state-of-the-art continual graph representation learning framework and alleviate catastrophic forgetting problem.

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Chengyi Yang, Pengzhen Li, Jiayin Qi, Aimin Zhou, Ji Wu, Ji Liu

VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval (2024.acl-long)

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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Junjie Zhou, Zheng Liu, Shitao Xiao, Bo Zhao, Yongping Xiong

Fusing Highly Specialized Language Models for Comprehensive Expertise (2025.acl-long)

Challenge:	Existing methods for multimodal retrieval are mostly text-oriented, which lack the capability to process visual information.
Approach:	They propose a multi-modal multi-text embedding model VISTA which extends a powerful text encoder with the image understanding capability by introducing visual token embedds.
Outcome:	The proposed model achieves superior performance across a variety of multi-modal retrieval tasks in zero-shot and supervised settings.

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Ning Ding, Yulin Chen, Ganqu Cui, Xingtai Lv, Weilin Zhao, Kaiyan Zhang, Ruobing Xie, Bowen Zhou, Zhiyuan Liu, Maosong Sun

DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI (2024.findings-eacl)

Challenge:	Existing models that focus on language, programming code, and mathematical symbols are not able to achieve mastery of all three domains simultaneously.
Approach:	They propose to fuse highly-specialized models that are already sufficiently trained on different domains to achieve a highly-specific model.
Outcome:	The proposed model could achieve mastery of the three crucial domains simultaneously.

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Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong

Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)

Challenge:	DialogStudio is the largest and most diverse collection of dialogue datasets . existing datasets lack diversity and comprehensiveness, authors say .
Approach:	They introduce DialogStudio: the largest and most diverse collection of dialogue datasets . DialogStuio aggregates more than 80 diverse dialogue dataset .
Outcome:	a new dataset is created to improve the quality and diversity of dialogue datasets . DialogStudio is the largest and most diverse collection of dialogue data .

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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

Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen LLMs (2026.acl-long)

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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Xuancheng Li, Haitao Li, Yujia Zhou, Yiqun Liu, Qingyao Ai

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)

Challenge:	Large language models (LLMs) are largely static and often redo reasoning or repeat mistakes. Prior experience reuse relies on external retrieval, which is similarity-based, can introduce noise, and adds latency.
Approach:	They propose a lightweight plug-in that stores experience in its parameters and generates a structured, instance-tailored experience entry in a single forward pass to guide a frozen LLM executor.
Outcome:	Experiments on mathematical reasoning benchmarks show consistent accuracy gains across executors with low overhead.

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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

Cross-Lingual Dependency Parsing by POS-Guided Word Reordering (2020.findings-emnlp)

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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Lu Liu, Yi Zhou, Jianhan Xu, Xiaoqing Zheng, Kai-Wei Chang, Xuanjing Huang

HyperText: Endowing FastText with Hyperbolic Geometry (2020.findings-emnlp)

Challenge:	Existing approaches to cross-lingual dependency parsing rely on large corpus size and cost.
Approach:	They propose a cross-lingual dependency parsing approach based on word reordering . they propose to train a model that transfers knowledge learned in one or multiple languages to target languages .
Outcome:	The proposed approach outperforms the baseline approach in Hindi and Latin by 15.3% and 6.7%.

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Yudong Zhu, Di Zhou, Jinghui Xiao, Xin Jiang, Xiao Chen, Qun Liu

Emergent Modularity in Pre-trained Transformers (2023.findings-acl)

Challenge:	Empirically, we show that HyperText outperforms FastText on a range of text classification tasks with much reduced parameters.
Approach:	They propose a model that uses hyperbolic geometry to model tree-like hierarchies in natural language sentences by embedding words or ngrams in hyperbolical space.
Outcome:	Empirically, the proposed model outperforms FastText on a range of text classification tasks with much reduced parameters.

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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Chaojun Xiao, Xiaozhi Wang, Xu Han, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Jie Zhou

Learning to Translate by Translating: Stabilizing the Dual Loop via Semantic-Aware Self-Evolution (2026.findings-acl)

Challenge:	Existing studies on pre-trained Transformers show that they learn fine-grained neuron functions.
Approach:	They examine the presence of modularity in pre-trained Transformers . they focus on Mixture-of-Experts, a promising candidate for modularity .
Outcome:	The proposed structure stabilizes at the early stage, which is faster than neuron stabilization.

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Kui Liu, Mingming Yin, Zuoli Tang, Zihao Li, Chilin Fu, Xiaolu Zhang, Jun Zhou, Lixin Zou, Chenliang Li

RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection (2026.acl-long)

Challenge:	Large Language Models (LLMs) have been successful in machine translation, but lack of high-quality parallel corpora and cost constrain scalability.
Approach:	They propose an LLM-driven dual-learning framework that enables autonomous translation . they employ a robust semantic-aware reward function that balances adequacy with reconstruction fidelity .
Outcome:	The proposed model outperforms larger models on benchmarks and achieves parity with state-of-the-art supervised baselines on mainstream benchmarks.

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Ziyang Zhou, Ziqi Liu, Yan Wang, Yiming Lin, Yangbin Chen

Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning (2026.findings-acl)

Challenge:	Existing approaches to sarcastic detection use a uniform reasoning strategy . existing approaches lack a framework to deal with the diverse analytical demands of sarcasm .
Approach:	They propose a Retrieval-Augmented Multi-Agent framework for Sarcasm Detection . the framework provides transparent and interpretable reasoning traces .
Outcome:	The proposed framework outperforms existing methods on four benchmarks and outperformed the strong GPT-4o+CoC baseline.

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Ganghao Liu, Qin Zhou, Zhe Wang, Xuehan Lu, Haihua Huang, Yunfei Tong, Heng Tian

SimVBG: Simulating Individual Values by Backstory Generation (2025.emnlp-main)

Challenge:	Parameter-efficient fine-tuning (PEFT) is a low-cost alternative to full fine-timing due to the massive overhead.
Approach:	They propose a Mixture-of-Experts approach that enhances specialization while maintaining low resource overhead.
Outcome:	The proposed approach outperforms or matches state-of-the-art methods on GLUE, GSM8K, MBPP, and a text rewriting task from SmolTalk.

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Bangde Du, Ziyi Ye, Zhijing Wu, Monika A. Jankowska, Shuqi Zhu, Qingyao Ai, Yujia Zhou, Yiqun Liu

Challenge:	Large language models (LLMs) have strong human-like capabilities, but rarely simulating individualized human values.
Approach:	They propose a framework that simulates individual values based on individual backstories . they use structured data on an individual to transform their backstoried information to a backstory .
Outcome:	The proposed framework improves top-1 accuracy by more than 10% over retrieval-augmented generation methods.

RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)

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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji

Beyond Full Fine-tuning: Harnessing the Power of LoRA for Multi-Task Instruction Tuning (2024.lrec-main)

Challenge:	Existing methods for event prediction are incomplete and noisy.
Approach:	They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework .
Outcome:	The proposed framework can be applied to a wide variety of newsworthy scenarios.

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Chunlei Xin, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training (2020.coling-main)

Challenge:	Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning algorithm for large-scale language models.
Approach:	They conduct a systematic study of Low-Rank Adaptation (LoRA) on diverse tasks and rich resources with different learning capacities.
Outcome:	The proposed algorithm can achieve remarkable performance in high-resource and multi-task scenarios, even comparable to full fine-tuning.

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Yingyao Wang, Junwei Bao, Guangyi Liu, Youzheng Wu, Xiaodong He, Bowen Zhou, Tiejun Zhao

Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution (2026.findings-acl)

Challenge:	Existing few-shot relation classifiers struggle to distinguish them with few annotated instances due to high co-occurrence of some relations .
Approach:	They propose a few-shot relation classification model with two mechanisms to decouple easily-confused relations.
Outcome:	The proposed model achieves comparable and even better results to strong baselines in terms of accuracy.

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Zouying Cao, Jiaji Deng, Li Yu, Weikang Zhou, Zhaoyang Liu, Bolin Ding, Hai Zhao

S2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning (2025.acl-long)

Challenge:	Existing frameworks treat memory as a static append-only archive . Existing systems focus on passive accumulation, resulting in a 'passive accumulation' of memory.
Approach:	They propose a framework for experience-driven agent evolution that integrates procedural memory with contextual information to create a high-quality experience pool.
Outcome:	Experiments on BFCL-V3 and AppWorld show that ReMe outperforms memoryless Qwen3-8B.

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Ruotian Ma, Peisong Wang, Cheng Liu, Xingyan Liu, Jiaqi Chen, Bang Zhang, Xin Zhou, Nan Du, Jia Li

Reinforcement Learning on Pre-Training Data (2026.acl-long)

Challenge:	Existing approaches to incentivize LLMs’ deep thinking abilities require large-scale data or significant training efforts.
Approach:	They introduce an efficient framework that enhances LLM reasoning by teaching models to self-verify and self-correct during inference.
Outcome:	The proposed framework outperforms models trained on long-CoT distilled data with 3.1k initialization samples and achieves an accuracy improvement of 51.0% to 81.6%.

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models (2025.findings-acl)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu

Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking (2021.naacl-main)

Challenge:	Automated Essay Scoring (AES) systems face three major challenges: reliance on handcrafted features that limit generalizability, difficulty in capturing fine-grained traits like coherence and argumentation, and inability to handle multimodal contexts.
Approach:	They propose a multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering.
Outcome:	The proposed system can evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering.

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Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba

CiteLab: Developing and Diagnosing LLM Citation Generation Workflows via the Human-LLM Interaction (2025.acl-demo)

Challenge:	Existing models for zero-shot cross-domain dialogue state tracking require in-domain data to model a new domain.
Approach:	They propose a slot descriptions enhanced generative approach for zero-shot cross-domain DST by encoding a dialogue context and a slots with a pre-trained encoder and generating slot value in auto-regressive manner.
Outcome:	The proposed model significantly improves state-of-the-art results in zero-shot cross-domain setting.

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Jiajun Shen, Tong Zhou, Yubo Chen, Kang Liu, Jun Zhao

Challenge:	Existing frameworks for enabling Large Language Models to generate citations are lacking . however, they can still produce hallucinated responses that are non-factual or irrelevant to the input.
Approach:	They propose an open-source and modular framework for enabling LLMs to generate citations in Question-Answering tasks.
Outcome:	The proposed framework is extensible and paired with a visual interface, Citefix, facilitating case study and modification of existing citation generation methods.

Prix-LM: Pretraining for Multilingual Knowledge Base Construction (2022.acl-long)

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Wenxuan Zhou, Fangyu Liu, Ivan Vulić, Nigel Collier, Muhao Chen

Pairwise Prompt-Based Tuning with Parameter Efficient Fast Adaptation for Generalized Zero-Shot Intent Detection (2025.findings-naacl)

Challenge:	Existing methods to build and enrich multilingual knowledge bases have not been successful . knowledge expressed in different languages may be complementary and unequally distributed .
Approach:	They propose a model that integrates useful multilingual and KB-based factual knowledge into a single model.
Outcome:	The proposed model can provide richer combined knowledge than monolingual KBs.

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Xiaotong Zhang, Qianru Zhou, Han Liu, Hong Yu

KnowDR-REC: Auditing Knowledge-Conditioned Visual Grounding in Referring Expression Comprehension (2026.findings-acl)

Challenge:	Existing methods to generalize from seen intents to unseen intents are not effective . Xian et al., 2019: a novel approach to generalized zero-shot intent detection is needed .
Approach:	They propose a pairwise prompt-based tuning model with parameter efficient fast adaptation . they leverage hybrid contrastive learning in discriminant space and masked language modeling .
Outcome:	The proposed model can generalize to unseen intents with the help of seen intents . the proposed model is based on a pairwise prompt-based tuning model with fast adaptation .

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Guanghao Jin, Jingpei Wu, Tianpei Guo, Yiyi Niu, Weidong Zhou, Linyi Yang, Guoyang Liu

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models (2025.acl-long)

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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Zihan Qiu, Zeyu Huang, Bo Zheng, Kaiyue Wen, Zekun Wang, Rui Men, Ivan Titov, Dayiheng Liu, Jingren Zhou, Junyang Lin

DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)

Challenge:	Existing Mixture-of-Experts training frameworks use a micro-batch to calculate LBL . micro-batches are restricted to a single sequence, preventing expert specialization .
Approach:	They propose to use a global-batch to loosen the load balance constraint for MoEs models . they propose to synchronize fi across micro-batches and then use it to calculate the LBL .
Outcome:	The proposed global-batch LBL improves the domain specialization of experts . the micro-battery LBL is almost at the sequence level, and the router is pushed to distribute the token evenly .

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Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang

On the Emotion Understanding of Synthesized Speech (2026.acl-long)

Challenge:	Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF).
Approach:	a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance.
Outcome:	a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models .

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Yuan Ge, Haishu Zhao, AoKai Hao, Junxiang Zhang, Bei Li, Xiaoqian Liu, Chenglong Wang, Jianjin Wang, Bingsen Zhou, Bingyu Liu, JingBo Zhu, Zhengtao Yu, Tong Xiao

CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)

Challenge:	Existing models for emotion understanding do not capture fundamental features of synthesized speech.
Approach:	They evaluate emotion recognition models on synthesized speech using SER models and generative models.
Outcome:	The proposed model can't generalize to synthesized speech because of speech token prediction . generative models tend to infer emotion from textual semantics while ignoring paralinguistic cues.

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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun

Model Balancing Helps Low-data Training and Fine-tuning (2024.emnlp-main)

Challenge:	Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages .
Approach:	They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder .
Outcome:	The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities.

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Zihang Liu, Yuanzhe Hu, Tianyu Pang, Yefan Zhou, Pu Ren, Yaoqing Yang

SimpleOCR: Rendering Visual Questions to Teach MLLMs to Read (2026.findings-acl)

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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Yibo Peng, Peng Xia, Ding Zhong, Kaide Zeng, Siwei Han, Yiyang Zhou, Jiaqi Liu, Ruiyi Zhang, Huaxiu Yao

RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction (2023.findings-emnlp)

Challenge:	MLLMs lack visual grounding mechanism to read text embedded in images, or rely on parametric shortcuts . despite strong OCR capabilities, models suffer performance degradation of 12.7% in the VQ setting .
Approach:	They propose a plug-and-play training strategy that invalidates shortcuts in text prompts . they propose 'vq' setting where text queries are rendered directly onto images .
Outcome:	The proposed training strategy surpasses the base model by 5.4% and GRPO based on original images by 2.7% on four representative OOD benchmarks.

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Chengyuan Liu, Fubang Zhao, Yangyang Kang, Jingyuan Zhang, Xiang Zhou, Changlong Sun, Kun Kuang, Fei Wu

X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding (2025.findings-emnlp)

Challenge:	Named Entity Recognition (NER) and Relation Extraction (RE) models have limited success when extracting general schemas such as quadruples and quintuples.
Approach:	They propose a formal formulation that covers almost all extraction schemas and a Recursive Method with Explicit Schema Instructor for UIE.
Outcome:	The proposed method shows strong performance under full-shot and few-shot settings and achieves state-of-the-art results on the tasks of extracting complex schemas.

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Wenqi Zhou, Kai Cao, Hao Zheng, Yunze Liu, Xinyi Zheng, Miao Liu, Per Ola Kristensson, Walterio W. Mayol-Cuevas, Fan Zhang, Weizhe Lin, Junxiao Shen

ViFT: Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models (2025.findings-emnlp)

Challenge:	Existing benchmark datasets focus on short to moderately long videos, leaving a substantial gap in evaluating extensive, ultra-long egocentric video recordings.
Approach:	X-LeBench is a benchmark dataset designed to evaluate long egocentric video recordings . it uses a life-logging pipeline to produce realistic, coherent daily plans .
Outcome:	X-LeBench is a new benchmark dataset designed to evaluate long-form egocentric video understanding . the approach produces realistic, coherent daily plans aligned with real-world video data .

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Zikang Liu, Kun Zhou, Xin Zhao, Dawei Gao, Yaliang Li, Ji-Rong Wen

Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition (2022.emnlp-main)

Challenge:	Visual instruction tuning is the predominant technology in eliciting multimodal task-solving capabilities of large vision-language models.
Approach:	They propose a visual instruction-free fine-tuning framework for large vision-language models . they require only text-only instructions and image caption data during training .
Outcome:	The proposed framework is based on visual instruction tuning, but requires images as input . it can achieve state-of-the-art performance on several downstream benchmarks with less training data.

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Renjie Zhou, Zhongyi Xie, Jian Wan, Jilin Zhang, Yong Liao, Qiang Liu

Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning (2026.acl-long)

Challenge:	Named entity recognition (NER) is the recognition of entities with specific meanings in the text, mainly including person, organization, location, etc.
Approach:	They propose an edge-aware node joint update module and introduce a node-awful edge update module to explore hidden in structured information and solve the wrong dependency label information to some extent.
Outcome:	The proposed model can exploit the structured information on the dependency tree to improve the recognition of long entities.

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Hanbing Liu, Lang Cao, Yuanyi Ren, Mengyu Zhou, Haoyu Dong, Xiaojun Ma, Shi Han, Dongmei Zhang

CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)

Challenge:	Large language models (LLMs) often produce unnecessarily long explanations that reduce efficiency.
Approach:	They propose a length-aware reward that selectively penalizes insignificance tokens . they also propose 'dynamic length control' that encourages more detailed reasoning .
Outcome:	The proposed method reduces response length while maintaining correctness, the authors show . it selectively penalizes insignificance tokens while maintaining accuracy .

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Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, Jie Zhou

MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning (2026.acl-long)

Challenge:	Existing EE methods do not model event characteristics from large unsupervised data.
Approach:	They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures.
Outcome:	The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks.

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Weikang Shi, Aldrich Yu, Rongyao Fang, Houxing Ren, Ke Wang, Aojun Zhou, Changyao Tian, Xinyu Fu, Yuxuan Hu, Zimu Lu, Linjiang Huang, Si Liu, Rui Liu, Hongsheng Li

ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition (2026.findings-acl)

Challenge:	Existing approaches to visual chain-of-thought are limited by external tools or fail to generate high-fidelity diagrams.
Approach:	They propose a framework to enable large multimodal models with VCoT capabilities . they pre-train a model on a 15.2M-pair corpus and teach it how to leverage visual aids .
Outcome:	The proposed framework unlocks complex, human-like visual reasoning in large language models . it pre-trains the model on a 15.2M-pair corpus and fine-tunes it on MathCanvas-Instruct .

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Yujie Liu, Zonglin Yang, Tong Xie, Jinjie Ni, Ben Gao, Yuqiang Li, Shixiang Tang, Wanli Ouyang, Erik Cambria, Dongzhan Zhou

Code Generation From Flowcharts with Texts: A Benchmark Dataset and An Approach (2022.findings-emnlp)

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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Zejie Liu, Xiaoyu Hu, Deyu Zhou, Lin Li, Xu Zhang, Yanzheng Xiang

Aligning Retrieval with Reader Needs: Reader-Centered Passage Selection for Open-Domain Question Answering (2025.coling-main)

Challenge:	Currently, researchers focus on generating codes from requirement documents.
Approach:	They propose to generate source code from flowcharts with texts instead of directly translating requirements into codes.
Outcome:	The proposed model improves on the baselines by transforming flowcharts into pseudo-code . the proposed model is based on 320 flowchartes with their corresponding source codes .

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Chunlei Xin, Shuheng Zhou, Xuanang Chen, Yaojie Lu, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach (2022.coling-1)

Challenge:	Existing retrieval methods aim to gather relevant passages but fail to prioritize consistent and useful information for the reader.
Approach:	They propose a novel method which re-ranks passages based on the reader's prediction probability distribution and clusters passage according to the predicted answers.
Outcome:	The proposed method improves the quality of evidence passages under zero-shot scenarios.

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Bo Zhou, Chenhao Wang, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, Xiaojian Jiang, Qiuxia Li

MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)

Challenge:	Existing methods to predict event sequences are complex and ignore the knowledge of external events.
Approach:	They propose a statistical induction problem to generate a sequence of events by exploring the similarity between the given goal and known sequences of events.
Outcome:	The proposed model outperforms existing methods on an event sequence prediction task.

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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

Beyond Static Persona Consistency: Dynamic Persona Coherence in LLM Role-Playing (2026.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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Yirui QI, Xiaoming Zhang, Ruilin Zeng, Mengyao Liu, Ziyi Zhou, Dezhuang Miao, Bingyu Yan, Zhenyu Guan

Challenge:	Existing LLMs conflate identity consistency with emotional rigidity . Existing models exhibit either robotic repetition or persona drift .
Approach:	They propose a framework that decouples Identity-Layer Stability from Adaptive-Layer Appropriateness to achieve persona coherence repair.
Outcome:	Experiments on GPT-4o, Claude-3.5-Sonnet, and DeepSeek-V3.2 show consistent improvements (+16–84% gains)

Regularized Attentive Capsule Network for Overlapped Relation Extraction (2020.coling-main)

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Tianyi Liu, Xiangyu Lin, Weijia Jia, Mingliang Zhou, Wei Zhao

Selective Knowledge Distillation: Fusing LLM Semantic Strengths with DNN Efficiency for Binary Code Similarity Detection (2026.acl-long)

Challenge:	Existing methods to extract relations from distant supervision contain low-quality instances with noisy words and overlapped relations.
Approach:	They propose a Regularized Attentive Capsule Network to better identify overlapped relations in informal sentences . they embed multi-head attention into the capsule network as the low-level capsules .
Outcome:	Extensive experiments show that the proposed model improves relation extraction.

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Shize Zhou, Peiyu Liu, Lirong Fu, Tong Ye, Wenhai Wang

AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis (2023.findings-acl)

Challenge:	BinSKD is a binary code similarity detection technique that can be used in bug detection, patch analysis, and malware detection.
Approach:	They propose to leverage an LLM-based BCSD method as the teacher model and transfer its knowledge of high-level program semantics to various DNN-based student models.
Outcome:	The proposed method yields Recall@1 improvements of 14.5%–91.2% for DNN-based BCSD methods and enables HermesSim to match the teacher’s performance with orders-of-magnitude efficiency.

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Ru Zhou, Wenya Guo, Xumeng Liu, Shenglong Yu, Ying Zhang, Xiaojie Yuan

Outdated Issue Aware Decoding for Factual Knowledge Editing (2024.findings-acl)

Challenge:	Existing methods to extract aspects from text-image pairs and recognize their sentiments are noisy and coarsely establishing image-aspect alignment will interfere with aspect-relevant semantic and sentiment information.
Approach:	They propose an Aspect-oriented method to detect aspect-relevant semantic and sentiment information by selecting textual tokens and image blocks that are semantically related to the aspects.
Outcome:	The proposed method is superior to existing methods in the field of sentiment analysis.

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Zengkui Sun, Yijin Liu, Jiaan Wang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou

Hierarchy-Aware Global Model for Hierarchical Text Classification (2020.acl-main)

Challenge:	Existing knowledge editing methods retain outdated responses for reasoning questions . naively retraining LLMs can be computationally intensive and can lead to catastrophic forgetting .
Approach:	They propose a simple yet effective decoding strategy to enhance edited models on reasoning questions.
Outcome:	The proposed method outDates ISsue aware deCOding (DISCO) to improve models on reasoning questions.

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Jie Zhou, Chunping Ma, Dingkun Long, Guangwei Xu, Ning Ding, Haoyu Zhang, Pengjun Xie, Gongshen Liu

Dense Hierarchical Retrieval for Open-domain Question Answering (2021.findings-emnlp)

Challenge:	Existing methods for hierarchical text classification are limited and lack holistic structural information.
Approach:	They propose a hierarchy-aware global model with two variants that learn hierarchy-based label embeddings through an encoder and conduct inductive fusion of label-alike text features.
Outcome:	The proposed model improves on three benchmark datasets.

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Ye Liu, Kazuma Hashimoto, Yingbo Zhou, Semih Yavuz, Caiming Xiong, Philip Yu

Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing (2025.findings-acl)

Challenge:	Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA) current dense retrievers require splitting documents into short passages that usually contain local, partial and sometimes biased context, and may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result.
Approach:	They propose a hierarchical framework which can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic specific to each passage.
Outcome:	The proposed framework significantly outperforms the original dense passage retriever and helps an end-to-end QA system outperfect the strong baselines on multiple open-domain QA benchmarks.

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Yifan Lu, Jing Li, Yigeng Zhou, Yihui Zhang, Wenya Wang, Xiucheng Li, Meishan Zhang, Fangming Liu, Jun Yu, Min Zhang

Challenge:	Existing knowledge editing methods for large language models (LLMs) suffer from over-editing, where detoxified models reject legitimate queries, compromising overall performance.
Approach:	They propose a toxicity-aware knowledge editing approach that dynamically detects toxic activation patterns during forward propagation and then routes computations through adaptive inter-layer pathways to mitigate toxicity effectively.
Outcome:	The proposed method outperforms existing methods on large language models and enhances the SafeEdit benchmark.

Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)

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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

AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment (2023.findings-acl)

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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Ruiqi Li, Rongjie Huang, Lichao Zhang, Jinglin Liu, Zhou Zhao

XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)

Challenge:	Existing approaches to speech-to-singing voice conversion are difficult to learn in text-free situations.
Approach:	They propose an STS model which views speech variance as different modalities . it uses a novel rhythm adaptor to predict the target rhythm representation . they also use the predicted rhythm representation to re-align the content .
Outcome:	The proposed model achieves superior performance in terms of objective and subjective metrics.

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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

MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices (2020.acl-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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Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, Denny Zhou

ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)

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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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang

Towards Multiple References Era – Addressing Data Leakage and Limited Reference Diversity in Machine Translation Evaluation (2024.findings-acl)

Challenge:	Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE).
Approach:	They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE.
Outcome:	The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency.

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Xianfeng Zeng, Yijin Liu, Fandong Meng, Jie Zhou

Sailor: Open Language Models for South-East Asia (2024.emnlp-demo)

Challenge:	Recent research shows a weak correlation between n-gram-based metrics and human evaluations in machine translation tasks.
Approach:	They propose to use multiple references generated by LLMs to improve alignment between automatic metrics and human evaluations.
Outcome:	The proposed approach improves the alignment between automatic metrics and human evaluations on the WMT22 benchmark with 4 languages and achieves a maximum accuracy gain of 9.5%.

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Longxu Dou, Qian Liu, Guangtao Zeng, Jia Guo, Jiahui Zhou, Xin Mao, Ziqi Jin, Wei Lu, Min Lin

Worldwide LiveVQA: Real-Time Visual Knowledge Seeking and Updating Across Languages (2026.findings-acl)

Challenge:	Large language models (LLMs) rely on English data for training, but are often not comparable across other languages.
Approach:	They propose to develop a family of open language models for SEA languages . they use BPE dropout, aggressive data cleaning and deduplication to improve model robustness .
Outcome:	The proposed models perform well across four benchmarks, including commonsense reasoning, question answering, reading comprehension and examination.

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Xuanao Huang, Xingjia Liu, Zetong Zhou, Yuyang Peng, Yao Wan, Dongping Chen

BMInf: An Efficient Toolkit for Big Model Inference and Tuning (2022.acl-demo)

Challenge:	Existing resources for visual knowledge remain confined to English, creating a "Worldwide Knowledge Gap" eric liu: production and dissemination of knowledge exhibit a distinct trend toward decentralization and linguistic fragmentation.
Approach:	They propose a dataset for multilingual visual knowledge seeking and updating across ten major languages.
Outcome:	The proposed dataset is the first dynamic-updating dataset for multilingual visual knowledge seeking and updating across ten major languages.

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Xu Han, Guoyang Zeng, Weilin Zhao, Zhiyuan Liu, Zhengyan Zhang, Jie Zhou, Jun Zhang, Jia Chao, Maosong Sun

T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)

Challenge:	Recent years, pre-trained language models (PLMs) have achieved promising results on various NLP tasks.
Approach:	They propose an open-source toolkit for big model inference and tuning which can support big model tuning at extremely low computation cost.
Outcome:	The proposed toolkit can support big model inference and tuning at extremely low computation cost.

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Zehan Wang, Ke Lei, Chen Zhu, Jiawei Huang, Sashuai Zhou, Luping Liu, Xize Cheng, Shengpeng Ji, Zhenhui Ye, Tao Jin, Zhou Zhao

Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization (2024.findings-acl)

Challenge:	Text-to-audio (T2A) models still struggle to satisfy human preferences for prompt-following and acoustic quality when generating complex multi-event audio.
Approach:	They propose to use AI feedback learning to enhance basic capabilities of text-to-audio models . they use a large audio preference dataset to evaluate the model's capabilities .
Outcome:	The proposed model improves in simple and complex scenarios with AI feedback learning.

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Zhanhui Zhou, Jie Liu, Jing Shao, Xiangyu Yue, Chao Yang, Wanli Ouyang, Yu Qiao

Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models (2025.findings-acl)

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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Liyang He, Chenglong Liu, Rui Li, Zhenya Huang, Shulan Ruan, Jun Zhou, Enhong Chen

MedWriter: Knowledge-Aware Medical Text Generation (2020.coling-main)

Challenge:	Sentence embedding is essential for many NLP tasks, but reliance on manual labels limits scalability.
Approach:	They propose a method for controlling the generation direction of large language models in the latent space by integrating ranking information and semantic information.
Outcome:	The proposed method achieves new SOTA performance with a modest cost in ranking sentence synthesis.

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Youcheng Pan, Qingcai Chen, Weihua Peng, Xiaolong Wang, Baotian Hu, Xin Liu, Junying Chen, Wenxiu Zhou

Advancement in Graph Understanding: A Multimodal Benchmark and Fine-Tuning of Vision-Language Models (2024.acl-long)

Challenge:	Recent studies focus on the information of unstructured text rather than structured information of the knowledge graph.
Approach:	They propose a knowledge-aware text generation model for medical domains that incorporates knowledge graphs into the model to improve the quality of generated text.
Outcome:	The proposed model improves the quality of generated text and has robust superiority over other methods.

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Qihang Ai, Jiafan Li, Jincheng Dai, Jianwu Zhou, Lemao Liu, Haiyun Jiang, Shuming Shi

CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue (2022.coling-1)

Challenge:	Graph data organizes complex relationships and interactions between objects . Graph neural networks (GNNs) are becoming more popular in graph learning .
Approach:	They propose a new paradigm for interactive and instructional graph data understanding and reasoning . they first evaluate the capabilities of public VLMs in graph learning from multiple aspects .
Outcome:	The proposed model achieves an accuracy increase of 5%-15% compared to baseline models . the best-performing model achieve scores comparable to Gemini in GPT-asissted Evaluation .

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Libo Qin, Qiguang Chen, Tianbao Xie, Qian Liu, Shijue Huang, Wanxiang Che, Zhou Yu

SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation (2026.findings-acl)

Challenge:	Consistency identification in task-oriented dialog usually consists of three subtasks . a proposed model for consistency identification in dialog is based on an explicit interaction paradigm .
Approach:	They propose a cycle guided interactive learning model that makes information exchange explicit from all the three tasks.
Outcome:	The proposed model achieves state-of-the-art performance pushing the overall score to 56.3% (5.0% point absolute improvement)

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Shuang Cheng, Yihan Bian, Dawei Liu, Yuhua Jiang, Yihao Liu, Linfeng Zhang, Qian Yao, Zhongbo Tian, Wenhai Wang, Qipeng Guo, Kai Chen, Biqing Qi, Bowen Zhou

StableToolBench-MirrorAPI: Modeling Tool Environments as Mirrors of 7,000+ Real-World APIs (2025.findings-acl)

Challenge:	Autoregressive (AR) language models are a dominant paradigm in the field of parallelism and non-causal modeling.
Approach:	They propose a blockwise discrete diffusion model that preserves AR-compatible serving while enabling parallel intra-block generation.
Outcome:	The proposed model achieves theoretical speedups over 5 and wall-clock speedup of 2.3 on H200 GPUs in latency-critical regimes.

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Zhicheng Guo, Sijie Cheng, Yuchen Niu, Hao Wang, Sicheng Zhou, Wenbing Huang, Yang Liu

APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention (2026.acl-long)

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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Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Ao Sun, Ziqi Yuan, Hao Zhou, Fandong Meng, Zhiyuan Liu

FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (D19-1)

Challenge:	Existing methods for long-video inference use compression or sparse attention . existing methods restrict LMMs from handling longer, more complex videos .
Approach:	They propose a sequence-parallel framework with optimized attention that accelerates long-video inference across multiple GPUs.
Outcome:	The proposed framework delivers speedups of 12.72x, 1.70x, and 1.18x over FlashAttn, ZigZagRing, and APB without significant performance loss.

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Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training (2022.emnlp-main)

Challenge:	Few-shot domain adaptation and NOTA detection are two real-world challenges for few-shot relation classification models.
Approach:	They propose a task to investigate two aspects of few-shot relation classification models . they build upon the FewRel dataset by adding a new test set in a different domain .
Outcome:	The proposed task can evaluate few-shot domain adaptation and few- shot none-of-the-above detection on a new domain and NOTA relation choice.

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Ziqiang Zhang, Long Zhou, Junyi Ao, Shujie Liu, Lirong Dai, Jinyu Li, Furu Wei

Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs (2026.acl-long)

Challenge:	Existing models for pre-training text and speech are based on unlabeled audio data.
Approach:	They propose a unified-modal speech-unit-text pre-training model that connects speech encoders and text decoders with a shared unit encoder.
Outcome:	The proposed model improves on automatic speech recognition and speech translation tasks and achieves state-of-the-art performance on both the LibriSpeech ASR and MuST-C ST tasks.

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Tingchao Fu, Wenkai Wang, Fanxiao Li, Huadong Zhang, Jinhong Zhang, Dayang Li, Yunyun Dong, Renyang Liu, Wei Zhou

A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)

Challenge:	Existing knowledge editing paradigms suffer from editing decoupling failures . entity knowledge is sequestered into disentangled modality-specific pathways .
Approach:	They propose a method that explicitly disentangles and localizes modality-specific neuron groups for targeted knowledge.
Outcome:	The proposed method outperforms baselines in reliability and consistency while preserving model locality.

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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

Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models (2024.acl-long)

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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Kun Luo, Zheng Liu, Shitao Xiao, Tong Zhou, Yubo Chen, Jun Zhao, Kang Liu

Bridging Semantic and Modality Gaps in Zero-Shot Captioning via Retrieval from Synthetic Data (2025.findings-emnlp)

Challenge:	Existing methods for retrieval augmentation work with chunked contexts, which leads to poor quality of semantic representation and incomplete retrieval of useful information.
Approach:	They propose a method for retrieval augmentation of long-context language modeling using landmark embedding.
Outcome:	The proposed method outperforms existing retrieval methods with a notable advantage.

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Zhiyue Liu, Wenkai Zhou

Diffusion Glancing Transformer for Parallel Sequence-to-Sequence Learning (2024.naacl-long)

Challenge:	Existing methods for zero-shot image captioning fail to bridge the semantic gap between images and textual inputs.
Approach:	They propose a retrieval-based framework that leverages only existing synthetic image-text pairs as its search corpus to bridge the gap when using synthetic data for captioning.
Outcome:	The proposed method bridges the semantic gap between a synthetic image and its input text . it extracts image-related textual descriptions to mitigate the modality gap during decoding .

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Lihua Qian, Mingxuan Wang, Yang Liu, Hao Zhou

Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages (2022.acl-long)

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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Xu Han, Yuqi Luo, Weize Chen, Zhiyuan Liu, Maosong Sun, Zhou Botong, Hao Fei, Suncong Zheng

Diversity-oriented Data Augmentation with Large Language Models (2025.acl-long)

Challenge:	Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages.
Approach:	They propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages.
Outcome:	The proposed framework can learn effective FGET models for low-resource languages even without human-labeled data.

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Zaitian Wang, Jinghan Zhang, Xinhao Zhang, Kunpeng Liu, Pengfei Wang, Yuanchun Zhou

From Script to Stage: Automating Experimental Design for Social Simulations with LLMs (2026.findings-acl)

Challenge:	Existing data augmentation methods focus on increasing sample numbers while neglecting sample distribution diversity, which can lead to model overfitting.
Approach:	They propose a data augmentation framework that focuses on sample distribution diversity and trains a large language model as a diverse paraphraser.
Outcome:	The proposed framework achieves an average performance gain of 10.52% surpassing the runner-up baseline with more than three percentage points.

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Yuwei Guo, Zihan Zhao, Xiaowei Liu, Xiangning Yu, Qun Ma, Deyu Zhou, Xiao Xue

Challenge:	Xu et al., 2024): multi-agent simulations based on large language models are a new paradigm for social science research . traditional experimental design relies on interdisciplinary expertise and technical barriers . Xiaoping and Xin eli argue that LLM-driven agents are unreliable for rigorous experimental design due to hallucinations and limited verifiability.
Approach:	They propose a framework for multi-agent experiment design based on script generation . Script Composition, Script Finalization, and Actor Generation are the core phases of the framework .
Outcome:	The proposed framework lowers the barrier for social science experimental design and provides scientifically grounded decision support for policy-making.

P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs (2025.emnlp-main)

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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

Detecting Adversarial Samples through Sharpness of Loss Landscape (2023.findings-acl)

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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Rui Zheng, Shihan Dou, Yuhao Zhou, Qin Liu, Tao Gui, Qi Zhang, Zhongyu Wei, Xuanjing Huang, Menghan Zhang

WorkTeam: Constructing Workflows from Natural Language with Multi-Agents (2025.naacl-industry)

Challenge:	Existing studies have shown that adversarial samples are more vulnerable than normal ones to textual adversarials.
Approach:	They propose a simple and effective sharpness-based detector that can distinguish adversarial samples by maximizing the loss increment within the region where the inference sample is located.
Outcome:	The proposed method outperforms previous detection methods by large margins on three text classification tasks.

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Hanchao Liu, Rongjun Li, Weimin Xiong, Ziyu Zhou, Wei Peng

FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling (2025.acl-long)

Challenge:	Existing workflow construction methods require specialized knowledge and task-switching skills.
Approach:	They propose a multi-agent workflow framework that incorporates a supervisor, orchestrator, and filler agent.
Outcome:	The proposed framework significantly increases the success rate of workflow construction . the proposed framework is based on a dataset of 3,695 real-world business samples .

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Weilin Zhao, Tengyu Pan, Xu Han, Yudi Zhang, Sun Ao, Yuxiang Huang, Kaihuo Zhang, Weilun Zhao, Yuxuan Li, Jie Zhou, Hao Zhou, Jianyong Wang, Maosong Sun, Zhiyuan Liu

Deploying Multi-task Online Server with Large Language Model (2025.coling-industry)

Challenge:	Speculative sampling is an efficient way to accelerate the auto-regressive generation process of large language models.
Approach:	They propose a frequency-ranked speculative sampling framework that optimizes draft candidate selection through vocabulary space compression.
Outcome:	Experiments show that FR-Spec reduces LM Head computation overhead by 75% while ensuring the equivalence of the final output distribution.

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Yincen Qu, Hengyue Liu, Kun Wang, Xiangying Dai, Xiaoou Lu, Hui Zhou, Chao Ma

Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models (2024.acl-long)

Challenge:	In the industry, numerous natural language processing tasks are deployed online . traditional approaches tackle each task separately by its own network and pipeline .
Approach:	They propose a three-stage multi-task learning framework for large language models . it involves task filtering, fine-tuning on high-resource tasks, and finally fine- tuning on all tasks .
Outcome:	The proposed framework reduces up to 90% of overhead while reducing latency and resource usage.

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Xudong Lu, Qi Liu, Yuhui Xu, Aojun Zhou, Siyuan Huang, Bo Zhang, Junchi Yan, Hongsheng Li

PrefRAG: Correcting Semantic Errors in Auto-Formalization for Logical Reasoning with Program Preference RAG (2026.findings-acl)

Challenge:	Mixture-of-Experts (MoE) LLMs achieve higher performance with fewer active parameters, but are still difficult to deploy due to their immense parameter sizes.
Approach:	They propose expert-level sparsification techniques to enhance the deployment efficiency of large language models by introducing plug-and-play expert pruning and skipping techniques.
Outcome:	The proposed methods reduce model sizes and increase inference speed while maintaining satisfactory performance across a wide range of tasks.

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Yuyin Zhou, Yongmei Liu

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction (2022.emnlp-main)

Challenge:	Existing auto-formalization methods for logical reasoning are prone to syntactic and semantic errors.
Approach:	They propose a neuro-symbolic paradigm for logical reasoning based on auto-formalization . they propose 'programme preference retrieval-augmented generation' to detect and repair syntactic and semantic errors.
Outcome:	The proposed approach outperforms baselines on in-distribution and out-of-difference datasets.

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Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters (2026.acl-long)

Challenge:	Existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions.
Approach:	They construct a large-scale human-annotated ERE dataset with improved annotation schemes to address these drawbacks.
Outcome:	The proposed dataset is larger than existing datasets of all the ERE tasks by at least an order of magnitude.

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Zhiyu Xu, Lean Wang, Yuanxin Liu, Lei Li, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning (2025.findings-acl)

Challenge:	Existing research lacks systematic analysis of the applicability and methodology of cross-modal skill injection.
Approach:	They investigate the applicability and methodology of cross-modal skill injection by integrating a domain-expert LLM into a VLM.
Outcome:	The proposed method enables transfer of domain-specific expertise from Large Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead.

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Ming Li, Pei Chen, Chenguang Wang, Hongyu Zhao, Yijun Liang, YuPeng Hou, Fuxiao Liu, Tianyi Zhou

Pointing to a Llama and Call it a Camel: On the Sycophancy of Multimodal Large Language Models (2025.emnlp-main)

Challenge:	Current instruction tuning relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity.
Approach:	They propose a human/model-free compositional data synthesis method that can create rich and diverse augmentations from existing instruction tuning data to enhance large language models.
Outcome:	The proposed method improves performance over benchmarks and reduces training costs by 80% compared with original instruction tuning.

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Renjie Pi, Kehao Miao, Li Peihang, Runtao Liu, Jiahui Gao, Jipeng Zhang, Xiaofang Zhou

Memorize Step by Step: Efficient Long-Context Prefilling with Incremental Memory and Decremental Chunk (2024.emnlp-main)

Challenge:	Multimodal large language models exhibit a pronounced form of visual sycophantic behavior when they process image inputs.
Approach:	They propose a technique that allows multimodal large language models to engage in reflective reasoning and determine whether a user’s instruction is misleading or corrective.
Outcome:	The proposed model resists misleading instructions but is stubborn even if it is wrong.

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Zhiyuan Zeng, Qipeng Guo, Xiaoran Liu, Zhangyue Yin, Wentao Shu, Mianqiu Huang, Bo Wang, Yunhua Zhou, Linlin Li, Qun Liu, Xipeng Qiu

Explicit Cross-lingual Pre-training for Unsupervised Machine Translation (D19-1)

Challenge:	Existing methods to optimize LLM for long sequences for long documents are slow and consume memory.
Approach:	They propose a method that starts with a small memory size and gradually increases it . they propose Decremental Chunk based on Incremental Memory (IMDC) which reduces chunk size while increasing memory size .
Outcome:	The proposed method is faster (1.45x) and reduces GPU memory consumption by 23.3% compared to fixed-size memory.

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Shuo Ren, Yu Wu, Shujie Liu, Ming Zhou, Shuai Ma

Don’t Corrupt the Fact: A Trustworthy RAG Watermarking Framework based on Dual Factual Shield (2026.acl-long)

Challenge:	Existing approaches to build initial unsupervised machine translation models with cross-lingual n-gram embeddings are inexplicit and limited.
Approach:	They propose a cross-lingual pre-training method that incorporates cross-linguistic training signals into pre-trained models by randomly choosing source n-grams in the input text stream.
Outcome:	The proposed method significantly improves the performance of unsupervised machine translation.

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Hao Huang, JiaTang Luo, Ruihua Zhou, Yunpeng Li, Yuling Liu

ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation (2026.findings-acl)

Challenge:	Existing watermarking methods are fact-agnostic and cause "faithfulness hallucinations" a novel framework to enforce knowledge loyalty is proposed to improve watermarks .
Approach:	They propose a new framework that enforces knowledge loyalty by spoofing terms from retrieved contexts and prompt-based semantic guidance to protect against factual corruption.
Outcome:	The proposed framework reduces the Knowledge Corruption Rate while maintaining its original high security and robustness.

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Ziyi Liu, Bahareh Sarrafzadeh, Pei Zhou, Longqi Yang, Jieyu Zhao, Ashish Sharma

Linguistics & Cognitive NLP

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.

MoEfication: Transformer Feed-forward Layers are Mixtures of Experts (2022.findings-acl)

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Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Structure-aware Domain Knowledge Injection for Large Language Models (2025.acl-long)

Challenge:	Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge.
Approach:	They propose to convert a model into its MoE version with the same parameters and build expert routers to decide which experts will be used for each input.
Outcome:	The proposed model can use 10% to 30% of FFN parameters while maintaining over 95% original performance.

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Kai Liu, Ze Chen, Zhihang Fu, Wei Zhang, Rongxin Jiang, Fan Zhou, Yaowu Chen, Yue Wu, Jieping Ye

Grounding Language Model with Chunking-Free In-Context Retrieval (2024.acl-long)

Challenge:	Structure-aware Continual Pre-Training (SCPT) and Structure-Aware Supervised Fine-Tuning (SSFT) are two-stage strategies for knowledge injection and alignment that reduces the training corpus needs to 5% while achieving 100% of traditional knowledge injection performance.
Approach:	They propose a method to efficiently transform foundation Large Language Models into domain specialists by using two-stage strategies: Structure-aware Continual Pre-Training and Structure-Aware Supervised Fine-Tuning.
Outcome:	The proposed method significantly reduces the training corpus needs to a mere 5% while achieving 100% of traditional knowledge injection performance.

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Hongjin Qian, Zheng Liu, Kelong Mao, Yujia Zhou, Zhicheng Dou

Entity-Aware Abstractive Multi-Document Summarization (2021.findings-acl)

Challenge:	CFIC retrieval approach eliminates the need for document chunking and provides a more efficient and efficient method for RAG systems.
Approach:	They propose a Chunking-Free In-Context retrieval approach specifically tailored for RAG systems . they employ auto-aggressive decoding to accurately identify specific evidence text .
Outcome:	The proposed method is better than traditional methods on open question answering datasets.

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Hao Zhou, Weidong Ren, Gongshen Liu, Bo Su, Wei Lu

Summarization

Challenge:	Existing models for multidocument summarization do not focus on explicitly modeling the underlying semantic information across documents.
Approach:	They propose an entityaware model for abstractive multi-document summarization that augments the classical Transformer-based encoder-decoder framework with a heterogeneous graph consisting of text units and entities as nodes.
Outcome:	The proposed model can deal with saliency and redundancy issues explicitly and can be used with pre-trained language models, arriving at improved performance.

Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation (2022.acl-long)

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Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur

OpenAttack: An Open-source Textual Adversarial Attack Toolkit (2021.acl-demo)

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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Guoyang Zeng, Fanchao Qi, Qianrui Zhou, Tingji Zhang, Zixian Ma, Bairu Hou, Yuan Zang, Zhiyuan Liu, Maosong Sun

SAMP: A Model Inference Toolkit of Post-Training Quantization for Text Processing via Self-Adaptive Mixed-Precision (2023.emnlp-industry)

Challenge:	Various attack models are distinct and implemented with different programming frameworks and settings, which hinders quick utilization and fair comparison of attack models.
Approach:	They propose an open-source textual adversarial attack toolkit to solve these issues by combining 15 typical attack models into one toolkit.
Outcome:	The proposed toolkit supports all attack types, multilinguality, and parallel processing.

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Rong Tian, Zijing Zhao, Weijie Liu, Haoyan Liu, Weiquan Mao, Zhe Zhao, Kan Zhou

Challenge:	Existing methods for quantization of models are too complicated and can cause performance damage.
Approach:	They propose a self-adaptive mixed-precision (SAMP) toolkit to automatically control quantization rate by a mixed-presence architecture to balance model accuracy and efficiency.
Outcome:	The proposed toolkit has a higher speedup than PyTorch and FasterTransformer while ensuring the required accuracy.

MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark (2024.findings-acl)

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Hongwei Liu, Zilong Zheng, Yuxuan Qiao, Haodong Duan, Zhiwei Fei, Fengzhe Zhou, Wenwei Zhang, Songyang Zhang, Dahua Lin, Kai Chen

Numerical Reasoning

Challenge:	Recent advances in large language models have showcased significant improvements in mathematics, but traditional benchmarks like GSM8k offer a unidimensional perspective.
Approach:	MathBench is a benchmark that rigorously assesses the mathematical capabilities of large language models.
Outcome:	MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills.

ACE: A LLM-based Negotiation Coaching System (2024.emnlp-main)

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Ryan Shea, Aymen Kallala, Xin Liu, Michael Morris, Zhou Yu

Challenge:	The rapid progress of LLMs has led to the development of more sophisticated AI tutoring systems.
Approach:	They develop an LLM-based assistant for coaching negotiation that provides users with targeted feedback for improvement.
Outcome:	The proposed system improves negotiation performance significantly compared to a system that doesn’t provide feedback and one which uses an alternative method.

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo

Augmenting Multi-Agent Communication with State Delta Trajectory (2025.emnlp-main)

Challenge:	Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task.
Approach:	They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5.
Outcome:	The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers.

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Yichen Tang, Weihang Su, Yujia Zhou, Yiqun Liu, Min Zhang, Shaoping Ma, Qingyao Ai

Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration (2023.emnlp-main)

Challenge:	Multi-agent systems based on large language models (LLMs) have shown to be effective in downstream tasks.
Approach:	They propose a protocol that transfers both natural language tokens and token-wise state transition trajectory from one agent to another.
Outcome:	The proposed protocol can transfer both natural language tokens and token-wise state transition trajectory from one agent to another.

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Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang

DeepRTL2: A Versatile Model for RTL-Related Tasks (2025.findings-acl)

Challenge:	Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
Approach:	They propose a framework that leverages the strength of both LLMs and domain-specific models in the context of precedents.
Outcome:	The proposed framework leverages the strength of both LLM and domain models in the context of precedents.

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Yi Liu, Hongji Zhang, Yunhao Zhou, Zhengyuan Shi, Changran Xu, Qiang Xu

A Generative Framework for Personalized Sticker Retrieval (2025.findings-emnlp)

Challenge:	Integration of large language models into electronic design automation has been a key driver in eDA.
Approach:	They propose a family of large language models that unifies generation- and embedding-based tasks related to RTL.
Outcome:	The proposed model achieves state-of-the-art performance across all evaluated tasks.

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Changjiang Zhou, Ruqing Zhang, Jiafeng Guo, Yu-An Liu, Fan Zhang, Ganyuan Luo, Xueqi Cheng

Training-Free Test-Time Contrastive Learning for Large Language Models (2026.findings-acl)

Challenge:	Existing relevance-based generative retrieval methods lack personalization, leading to a mismatch between diverse user expectations and the retrieved results.
Approach:	They propose a representation learning model that learns discriminative user representations to encode user-specific sticker preferences.
Outcome:	The proposed framework outperforms state-of-the-art methods in generating relevant stickers for queries.

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Kaiwen Zheng, Kai Zhou, Jinwu Hu, Te Gu, Mingkai Peng, Fei Liu

InteMATs: Integrating Granularity-Specific Multilingual Adapters for Cross-Lingual Transfer (2023.findings-emnlp)

Challenge:	Existing training-free alternatives to training-based models are static or depend on external guidance.
Approach:	They propose a training-free adaptation framework that enables a frozen LLM to improve online by distilling supervision from its own inference experiences.
Outcome:	The proposed framework outperforms existing test-time adaptation methods under online evaluation.

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Meizhen Liu, Xu Guo, He Jiakai, Jianye Chen, Fengyu Zhou, Siu Hui

The Lessons of Developing Process Reward Models in Mathematical Reasoning (2025.findings-acl)

Challenge:	Existing work relies on full-model fine-tuning on large parallel datasets to enhance cross-lingual alignment of MLLMs.
Approach:	They propose an approach that integrates multilingual adapters trained on texts of different levels of granularity into multilingual models.
Outcome:	The proposed approach improves the performance of multilingual language models on low-resource languages.

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Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin

CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs (2026.findings-acl)

Challenge:	a recent study shows that process reward models can make mistakes, leading to wrong conclusions.
Approach:	They propose a consensus filtering mechanism that integrates MC estimation with LLM-as-a-judge to improve model performance and data efficiency.
Outcome:	The proposed model outperforms existing open-source alternatives and provides practical guidelines for future research.

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Xingcheng Zhou, Hao Guo, Rui Song, Walter Zimmer, Mingyu Liu, André Schamschurko, Hu Cao, Alois Knoll

FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)

Challenge:	Existing Vision-language models are prone to hallucinating nonexistent entities or events and missing subtle but critical visual cues.
Approach:	They propose a Traffic VideoQA Benchmark that enforces a single structured decision pattern over each video question quadruple and provides actionable diagnostics that decompose failures into positive omission, positive swap, negative hallucination, mutual-exclusivity violation.
Outcome:	The proposed model detects true hazards when an accident occurs, and rejects plausible-but-false hypotheses under near-identical counterfactual scenes.

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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie

Challenge:	FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions .
Approach:	They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures .
Outcome:	The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes.

SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval (2022.emnlp-industry)

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Kun Zhou, Yeyun Gong, Xiao Liu, Wayne Xin Zhao, Yelong Shen, Anlei Dong, Jingwen Lu, Rangan Majumder, Ji-rong Wen, Nan Duan

FaStFact: Faster, Stronger Long-Form Factuality Evaluations in LLMs (2025.findings-emnlp)

Challenge:	Existing methods for sapping negatives from large document pool suffer from the uninformative or false negative problem.
Approach:	They propose a method to sample negatives from a large document pool using a new sampling probability distribution.
Outcome:	The proposed method can be used to sample more ambiguous negatives on four public and one industry datasets.

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Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, Zhijiang Guo

Stable Language Guidance for Vision–Language–Action Models (2026.acl-long)

Challenge:	Prior evaluation pipelines fail to evaluate factuality of long-form LLMs due to inefficiency and costly human assessment.
Approach:	They propose a fast and strong evaluation pipeline that can evaluate factuality of long-form LLMs . they propose 'faStFact' to reduce cost of web searching and inference calling .
Outcome:	The proposed evaluation pipeline achieves highest alignment with human evaluation and efficiency among existing baselines.

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Zhihao Zhan, Yuhao Chen, Jiaying Zhou, Qinhan Lyu, Hao Liu, Keze Wang, Liang Lin, Guangrun Wang

Deep Differential Amplifier for Extractive Summarization (2021.acl-long)

Challenge:	Existing vision-Language-Action models are notoriously brittle to linguistic perturbations.
Approach:	They propose a probabilistic framework that disentangles physical affordance from semantic execution.
Outcome:	The proposed framework disentangles physical affordance from semantic execution.

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Ruipeng Jia, Yanan Cao, Fang Fang, Yuchen Zhou, Zheng Fang, Yanbing Liu, Shi Wang

Unsupervised Context Rewriting for Open Domain Conversation (D19-1)

Challenge:	Existing approaches to extract summary from document with a disproportionate ratio of selected and unselected sentences are far from human performance.
Approach:	They propose a model that rebalances sentence-level extractive summarization by amplifying the semantic difference between each sentence and all other sentences and applying the residual unit as the second item of the differential amplifier to deepen the architecture.
Outcome:	The proposed model performs competitively against state-of-the-art methods on two benchmark datasets.

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Kun Zhou, Kai Zhang, Yu Wu, Shujie Liu, Jingsong Yu

TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation (2026.findings-acl)

Challenge:	Existing approaches to model conversation context have drawbacks, such as lack of coreferences and long dependency.
Approach:	They propose a context rewriting method which explicitly rewrites the last utterance by considering context history.
Outcome:	The proposed method outperforms baselines in terms of rewriting quality, multi-turn response generation, and end-to-end retrieval-based chatbots.

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Bangde Du, Minghao Guo, Songming He, Ziyi Ye, Xi Zhu, Weihang Su, Shuqi Zhu, Yujia Zhou, Yongfeng Zhang, Qingyao Ai, Yiqun Liu

Human or LLM as Standardized Patients? A Comparative Study in Medical Education (2026.acl-long)

Challenge:	Existing studies show that advanced LLMs produce text indistinguishable from human writing.
Approach:	They propose a benchmark to assess persona simulation across diverse contexts by decomposing the evaluation into six fundamental capabilities including opinion consistency, memory recall, logical reasoning, persona tone, and syntactic style.
Outcome:	The proposed model achieves moderate accuracy but falls short of the basic capabilities needed to simulate personas in real-world contexts.

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Bingquan Zhang, Xiaoxiao Liu, Yuchi Wang, Zhou Lei, Qianqian Xie, Benyou Wang

CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges (2025.acl-long)

Challenge:	Standardized patients (VSPs) are indispensable for clinical skills training but remain expensive and difficult to scale.
Approach:	They propose a multi-agent VSP framework that separates case-grounded information disclosure from response generation to support stable, inquiry-conditioned patient behavior.
Outcome:	The proposed framework more closely matches human SP behavior than existing VSPs, particularly in case consistency and controlled disclosure.

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Haitao Li, Junjie Chen, Qingyao Ai, Zhumin Chu, Yujia Zhou, Qian Dong, Yiqun Liu

Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation (2022.coling-1)

Challenge:	Empirical evaluations of large language models demonstrate that they improve performance in a wide range of tasks.
Approach:	They propose a label-free method for mitigating selection bias during inference by reformulating debiasing as an optimization task.
Outcome:	The proposed method mitigates selection bias and improves performance compared to existing methods.

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Yihe Wang, Yitong Li, Yasheng Wang, Fei Mi, Pingyi Zhou, Xin Wang, Jin Liu, Xin Jiang, Qun Liu

Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts (2026.acl-long)

Challenge:	Existing methods for generating open-domain dialogue systems underutilize training data.
Approach:	They propose a retrieval-generation training framework that takes advantage of heterogeneous training data by considering them as "evidence" they use BERTScore retrieval framework which gives better qualities of the training data, they show .
Outcome:	The proposed method performs well on zero-shot experiments and is more robust to real-world data.

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Jing Zhou, Peng Wang, Wenjun Ke, Jiajun Liu, Yao He

Jointly Learning to Repair Code and Generate Commit Message (2021.emnlp-main)

Challenge:	Existing methods for IE tasks suffer from inconsistent schema representation and implicitly intermediate reasoning . UC-UIE adopts a low-rank adapted hierarchical Mixture-of-Experts adapter for UIE tasks .
Approach:	They propose a framework that decomposes IE reasoning into three universal capabilities . UC-UIE adopts a low-rank Adaptation adapter to fine-tune LLMs for IE tasks .
Outcome:	The proposed framework outperforms full-parameter tuning methods with 1.24% trainable parameters and outperformed existing methods in generalization and interpretability.

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Jiaqi Bai, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, Zhoujun Li

TrojanSQL: SQL Injection against Natural Language Interface to Database (2023.emnlp-main)

Challenge:	Existing work performs code repair and commit message generation independently.
Approach:	They propose a cascaded method to repair program codes and generate commit messages in a unified framework.
Outcome:	The proposed model significantly outperforms baselines on a buggy-fixed-commit dataset.

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Jinchuan Zhang, Yan Zhou, Binyuan Hui, Yaxin Liu, Ziming Li, Songlin Hu

ALLSH: Active Learning Guided by Local Sensitivity and Hardness (2022.findings-naacl)

Challenge:	Existing studies on text-to-SQL systems have not investigated its security aspects . however, how to implement such attacks remains an open question.
Approach:	They propose a backdoor-based SQL injection framework for text-to-SQL systems that uses boolean-based injection and union-based injecting techniques to exploit SQL injection vulnerabilities.
Outcome:	The proposed framework can produce harmful SQL statements invalidating user queries or compromise sensitive information about the database.

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Shujian Zhang, Chengyue Gong, Xingchao Liu, Pengcheng He, Weizhu Chen, Mingyuan Zhou

AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments (2024.lrec-main)

Challenge:	Existing studies show that labeling in crowdsourcing annotations is not an annotation artifact but rather a core linguistic phenomenon.
Approach:	They propose to retrieve unlabeled data with a local sensitivity and hardness-aware acquisition function.
Outcome:	The proposed method achieves consistent gains over the commonly used active learning strategies in various classification tasks.

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Huadai Liu, Xu Wenqiang, Xuan Lin, Jingjing Huo, Hong Chen, Zhou Zhao

LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding (2025.emnlp-main)

Challenge:	Argument mining is a thriving task in natural language processing, but its generalization is limited by existing datasets.
Approach:	They propose to use a dataset to help model argument mining . the dataset AntCritic supports both argument component detection and argument relation prediction tasks.
Outcome:	The proposed model can detect arguments and identify their relationships automatically.

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Yuxuan Hu, Jihao Liu, Ke Wang, Jinliang Zheng, Weikang Shi, Manyuan Zhang, Qi Dou, Rui Liu, Aojun Zhou, Hongsheng Li

Negative-Aware Diffusion Process for Temporal Knowledge Graph Extrapolation (2026.findings-eacl)

Challenge:	Recent advances in Large Language Models have opened new avenues for solving complex optimization problems, including Neural Architecture Search (NAS).
Approach:	They propose a framework that leverages LLMs for cross-domain neural architecture optimization without extensive domain-specific tuning.
Outcome:	The proposed framework achieves competitive performance in both in-domain and out-of-domain tasks.

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Yanglei Gan, Peng He, Yuxiang Cai, Run Lin, Guanyu Zhou, Qiao Liu

You Only Query Twice: Multimodal Rumor Detection via Evidential Evaluation from Dual Perspectives (2025.coling-main)

Challenge:	Temporal Knowledge Graphs (TKGs) are dynamic structures representing entities and their evolving relationships through time.
Approach:	They propose a non-parametric model that encodes subject-centric histories into sequential embeddings.
Outcome:	The proposed model encodes subject-centric histories of entities, relations and temporal intervals into sequential embeddings.

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Junyi Chen, Leyuan Liu, Tian Lan, Fan Zhou, Xiaosong Zhang

Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoning (2026.findings-acl)

Challenge:	Existing rumor detectors exhibit limitations in fully exploiting responses to the source tweet as essential public opinions, and in explaining and indicating the reliability of the results obtained. Existing research mainly combats this with content and response-based detection methods.
Approach:	They propose a Large Language Model with both multimodal source content and the corresponding response set to extract contrasting evidence to enable maximal utilization of informative responses.
Outcome:	The proposed approach can indicate the model’s uncertainty (i.e., reliability) of the results.

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Haolin Liu, Dian Yu, Sidi Lu, Yujun Zhou, Rui Liu, Zhenwen Liang, Haitao Mi, Chen-Yu Wei, Dong Yu

DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective (2025.findings-emnlp)

Challenge:	Existing reinforcement learning methods rely on sparse outcome rewards, which fail to credit correct intermediate steps in partially successful solutions.
Approach:	They propose a process reward model that rewards correct steps only when they detect errors . they propose VPPO, which rewards the correct prefix and an erroneous suffix .
Outcome:	a new approach outperforms sparse-reward RL and prior PRM-guided baselines on Pass@1 and Pass@K . a process reward model (PRM) outperformed sparser-rebound RL on multiple reasoning benchmarks .

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Dengyun Peng, Yuhang Zhou, Qiguang Chen, JinHao Liu, Jingjing Chen, Libo Qin, Wanxiang Che

Grammar-Based Patches Generation for Automated Program Repair (2021.findings-acl)

Challenge:	Existing methods for prompt optimization still face challenges in robustness, efficiency, and generalization.
Approach:	They propose 7 new approaches inspired by traditional deep learning paradigms for prompt optimization that integrate text-based gradient optimization.
Outcome:	The proposed methods integrate deep learning paradigms into text-based gradient optimization.

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Yu Tang, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, Muyun Yang

XMC-Agent : Dynamic Navigation over Scalable Hierarchical Index for Incremental Extreme Multi-label Classification (2024.findings-acl)

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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Yanjiang Liu, Tianyun Zhong, Yaojie Lu, Hongyu Lin, Ben He, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

How Credible Is an Answer From Retrieval-Augmented LLMs? Investigation and Evaluation With Multi-Hop QA (2025.coling-main)

Challenge:	Existing methods for XMC struggle with the growing set of labels due to their static label assumptions, and embedding-based methods struggle with complex mapping relationships due to late interaction paradigm.
Approach:	They propose a large language model (LLM) powered agent framework for extreme multi-label classification, XMC-Agent, which can effectively learn, manage and predict the extremely large and dynamically increasing set of labels.
Outcome:	The proposed framework can learn, manage and predict the extremely large and dynamically growing set of labels and achieves state-of-the-art performance on three standard datasets.

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Yujia Zhou, Zheng Liu, Zhicheng Dou

Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning (2025.acl-long)

Challenge:	Retrieval-augmented large language models (RaLLMs) are reshaping knowledge acquisition, offering long-form, knowledge-grounded answers through advanced reasoning and generation capabilities.
Approach:	They propose a benchmarking system to evaluate RaLLMs' correctness and Groundedness to determine their reliability in multi-hop question-answering tasks.
Outcome:	The proposed model-based evaluation pipeline for multi-hop question-answering tasks reveals that the model generates inaccuracies when dealing with flawed or partial knowledge.

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Lei Li, Hehuan Liu, Yaxin Zhou, ZhaoYang Gui, Xudong Weng, Yi Yuan, Zheng Wei, Zang Li

Context-Sensitive Generation of Open-Domain Conversational Responses (C18-1)

Challenge:	Existing methods for enhancing the performance of large language models require expensive manual annotations.
Approach:	They propose an offline direct preference optimization method that collects preference pairs through iterative sampling and execution feedback to improve model confidence.
Outcome:	The proposed method improves performance on three reasoning tasks and shows a 3.6% improvement over the standard method.

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Weinan Zhang, Yiming Cui, Yifa Wang, Qingfu Zhu, Lingzhi Li, Lianqiang Zhou, Ting Liu

KNN-Contrastive Learning for Out-of-Domain Intent Classification (2022.acl-long)

Challenge:	Existing studies on single-turn conversation generation focus on coherence and context-sensitive generation of open-domain conversational responses.
Approach:	They propose static and dynamic attention based approaches for context-sensitive generation of open-domain conversational responses.
Outcome:	The proposed model outperforms all baselines on automatic and human evaluation on two public datasets.

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Yunhua Zhou, Peiju Liu, Xipeng Qiu

Large Language and Protein Assistant for Protein-Protein Interactions Prediction (2025.acl-long)

Challenge:	Existing methods for OOD intent classification are limited to regions with compact or simply-connected features, which assumes no OOD intentions reside.
Approach:	They propose a method that uses k-nearest neighbors to learn discriminative semantic features that are more conducive to OOD detection.
Outcome:	The proposed method improves OOD detection performance while requiring no restrictions on feature distribution.

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Peng Zhou, Pengsen Ma, Jianmin Wang, Xibao Cai, Haitao Huang, Wei Liu, Longyue Wang, Lai Hou Tim, Xiangxiang Zeng

Challenge:	Existing methods for predicting protein-protein interactions oversimplify the problem of PPI prediction in a semi-supervised manner.
Approach:	They propose a multimodal large language model that integrates proteins and PPI networks.
Outcome:	Experiments show that LLaPA can predict protein-protein interactions (mPPI) types and affinities based on sequence data.

JanusMM: A Benchmark for Self-Deprecation Understanding in Real-World Multimodal Conversations (2026.acl-long)

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Xinyi Xu, Bingguang Hao, Yongyi Xiong, Zimo Chen, Xinchen Liu, Hongxin Guo, Xuelong Wang, Silin Zhou, Shihan Dou

On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)

Challenge:	Multimodal large language models (MLLMs) are a common communicative strategy in human society, often using image-text interplay to express emotions and intentions.
Approach:	They propose to evaluate multimodal large language models (MLLMs)' understanding of self-deprecation in real-world conversations using 2,016 bilingual memes.
Outcome:	The proposed framework evaluates MLLMs' understanding of self-deprecation in real-world conversations.

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Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)

Challenge:	Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing.
Approach:	They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability.
Outcome:	The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing.

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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

Plug-and-Play Knowledge Injection for Pre-trained Language Models (2023.acl-long)

Challenge:	Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work.
Approach:	They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations.
Outcome:	The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work.

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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Huadong Wang, Deming Ye, Chaojun Xiao, Xu Han, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning (2022.findings-emnlp)

Challenge:	Existing knowledge injection methods are not suitable for enhancing pre-trained language models with external knowledge bases.
Approach:	They propose a plug-and-play knowledge injection method where knowledge bases are injected into frozen existing downstream models by a knowledge plugin.
Outcome:	The proposed method improves the performance of knowledge injection on knowledge-driven tasks while keeping model parameters frozen.

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Qingyi Si, Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou

LearnerCoMPASS: Intelligent Tutoring System with Dynamic Cognitive Diagnosis and Multi-Model Path Planning (2026.acl-long)

Challenge:	Recent studies have shown that biased samples can be brittle for VQA models . however, the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data.
Approach:	They propose a contrastive learning approach that exploits biased samples for unbiased information that contributes to reasoning.
Outcome:	The proposed method achieves competitive performance on the OOD dataset while maintaining robustness on the ID dataset.

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Ziji Sheng, Guiyao Tie, Weidong Wang, Pan Zhou, Daizong Liu

Multiple Character Embeddings for Chinese Word Segmentation (P19-2)

Challenge:	Existing adaptive learning systems struggle to achieve deep personalization, dynamic adaptability, and content trustworthiness.
Approach:	They propose a framework that integrates large language models into adaptive learning systems . they propose 'cognitive multi-model planning adapted system' to enable deep personalization .
Outcome:	The proposed framework outperforms state-of-the-art learning paths and improves trustworthiness.

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Jianing Zhou, Jingkang Wang, Gongshen Liu

Debiasing In-Context Learning by Instructing LLMs How to Follow Demonstrations (2024.findings-acl)

Challenge:	Chinese word segmentation is regarded as character-based sequence labeling task in most current work but it neglects important fact: Chinese characters contain both semantic and phonetic meanings.
Approach:	They propose a shared bi-LSTM-CRF model which fuses linguistic features efficiently by sharing the LSTM network during the training procedure.
Outcome:	The proposed model achieves state-of-the-art in AS and CityU corpora without external lexical resources.

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Lvxue Li, Jiaqi Chen, Xinyu Lu, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)

Challenge:	In-context learning (ICL) has gained considerable attention due to its data efficiency and task adaptability.
Approach:	They propose to de-biase demonstration bias in in-context learning by focusing on semantic ambiguity induced by demonstrations and reducing the semantic hazard.
Outcome:	The proposed methods significantly improve performance on six datasets.

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Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li

The Stochastic Parrot on LLM’s Shoulder: A Summative Assessment of Physical Concept Understanding (2025.naacl-long)

Challenge:	Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache.
Approach:	They propose a retrieval-based method to reduce the memory footprint of LLMs . they propose Windowed Rotary Position Embedding and query-aware vector quantization .
Outcome:	The proposed method can achieve lower performance degradation with lower overhead compared to existing methods . it can reduce the memory footprint and access overhead of long context large language models .

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Mo Yu, Lemao Liu, Junjie Wu, Tsz Ting Chung, Shunchi Zhang, Jiangnan Li, Dit-Yan Yeung, Jie Zhou

Challenge:	Recent years have witnessed remarkable advancements in large language models (LLMs) many researchers argue that LLMs may not * Equal contribution.
Approach:	They propose a task that summarises the memorization issue by using grid inputs that abstractly describe physical phenomena.
Outcome:	The proposed task alleviates the memorization issue by using grid-format inputs that abstractly describe physical phenomena.

CTTA-T: Continual Test-Time Adaptation for Text Understanding via Teacher-Student with a Domain-aware and Generalized Teacher (2026.acl-long)

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Tianlun Liu, Zhiliang Tian, Zhen Huang, Xingzhi Zhou, Wanlong Yu, Tianle Liu, Feng Liu, Dongsheng Li

TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training (2026.acl-long)

Challenge:	Existing models for text understanding fail to adapt to domain shifts in real-world applications . current models do not improve themselves as they are applied to new domains .
Approach:	They propose a continual test-time adaptation framework that adapts to evolving domains . they propose accumulating domains and a refine-then-filter framework to calibrate teacher predictions .
Outcome:	The proposed model excels in a teacher-student framework adaptable to evolving domains.

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Yifan Wang, null Binbinliu, Fengze Liu, Yuanfan Guo, Jiyao Deng, Xuecheng Wu, Weidong Zhou, Xiaohuan Zhou, Taifeng Wang

LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability (2025.findings-emnlp)

Challenge:	Static data mixing strategies in large language models are often suboptimal as they fail to adapt to the model’s evolving learning states.
Approach:	They propose a semi-dynamic data mixing framework that uses a key observation of influence ranking invariance to reduce computational overhead by 80% .
Outcome:	The proposed method reduces computational overhead by 80% and achieves an average performance gain of 2% across nine downstream benchmarks, effectively mitigating data under-digestion.

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Zikai Xiao, Fei Huang, Jianhong Tu, Jianhui Wei, Wen Ma, Yuxuan Zhou, Jian Wu, Bowen Yu, Zuozhu Liu, Junyang Lin

Challenge:	Existing benchmarks for long-form generation assess real-world queries with hard-to-verify metrics or use synthetic setups that overlook real-life intricacies.
Approach:	They propose a new approach that balances verifiable and real-world assessment with Target-Anchored Evaluation.
Outcome:	The proposed model balances real-world and verifiable assessment with Target-Anchored Evaluation (TAE) it generates queries, textual materials, and anchors based on verifier targets within real-life scenarios .

Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs (2026.findings-acl)

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Linhao Zhang, Yuhan Song, Aiwei Liu, Chuhan Wu, Sijun Zhang, Wei Jia, Yuan Liu, Houfeng Wang, Zhou Xiao

LongInsightBench: A Comprehensive Benchmark for Evaluating Omni-Modal Models on Human-Centric Long-Video Understanding. (2026.findings-acl)

Challenge:	Recent Audio Large Language Models (AudioLLMs) excel at reasoning tasks, but struggle at elementary auditory perception.
Approach:	They propose a framework that organizes audio information into three explicit components in a unified JSON format.
Outcome:	The proposed framework boosts fine-grained perception by 10.9% on MMSU over state-of-the-art models while preserving robust reasoning capabilities.

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ZhaoYang Han, Qihan Lin, Hao Liang, Bowen Chen, Zhou Liu, Wentao Zhang

A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction (2020.acl-main)

Challenge:	LongInsightBench is the first benchmark designed to assess models’ ability to understand long videos, with a focus on human language, viewpoints, actions, and other contextual elements.
Approach:	They propose a benchmark to assess models’ ability to understand long videos with a focus on human language, viewpoints, actions, and other contextual elements.
Outcome:	The proposed model excels in three key areas: a) long-duration, human-centric videos; b) diversifying and challenging task scenarios; c) quality assurance pipeline; and d) reliability.

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Shuo Ren, Shujie Liu, Ming Zhou, Shuai Ma

CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph (2024.acl-demos)

Challenge:	Recent methods for bilingual lexicon induction are based on unsupervised cross-lingual word embeddings . previous methods only use word-level information, which is limited and inaccurate .
Approach:	They propose a graph-based approach to induce bilingual lexicons in a coarse-to-fine way . they use word cliques from graphs and aligned clique-level words to find initial translation solution .
Outcome:	The proposed method improves the performance of bilingual lexicon induction compared with previous methods.

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Tong Zhou, Yubo Chen, Kang Liu, Jun Zhao

SDAR-VL: Stable and Efficient Block-wise Diffusion for Vision-Language Understanding (2026.acl-long)

Challenge:	Large language models (LLMs) are susceptible to generating hallucinated content and often encompass factually inaccurate information.
Approach:	They propose a framework that leverages knowledge graphs to address the limitations of Large Language Models (LLMs) they identify and decompose required knowledge triples that are not present in the KG, enriching them and aligning updates with real-world demands.
Outcome:	The proposed framework reduces hallucinations and increases factual accuracy in QA scenarios while retaining the same quality of knowledge.

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Shuang Cheng, Yuhua Jiang, Zineng Zhou, Dawei Liu, Tao Wang, Linfeng Zhang, Biqing Qi, Bowen Zhou

Scheduled Dialog Policy Learning: An Automatic Curriculum Learning Framework for Task-oriented Dialog System (2021.findings-acl)

Challenge:	Existing block-wise discrete diffusion models lack robust autoregressive (AR) decoders.
Approach:	They propose a block-wise discrete diffusion framework for large-scale vision-language understanding with a progressive beta noise curriculum.
Outcome:	The proposed framework improves training efficiency, convergence stability, and task performance over conventional block diffusion.

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Sihong Liu, Jinchao Zhang, Keqing He, Weiran Xu, Jie Zhou

GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)

Challenge:	et al., 2013) show that dialog policy learning is an important component of the task-oriented dialogue system.
Approach:	They propose a framework that integrates curriculum learning and policy optimization . they propose to train dialog agents from easy dialogues to complex ones .
Outcome:	The proposed framework outperforms the state-of-the-art model on multi-task dialogues.

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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan

You Impress Me: Dialogue Generation via Mutual Persona Perception (2020.acl-main)

Challenge:	Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models.
Approach:	They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks.
Outcome:	The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages.

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Qian Liu, Yihong Chen, Bei Chen, Jian-Guang Lou, Zixuan Chen, Bin Zhou, Dongmei Zhang

Towards Emotional Support Dialog Systems (2021.acl-long)

Challenge:	Existing chit-chat systems tend to generate uninformative responses and lack coherent personality traits due to the diversity of speakers.
Approach:	They propose a transmitter-receiver framework which explicitly models understanding between interlocutors.
Outcome:	The proposed framework improves on a large public dataset, Persona-Chat, with a significant boost over the state-of-the-art frameworks.

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Siyang Liu, Chujie Zheng, Orianna Demasi, Sahand Sabour, Yu Li, Zhou Yu, Yong Jiang, Minlie Huang

Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding (2021.emnlp-main)

Challenge:	Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats.
Approach:	They propose an Emotional Support Conversation task and an ESC Framework to train emotional support into dialog systems.
Outcome:	The proposed framework provides an example of an Emotional Support Conversation task and shows that it is more effective than existing models.

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Daizong Liu, Xiaoye Qu, Pan Zhou

Getting the Most out of Simile Recognition (2022.findings-emnlp)

Challenge:	Existing methods to learn effective alignment between vision and language features are insufficient in practice due to complicated multi-step reasoning.
Approach:	They propose an iterative alignment network which iterates inter- and intra-modal features within multiple steps for more accurate grounding.
Outcome:	The proposed model performs better than the state-of-the-arts on three challenging benchmarks.

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Xiaoyue Wang, Linfeng Song, Xin Liu, Chulun Zhou, Hualin Zeng, Jinsong Su

Challenge:	Recent work ignores features other than surface strings and suffers from data hunger issue.
Approach:	They propose to use simile sentence classification and simile component extraction to find simile components.
Outcome:	The proposed model outperforms current state-of-the-art systems and baselines.

AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension (2024.acl-long)

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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

Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models (2023.findings-emnlp)

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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Weize Chen, Xiaoyue Xu, Xu Han, Yankai Lin, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou

MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)

Challenge:	Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments.
Approach:	They propose a method to enhance the inference efficiency of parameter-shared PLMs by pre-training models that can achieve even greater acceleration.
Outcome:	The proposed method improves inference efficiency on autoregressive and autoencoding models.

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Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang

NumNet: Machine Reading Comprehension with Numerical Reasoning (D19-1)

Challenge:	Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences.
Approach:	They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.
Outcome:	The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner.

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Qiu Ran, Yankai Lin, Peng Li, Jie Zhou, Zhiyuan Liu

CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding (D19-1)

Challenge:	Existing numerical MRC models are weak in numerical reasoning, such as addition, subtraction, sorting and counting.
Approach:	They propose a numerical MRC model that integrates numerical reasoning into existing MRC models and achieves an EM-score of 64.56% on the DROP dataset.
Outcome:	The proposed model outperforms all existing machine reading comprehension models by considering the numerical relations among numbers on the DROP dataset.

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Yijin Liu, Fandong Meng, Jinchao Zhang, Jie Zhou, Yufeng Chen, Jinan Xu

Challenge:	Existing models for slot filling and intent detection fail to fully utilize cooccurrence relations between slots and intents, which restricts their potential performance.
Approach:	They propose a novel Collaborative Memory Network (CM-Net) that captures slot-specific and intent-specific features in a collaborative manner.
Outcome:	The proposed network outperforms existing models on two benchmarks and a self-collected corpus.

How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition (2024.acl-long)

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Guanting Dong, Hongyi Yuan, Keming Lu, Chengpeng Li, Mingfeng Xue, Dayiheng Liu, Wei Wang, Zheng Yuan, Chang Zhou, Jingren Zhou

ProReason: Multi-Modal Proactive Reasoning with Decoupled Eyesight and Wisdom (2025.emnlp-main)

Challenge:	supervised fine-tuning (SFT) is a technique used to enhance multiple abilities in large language models.
Approach:	They propose to study the interplay of data composition between mathematical reasoning, code generation, and general human-aligning abilities during supervised fine-tuning.
Outcome:	The proposed model improves math reasoning and code generation with increasing data amount . the proposed model size and SFT strategies can be used to learn multiple skills with different scaling patterns.

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Jingqi Zhou, Sheng Wang, Jingwei Dong, Kai Liu, Lei Li, Jiahui Gao, Jiyue Jiang, Lingpeng Kong, Chuan Wu

ChatASU: Evoking LLM’s Reflexion to Truly Understand Aspect Sentiment in Dialogues (2024.lrec-main)

Challenge:	Large vision-language models often prioritize language knowledge over image information on visual reasoning tasks, incurring performance degradation.
Approach:	They propose a visual reasoning framework that decouples vision-reasoning capabilities and multi-run proactive perception.
Outcome:	The proposed framework outperforms existing models on benchmarks for open-source and closed-source models with 13.2% performance gain.

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Yiding Liu, Jingjing Wang, Jiamin Luo, Tao Zeng, Guodong Zhou

SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)

Challenge:	Existing studies on interactive ASU ignore the coreference issue for opinion targets while this phenomenon is ubiquitous in interactive scenarios especially dialogues, limiting the ASU performance.
Approach:	They propose a Chat-based Aspect Sentiment Understanding task that integrates various NLP tasks with the chat paradigm and propose 'trusted self-reflexion' approach with ChatGLM as backbone to address aspect coreference issue.
Outcome:	The proposed task outperforms state-of-the-art baselines and shows that it is highly effective.

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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin

EmoBench: Evaluating the Emotional Intelligence of Large Language Models (2024.acl-long)

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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Sahand Sabour, Siyang Liu, Zheyuan Zhang, June Liu, Jinfeng Zhou, Alvionna Sunaryo, Tatia Lee, Rada Mihalcea, Minlie Huang

Causal-Debias: Unifying Debiasing in Pretrained Language Models and Fine-tuning via Causal Invariant Learning (2023.acl-long)

Challenge:	Existing benchmarks for Emotional Intelligence (EI) focus on emotion recognition, neglecting essential EI capabilities.
Approach:	They propose a benchmark that proposes a comprehensive definition for machine EI . they propose 400 hand-crafted questions in English and Chinese to evaluate EI.
Outcome:	The proposed benchmarks focus on emotion recognition, neglecting EI capabilities . they are constructed from existing datasets, which include frequent patterns and errors . the proposed benchmark includes questions in English and Chinese that require thorough reasoning and understanding .

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Fan Zhou, Yuzhou Mao, Liu Yu, Yi Yang, Ting Zhong

Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.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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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

NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit (P19-3)

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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Liqun Liu, Funan Mu, Pengyu Li, Xin Mu, Jing Tang, Xingsheng Ai, Ran Fu, Lifeng Wang, Xing Zhou

Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning (D18-1)

Challenge:	NeuralClassifier is a toolkit for hierarchical multi-label text classification.
Approach:	They propose a toolkit for neural hierarchical multi-label text classification . they use a variety of text encoders to implement the model .
Outcome:	The proposed model achieves comparable performance with reported results in the literature.

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Tianyi Liu, Xinsong Zhang, Wanhao Zhou, Weijia Jia

Mitigating Spurious Correlations via Counterfactual Contrastive Learning (2025.findings-emnlp)

Challenge:	Existing methods for extracting relations are slow and lack precision . a novel approach to extract relations is proposed to reduce noise between sentences .
Approach:	They propose a word-level distant supervised approach for relation extraction using New York Times and Freebase.
Outcome:	The proposed method improves the area of precision/call(PR) from 0.35 to 0.39 over the state-of-the-art methods.

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Fengxiang Cheng, Chuan Zhou, Xiang Li, Alina Leidinger, Haoxuan Li, Mingming Gong, Fenrong Liu, Robert Van Rooij

Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation (2021.acl-long)

Challenge:	Existing methods to distinguish causally related words from spurious correlations are limited by the number of causally correlated words in a sentence.
Approach:	They propose to use probabilistic probability of necessity and probability of sufficiency to identify causal relationships rather than spurious correlations between words and class labels.
Outcome:	The proposed method is based on a contrastive learning approach name CPNS and is validated on public datasets.

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Yuanxin Liu, Fandong Meng, Zheng Lin, Weiping Wang, Jie Zhou

Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation (P19-1)

Challenge:	Knowledge distillation (KD) has shown great success in BERT compression.
Approach:	They propose a knowledge distillation paradigm that extracts the teacher's hidden state knowledge and then compresses it into three dimensions.
Outcome:	The proposed paradigm gives rise to training speedup of 2.7x 3.4x for two kinds of student models and computing devices.

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Shuming Ma, Pengcheng Yang, Tianyu Liu, Peng Li, Jie Zhou, Xu Sun

Would LLMs be Good Historical Linguists and Chinese Dialect Learners? (2026.acl-long)

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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Yicheng Liu, Shumin Shi, Youchao Zhou, Xingchen Zhang

Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database (2022.emnlp-main)

Challenge:	Large language models struggle with low-resource Chinese dialects due to substantial phonological divergence.
Approach:	They propose to incorporate Middle Chinese, the common historical ancestor of modern Chinese dialects, into LLMs to improve dialectal pronunciation modeling.
Outcome:	The proposed approach improves on standard Chinese but struggles with low-resource Chinese dialects . the proposed model improves over baselines while revealing variation across dialects.

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Ye Liu, Semih Yavuz, Rui Meng, Dragomir Radev, Caiming Xiong, Yingbo Zhou

Temporal Evidence Chain for Temporal Knowledge Graph Question Answering with Large Language Models (2026.acl-long)

Challenge:	Existing approaches on semantic parsing suffer from exponential growth of logical form candidates and can hardly generalize to unseen data.
Approach:	They propose a unified semantic parser for question answering on KB and DB . they define the primitive as the essential element in their framework .
Outcome:	The proposed framework can predict logical forms by altering and composing top-ranked primitives with different operations.

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Shihao Liu, Xiaofei Zhou, Bo Wang, Geyuan Zhang

A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification (2022.emnlp-main)

Challenge:	Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge from Temporal knowledge graphs.
Approach:	They propose a framework to construct temporal evidence chains for LLM reasoning using Temporal Knowledge Graphs.
Outcome:	TECQA outperforms existing methods on MultiTQ and CronQuestions.

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Kaifa Zhao, Le Yu, Shiyao Zhou, Jing Li, Xiapu Luo, Yat Fei Aemon Chiu, Yutong Liu

Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences (2024.acl-long)

Challenge:	Existing datasets that ignore law requirements are limited to English.
Approach:	They construct a Chinese privacy policy dataset that can be used to analyze software privacy policies.
Outcome:	The proposed dataset includes 483 Chinese Android privacy policies, over 11K sentences, and 52K fine-grained annotations.

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Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Fuxiao Liu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang

Table-based Fact Verification with Self-adaptive Mixture of Experts (2022.findings-acl)

Challenge:	Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks, but their ability to extrapolate from image sequences has been less investigated.
Approach:	They propose a new benchmark to assess MLLMs’ sequential image reasoning abilities.
Outcome:	The proposed benchmark features 4,761 diverse image sequences with varying lengths.

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Yuxuan Zhou, Xien Liu, Kaiyin Zhou, Ji Wu

LLM Inductive Reasoning Through Multi-Agent Enhanced Monte Carlo Tree Search (2026.findings-acl)

Challenge:	Existing research focuses on table-based fact verification, but a new trend is extending the scope to structured evidence.
Approach:	They propose a mixture-of-experts neural network to recognize and execute different types of reasoning . they use a management module to decide the contribution of each expert network to the verification result .
Outcome:	The proposed method achieves 85.1% accuracy on the TabFact dataset, comparable with the previous state-of-the-art models.

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Xiang Li, Yucheng Zhou, Xiangzhi Wei, Zesheng Shi, Haiyuan Wan, Gong Yifan, Fangming Liu, Jing Li

CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)

Challenge:	Existing methods for enhancing inductive reasoning of large language models often lack explicit optimization guidance and effective error correction.
Approach:	They propose a plug-and-play test-time framework that integrates multi-agent coordination with Monte Carlo Tree Search to improve inductive reasoning.
Outcome:	The proposed framework outperforms existing methods on four benchmarks and shows consistent improvements on QWQ-32B and Deepseek-V3 .

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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou

Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models (2024.findings-acl)

Challenge:	Large pre-trained models have improved performance on a variety of natural language processing tasks.
Approach:	They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators.
Outcome:	The proposed model performs better on two NL-PL applications by fine-tuning model parameters.

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Weihang Su, Changyue Wang, Qingyao Ai, Yiran Hu, Zhijing Wu, Yujia Zhou, Yiqun Liu

Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)

Challenge:	Existing studies on hallucination detection for LLMs focus on how to identify possible factrelated errors in outputs.
Approach:	They propose an unsupervised training framework that leverages the internal states of LLMs for real-time hallucination detection without requiring manual annotations.
Outcome:	The proposed framework outperforms existing state-of-the-art methods in hallucination detection.

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Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou

DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models (2024.emnlp-main)

Challenge:	Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents.
Approach:	They propose a generative model to map natural language questions into SQL queries.
Outcome:	The proposed model significantly improves state-of-the-art execution accuracy from 69.0% to 74.4% on a large question- SQL dataset.

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Ranchi Zhao, Zhen Thai, Yifan Zhang, Shengding Hu, Jie Zhou, Yunqi Ba, Jie Cai, Zhiyuan Liu, Maosong Sun

Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) are pre-trained on vast datasets composed of billions of tokens harvested from diverse text sources.
Approach:	They propose a data engineering method to refine the pretraining corpus through data rating, tagging and editing.
Outcome:	The proposed method improves the quality of the pretraining corpus by enhancing 100 billion tokens of the training corpus.

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Yufang Liu, Tao Ji, Changzhi Sun, Yuanbin Wu, Aimin Zhou

AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications (2022.coling-1)

Challenge:	Existing studies have revealed that Large Vision-Language Models suffer from hallucinations in practice, including object hallucines, spatial hallucinos, attribute hallucinications, etc.
Approach:	They propose to use CLIP model to mitigate object hallucinations by using a data augmentation method to create negative samples with a variety of hallucinian issues.
Outcome:	The proposed method mitigates object hallucinations and can be used as a visual encoder, effectively alleviating the object halluination issue in LVLMs.

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Yusen Zhang, Zhongli Li, Qingyu Zhou, Ziyi Liu, Chao Li, Mina Ma, Yunbo Cao, Hongzhi Liu

Investigating Hallucinations in Simultaneous Machine Translation: Knowledge Distillation Solution and Components Analysis (2025.naacl-long)

Challenge:	Existing methods to correct handwritten assignments are to use OCR to recognize characters and compare them to answers.
Approach:	They propose a multimodal approach to correct handwritten Chinese characters by combining the visual information of students' handwriting with the encoded representations of answers.
Outcome:	The proposed model outperforms OCR-based methods by a large margin.

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Donglei Yu, Xiaomian Kang, Yuchen Liu, Feifei Zhai, Nanchang Cheng, Yu Zhou, Chengqing Zong

A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data (2024.lrec-main)

Challenge:	Existing methods to mitigate hallucinations in siMT generate fluency but unfaithful translation.
Approach:	They propose a method that utilizes the OMT model to mitigate hallucinations in SiMT.
Outcome:	The proposed method reduces hallucinations and improves the SiMT performance.

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Hongxiao Zhang, Mingtong Liu, Chunyou Li, Yufeng Chen, Jinan Xu, Ming Zhou

TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models (2025.acl-long)

Challenge:	Existing methods for fine-tuning domain adaptation have overfitting problem in low-resource domains . lack of parallel data makes it difficult for model to learn domain-specific knowledge .
Approach:	They propose a Reinforcement Learning Domain Adaptation method for Neural Machine Translation that uses in-domain source monolingual data to make up for the lack of parallel data.
Outcome:	The proposed method can alleviate overfitting and reinforce the model to learn domain-specific knowledge.

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Xinyi He, Yihao Liu, Mengyu Zhou, Yeye He, Haoyu Dong, Shi Han, Zejian Yuan, Dongmei Zhang

Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization (2023.emnlp-main)

Challenge:	Tabular data are crucial in many fields and their understanding by large language models (LLMs) under high parameter efficiency paradigm is important.
Approach:	They propose a module that uses 2D LoRA to encode low-rank information on cell positions to improve table serialization and representation of two-dimensional structured information within a one-dimensional sequence.
Outcome:	Experiments on four tabular-related datasets show that TableLoRA outperforms vanilla LoRA and surpasses table encoding methods tested in control.

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Tianshi Che, Ji Liu, Yang Zhou, Jiaxiang Ren, Jiwen Zhou, Victor Sheng, Huaiyu Dai, Dejing Dou

Self-Modifying State Modeling for Simultaneous Machine Translation (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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Donglei Yu, Xiaomian Kang, Yuchen Liu, Yu Zhou, Chengqing Zong

Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach (2022.findings-acl)

Challenge:	Existing methods for simultaneous machine translation fail to optimize the policy . existing methods require building a decision path to learn the policy, but they cannot explore all potential paths .
Approach:	They propose a new training paradigm that uses a read/write policy to optimize the policy . existing methods usually require building a decision path to learn a suitable policy a user makes .
Outcome:	The proposed model outperforms strong baselines and allows offline models to acquire SiMT ability with fine-tuning.

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Xin Lv, Yankai Lin, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

Understanding LLMs’ Fluid Intelligence Deficiency: An Analysis of the ARC Task (2025.naacl-long)

Challenge:	Pre-trained language models capture factual knowledge from massive texts . but they are still quite behind the SOTA KGC models in terms of performance .
Approach:	They propose to use open-world assumption to evaluate PLM-based knowledge graph completion models . they propose to convert each triple and its support information into natural prompt sentences .
Outcome:	The proposed model is more accurate under the open-world assumption (OWA) this setting manual checks the correctness of knowledge that is not in KGs.

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Junjie Wu, Mo Yu, Lemao Liu, Dit-Yan Yeung, Jie Zhou

Challenge:	Recent research on fluid intelligence assessments has highlighted significant deficiencies in LLMs’ abilities.
Approach:	They analyze the challenges LLMs face in demonstrating fluid intelligence through controlled experiments using the most representative ARC task as an example.
Outcome:	The proposed model shows that it lacks the ability to combine skill composition and abstract input formats and lacks left-to-right decoding.

Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains (2024.lrec-main)

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Vincent Segonne, Aidan Mannion, Laura Cristina Alonzo Canul, Alexandre Daniel Audibert, Xingyu Liu, Cécile Macaire, Adrien Pupier, Yongxin Zhou, Mathilde Aguiar, Felix E. Herron, Magali Norré, Massih R Amini, Pierrette Bouillon, Iris Eshkol-Taravella, Emmanuelle Esperança-Rodier, Thomas François, Lorraine Goeuriot, Jérôme Goulian, Mathieu Lafourcade, Benjamin Lecouteux, François Portet, Fabien Ringeval, Vincent Vandeghinste, Maximin Coavoux, Marco Dinarelli, Didier Schwab

WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)

Challenge:	Pretrained language models are the de facto backbone of most state-of-the-art NLP systems.
Approach:	They propose a family of domain-specific pretrained PLMs for French focusing on three important domains: transcribed speech, medicine, and law.
Outcome:	The proposed models perform better on transcribed speech, medicine, and law domains than state-of-the-art models on a diverse set of tasks and datasets.

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Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou

Synchronously Generating Two Languages with Interactive Decoding (D19-1)

Challenge:	Long-form question answering requires two procedures: information retrieval and information synthesis.
Approach:	They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time .
Outcome:	The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset .

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Yining Wang, Jiajun Zhang, Long Zhou, Yuchen Liu, Chengqing Zong

Fine-grained Artificial Neurons in Audio-transformers for Disentangling Neural Auditory Encoding (2023.findings-acl)

Challenge:	Experimental results show that multilingual NMT models handle multiple language pairs in one model.
Approach:	They propose an interactive approach to translate a source language into two different languages simultaneously and interactively.
Outcome:	The proposed approach improves on IWSLT and WMT datasets.

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Mengyue Zhou, Xu Liu, David Liu, Zihao Wu, Zhengliang Liu, Lin Zhao, Dajiang Zhu, Lei Guo, Junwei Han, Tianming Liu, Xintao Hu

Dynamic Augmentation Data Selection for Few-shot Text Classification (2022.findings-emnlp)

Challenge:	Existing studies treat each transformer encoding layer as a single artificial neuron . layer-level embeddings aggregate multiple types of contextual attention captured by multiple head modules .
Approach:	They propose to embed each transformer encoding layer as a single artificial neuron . they propose to couple those ANs with their biological-neuron counterparts in the human brain .
Outcome:	The proposed models can be used to link representations to brain activity, the authors say . their results show that the proposed models carry meaningful neurolinguistic information .

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Guangliang Liu, Lifeng Jin, Owen Yuan, Jiayu Zhou

Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering (2025.acl-short)

Challenge:	Data augmentation is a popular method for fine-tuning pre-trained language models to increase model robustness and performance.
Approach:	They propose a dynamic data selection method to select effective augmentation data from different augmentation sources according to the model’s learning stage by identifying a set of augmentation samples that optimally facilitates the learning process of the most current model.
Outcome:	The proposed method outperforms strong baselines on a variety of sentence classification tasks.

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Songtao Jiang, Chenyi Zhou, Yan Zhang, Yeying Jin, Zuozhu Liu

ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning (2026.findings-acl)

Challenge:	Current approaches generate visual markers for all questions, generating excessive visual markers.
Approach:	They propose a plug-and-play approach that adapts to the complexity of questions . they propose combining fast intuitive judgments with deliberate analytical reasoning .
Outcome:	The proposed approach improves performance on four benchmarks on ScienceQA, TextQA, VizWiz, and MME.

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Zhensheng Wang, Xiaole Liu, Wenmian Yang, Kun Zhou, Yiquan Zhang, Weijia Jia

One vs. Many QA Matching with both Word-level and Sentence-level Attention Network (C18-1)

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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Lu Wang, Shoushan Li, Changlong Sun, Luo Si, Xiaozhong Liu, Min Zhang, Guodong Zhou

Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis (2025.acl-long)

Challenge:	Existing studies on question answer matching focus on formal text . however, there exists many scenarios where the QA text is informal .
Approach:	They propose a novel QA matching approach using informal text from a product review site.
Outcome:	The proposed approach improves word-level and sentence-level attentions for solving the noisy problem in the informal text.

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Junzhuo Li, Bo Wang, Xiuze Zhou, Peijie Jiang, Jia Liu, Xuming Hu

MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning (2026.findings-acl)

Challenge:	Existing attribution methods for dense models fail to capture dynamic routing-expert interactions in sparse MoE architectures.
Approach:	They propose to analyze sparse MoE architectures against dense models to capture dynamic routing-expert interactions.
Outcome:	The proposed algorithm shows that sparse models achieve higher efficiency per layer . it also shows that deep Qwen-MoE mitigates expert failures while minimizing complexity .

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Jiahang Lin, Kai Hu, Binghai Wang, Yuhao Zhou, Zhiheng Xi, Honglin Guo, Shichun Liu, Junzhe Wang, Shihan Dou, Enyu Zhou, Hang Yan, Zhenhua Han, Tao Gui, Qi Zhang, Xuanjing Huang

ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer (2023.emnlp-main)

Challenge:	Existing work on long document visual question answering is based on Retrieval-Augmented Generation (RAG) where textual or visual content is encoded into embeddings and relevance is determined by similarity scores with respect to the original query.
Approach:	They propose a framework that employs an agentic, vision-aware workflow to address long document visual question answering through iterative information discovery and synthesis.
Outcome:	The proposed framework outperforms existing RL systems by 10.4% on the MMLongbench-Doc benchmark and demonstrates superior training performance over GRPO.

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Huadai Liu, Rongjie Huang, Xuan Lin, Wenqiang Xu, Maozong Zheng, Hong Chen, Jinzheng He, Zhou Zhao

Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction (2021.findings-acl)

Challenge:	Text-to-speech (TTS) performance has improved with the advent of denoising Diffusion Probabilistic Models . however, perceived quality of audio depends on content, pitch, rhythm, and energy .
Approach:	They propose a visual TTS model with scalable diffusion transformers that complement phoneme sequences with visual information to generate high-perceived audio.
Outcome:	The proposed model outperforms existing models regardless of visibility of the scene . it can generate high-perceived audio, opening up new avenues for AR and VR applications .

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Tianyu Gao, Xu Han, Yuzhuo Bai, Keyue Qiu, Zhiyu Xie, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network (P19-1)

Challenge:	Distantly supervised relation extraction (RE) has attracted much attention in the past few years . previous methods to evaluate models manually or directly on autolabeled data have produced inaccurate evaluations .
Approach:	They propose to use distant supervision to generate large-scale autolabeled data . they build manually-annotated test sets for two DS-RE datasets and evaluate models .
Outcome:	The proposed method produces 53% wrong labels at the entity pair level in the popular NYT10 dataset.

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Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)

Challenge:	Existing studies on aspect sentiment classification focus on non-interactive reviews . a new task aims to predict sentiment polarities for specific aspects from interactive reviews based on annotated corpus .
Approach:	They propose a task to predict aspects from interactive QA style reviews using an annotated corpus.
Outcome:	The proposed approach is compared with state-of-the-art methods against a high-quality corpus of data.

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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

Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020.findings-emnlp)

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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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou

Challenge:	Standard test sets for supervised learning evaluate in-distribution generalization but are misleading when a dataset has systematic gaps.
Approach:	They propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data.
Outcome:	The proposed model performs significantly lower on contrast sets than on the original test sets—up to 25% in some cases.

A Split-and-Recombine Approach for Follow-up Query Analysis (D19-1)

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Qian Liu, Bei Chen, Haoyan Liu, Jian-Guang Lou, Lei Fang, Bin Zhou, Dongmei Zhang

Semantic Parsing

Challenge:	Context-dependent semantic parsing has proved to be an important but challenging task.
Approach:	They propose to perform follow-up query analysis to restate context-dependent queries with contextual information.
Outcome:	The proposed approach outperforms the state-of-the-art by nearly 8% on the FollowUp dataset . the extensibility of STAR on the SQA dataset is also promising .

MarkLLM: An Open-Source Toolkit for LLM Watermarking (2024.emnlp-demo)

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Leyi Pan, Aiwei Liu, Zhiwei He, Zitian Gao, Xuandong Zhao, Yijian Lu, Binglin Zhou, Shuliang Liu, Xuming Hu, Lijie Wen, Irwin King, Philip Yu

Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) embed imperceptible yet algorithmically detectable signals in outputs to identify LLM-generated text.
Approach:	They propose to develop an open-source toolkit for LLM watermarking that embeds imperceptible yet algorithmically detectable signals in model outputs to identify LLM-generated text.
Outcome:	MarkLLM provides a unified framework for implementing LLM watermarking algorithms, while providing user-friendly interfaces to ensure ease of access.

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Han Zhou, Xingchen Wan, Yinhong Liu, Nigel Collier, Ivan Vulić, Anna Korhonen

Unveiling Opinion Evolution via Prompting and Diffusion for Short Video Fake News Detection (2024.findings-acl)

Challenge:	Large language models (LLMs) have shown promising abilities as cost-effective and reference-free evaluators for assessing language quality.
Approach:	They propose an automatic Zero-shot Evaluation-oriented Prompt Optimization framework which produces fairer preference decisions and improves human alignment.
Outcome:	The proposed framework produces fairer preference decisions and better aligns LLMs with humans.

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Linlin Zong, Jiahui Zhou, Wenmin Lin, Xinyue Liu, Xianchao Zhang, Bo Xu

Can Multimodal Large Language Models Understand Spatial Relations? (2025.acl-long)

Challenge:	Existing methods for short video fake news detection ignore the implicit opinions and evolving nature of opinions across modalities.
Approach:	They propose a short video fake news model that mines implicit opinions within short videos and promotes the evolution of both explicit and implicit opinions across all modalities.
Outcome:	The proposed model outperforms existing methods on a publicly available dataset for short video fake news detection.

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Jingping Liu, Ziyan Liu, Zhedong Cen, Yan Zhou, Yinan Zou, Weiyan Zhang, Haiyun Jiang, Tong Ruan

Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction (2022.findings-emnlp)

Challenge:	Spatial relation reasoning is a crucial task for multimodal large language models to understand the objective world.
Approach:	They propose a human-annotated spatial relation reasoning benchmark based on COCO2017 to improve MLLMs' spatial relation thinking.
Outcome:	The proposed benchmark achieves 48.14% accuracy, far below the human-level accuracy of 98.40%.

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Shirong Ma, Yinghui Li, Rongyi Sun, Qingyu Zhou, Shulin Huang, Ding Zhang, Li Yangning, Ruiyang Liu, Zhongli Li, Yunbo Cao, Haitao Zheng, Ying Shen

BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation (2022.naacl-main)

Challenge:	Chinese Grammatical Error Correction (CGEC) is a challenging NLP task and a common application in human daily life.
Approach:	They propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors.
Outcome:	The proposed method improves performance of existing CGEC models and the benchmark is excellent resource for further development.

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Yuchen Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, Ming Zhou

Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training (2022.naacl-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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Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou

Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models (2026.eacl-long)

Challenge:	Recent studies show pre-trained language models contain matching subnetworks that have similar transfer learning performance as the original PLM.
Approach:	They propose to prune matching subnetworks using magnitude-based pruning . they propose to optimize the subnetwork structure towards the pre-training objectives .
Outcome:	The proposed method is more efficient in searching subnetworks and advantageous when fine-tuning within a range of data scarcity.

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Qiao Liang, Yanjiang Liu, Weixiang Zhou, Ben He, Yaojie Lu, Hongyu Lin, Jia Zheng, Xianpei Han, Le Sun, Yingfei Sun

ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model (2025.emnlp-main)

Challenge:	Existing research treats MLLMs as unified systems optimized through end-to-end training, but the impact of vision encoder’s prior knowledge is seldom investigated.
Approach:	They propose a metric to quantify the effect of prior knowledge on MLLM performance by integrating prior knowledge at the vision encoder level into a training framework.
Outcome:	The proposed training framework incorporates prior knowledge at the vision encoder level, and significantly boosts visual understanding capabilities of MLLMs.

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Zhongyi Zhou, Yichen Zhu, Minjie Zhu, Junjie Wen, Ning Liu, Zhiyuan Xu, Weibin Meng, Yaxin Peng, Chaomin Shen, Feifei Feng, Yi Xu

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

Challenge:	Recent advances in vision-language-action models prioritize robotic action mastery . however, models trained on visual-text pairs struggle to interpret multimodal data .
Approach:	They propose a framework that integrates multimodal data after initial control mastery and a Mixture-of-Experts architecture to minimize task interference.
Outcome:	The proposed framework surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks and achieves six times higher performance on visual question-answering datasets.

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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

SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection (2026.acl-industry)

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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Zhiyong Cao, Dunqiang Liu, Qi Dai, Haojun Xu, Huai Yuen Khor, Hao Wang, Huan He, Yafei Liu, Ke Ma, Ruqian Shi, Sicheng Zhou, Sijia Yao

SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation (2021.acl-long)

Challenge:	High-quality data in training proactive dialogue agents is scarce, despite fine-tuning and reinforcement learning . a recent study has shown that the effectiveness of supervised fine-touring is limited by the lack of high-quality, domain-specific training data.
Approach:	They propose a framework for training recruitment proactive dialogue agents using a high-fidelity user simulator and a multi-dimensional evaluation framework based on Chain-of-Intention.
Outcome:	The proposed framework outperforms existing simulator-based data selection strategies in a real-world recruitment scenario.

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Shuo Ren, Long Zhou, Shujie Liu, Furu Wei, Ming Zhou, Shuai Ma

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

Challenge:	Using pre-training methods for NMT models is difficult because of the cross-attention module . cross-linguistic embeddings are not used to pretrain a decoder .
Approach:	They propose a semantic interface between pre-trained encoder and pre-train decoder to improve NMT performance.
Outcome:	The proposed method improves on translation and unsupervised translation tasks.

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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

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models (2026.findings-acl)

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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Yifu Huo, Chenglong Wang, Ziming Zhu, Shunjie Xing, Peinan Feng, Tongran Liu, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Zhengtao Yu, Tong Xiao

Impromptu Cybercrime Euphemism Detection (2025.coling-main)

Challenge:	Reinforcement learning (RL) training typically improves single-sample success rates but limited exploration of diverse reasoning trajectories.
Approach:	They propose a training paradigm that interleaves conventional RL with inverse reinforcement learning (IRL) they propose 'Steering Probability Squeezing' to enhance exploration without external supervision .
Outcome:	The proposed training paradigm improves Pass@k and improves exploration of diverse reasoning trajectories without external supervision.

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Xiang Li, Yucheng Zhou, Laiping Zhao, Jing Li, Fangming Liu

Text Classification

Challenge:	Existing methods for detecting euphemisms are ineffective in impromptu euphorism detection . Existing approaches for e-mail detection are limited to word-level ephemismals .
Approach:	They propose a framework for impromptu euphemism detection that integrates context augmentation and multi-round iterative training to better predict the actual meaning of a masked token.
Outcome:	The proposed framework improves 76-fold over the previous state-of-the-art euphemism detector.

Scheduled Sampling Based on Decoding Steps for Neural Machine Translation (2021.emnlp-main)

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Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment (2026.acl-long)

Challenge:	Neural machine translation models are trained to maximize the likelihood of next token given previous golden tokens as inputs, but at the inference stage, golden token is unavailable.
Approach:	They propose to use scheduled sampling to replace ground-truth tokens with predicted tokens to bridge the gap between training and inference.
Outcome:	The proposed methods outperform the Transformer baseline and vanilla scheduled sampling on three large-scale WMT tasks.

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Yixuan Wang, Yue Huang, Hong Qian, Yunzhao Wei, Yifei Ding, Wenkai Wang, Zhi Liu, Zhongjing Huang, Aimin Zhou, Jiajun Guo

Challenge:	Existing LLM-based tools struggle with insufficient assessment cues, weak narrative coherence, limited stylistic diversity, and poor support for creative thinking.
Approach:	They propose an evolutionary tree-based psychometric context generator that integrates rule-guided outline planning, sentence-level MCTS generation, MAP-Elites quality-diversity optimization and assessment-guide refiner simulation.
Outcome:	The proposed tool outperforms strong LLMs and structured frameworks on 7 evaluation dimensions and shows higher alignment with expert-designed contexts.

Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration (D19-1)

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Zhufeng Pan, Kun Bai, Yan Wang, Lianqiang Zhou, Xiaojiang Liu

Evolving Agentic Workflow Driven by Human-Agent Collaboration (2026.findings-acl)

Challenge:	Experimental results show that restoring incomplete utterances from context improves the performance of open-domain dialogue systems.
Approach:	They propose to use a dataset to restore incomplete utterances from context . they propose to pick and combine the data to restore the incomplete .
Outcome:	The proposed model significantly boosts response quality of open-domain dialogue systems.

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Yuxin Liu, Jinxuan Zhang, Yuezhang Peng, Hefeng Zhou, Xiangfeng Wang, Jiong Lou, Chentao Wu, Jie LI, Jingjing Qu, Chaochao Lu

Challenge:	Existing approaches to generate agentic workflows using large language models are limited by high manual design costs, inefficient agentic search, and poor dynamic adaptability to new tasks and human preferences.
Approach:	They propose an evolutionary framework for generating agentic workflows through human-agent collaboration using evolutionary algorithms that mutate and cross over their structures, prompts, and LLM backbones.
Outcome:	The proposed framework surpasses other automated baselines by 27.34% while achieving comparable performance to o1-preview at only one-fourth of the cost.

CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference (2025.acl-long)

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Jinglong Luo, Guanzhong Chen, Yehong Zhang, Shiyu Liu, Hui Wang, Yue Yu, Xun Zhou, Yuan Qi, Zenglin Xu

ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment (2025.emnlp-main)

Challenge:	Existing privacy-preserving Transformer Inference frameworks suffer from high computational overhead and performance losses.
Approach:	They propose a framework that integrates random permutations and SMPC to address the "impossible trinity" CENTAUR resists diverse data reconstruction attacks and boosts inference speed by 5.030.4 times .
Outcome:	CENTAUR achieves an unprecedented balance between privacy, efficiency, and performance.

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Zhipeng Bian, Jieming Zhu, Qijiong Liu, Wang Lin, Guohao Cai, Zhaocheng Du, Jiacheng Sun, Zhou Zhao, Zhenhua Dong

D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)

Challenge:	Large language models and diffusion models have opened new possibilities for AI-generated content . personalized cover image generation remains underexplored despite its critical role in boosting user engagement on digital platforms.
Approach:	They propose a framework that integrates MLLM-based prompting with personalized preference alignment to generate high-quality, contextually relevant covers.
Outcome:	The proposed framework improves image quality, semantic fidelity, and personalization, leading to stronger user appeal and offline recommendation accuracy in downstream tasks.

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Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang

UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs (2024.acl-demos)

Challenge:	MRC requires machines to understand text and answer questions about the text.
Approach:	They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Outcome:	The proposed system is ranked at top 1 of all participants in terms of averaged F1 score.

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Chaoqun He, Renjie Luo, Shengding Hu, Ranchi Zhao, Jie Zhou, Hanghao Wu, Jiajie Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments (2026.acl-long)

Challenge:	Existing evaluation platforms are complex and poorly modularized, hindering seamless incorporation into researcher’s workflows.
Approach:	They propose a lightweight evaluation framework characterized by lightweight, comprehensiveness, modularity, and efficiency that integrates models, data, and metrics into a unified evaluation workflow.
Outcome:	The proposed evaluation framework is lightweight, comprehensive, modular, and efficient.

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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

Fully Hyperbolic Neural Networks (2022.acl-long)

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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Weize Chen, Xu Han, Yankai Lin, Hexu Zhao, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Challenge:	Existing hyperbolic neural networks encode features in the hyperbolical space yet formalize most of their operations in the tangent space.
Approach:	They propose a fully hyperbolic framework to build hyperbolical networks based on the Lorentz model by adapting Lorentzer transformations to formalize essential operations of neural networks.
Outcome:	The proposed framework has better performance on four NLP tasks compared with existing hyperbolic models .

Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design (2026.findings-acl)

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Zhilun Zhou, Zihan Liu, Jiahe Liu, Yihan Wang, Qingyu Shao, Fengli Xu, Depeng Jin, Yong Li

Challenge:	Prior studies have focused on designing customized MAS for specific tasks . a critical research question remains: do LLM agent groups exhibit a form of "general intelligence"
Approach:	They find a Collective Intelligence factor in human groups that captures their general capability.
Outcome:	The proposed model predicts the ACI factor based on the features of LLM agent groups and can improve generalization abilities.

STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)

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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

GASim: A Graph-Accelerated Hybrid Framework for Social Simulation (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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Xuan Zhou, Yanhui Sun, Hantao Yao, Allen He, Yongdong Zhang, Wu Liu

Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules (2023.findings-emnlp)

Challenge:	Large-scale social simulators require high latency due to expensive memory retrieval and sequential ABM execution.
Approach:	They propose a graph-accelerated hybrid multi-agent framework for large-scale social simulations that uses large language models and numerical agent-based models to scale up simulations.
Outcome:	The proposed framework delivers 9.94 speedup over the traditional framework and consumes less than 20% of tokens.

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Chaojun Xiao, Yuqi Luo, Wenbin Zhang, Pengle Zhang, Xu Han, Yankai Lin, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou

Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking (2022.findings-emnlp)

Challenge:	Large language models (LLMs) have been successful on NLP tasks but require huge parameter sizes and computational resources.
Approach:	They propose a parameter-efficient acceleration method that enhances computational efficiency through plug-and-play compression plugins.
Outcome:	The proposed method saves 53% computational costs using only 0.9% additional parameters with a performance drop of less than 2%.

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Yinghui Li, Shirong Ma, Qingyu Zhou, Zhongli Li, Li Yangning, Shulin Huang, Ruiyang Liu, Chao Li, Yunbo Cao, Haitao Zheng

R-Judge: Benchmarking Safety Risk Awareness for LLM Agents (2024.findings-emnlp)

Challenge:	Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors.
Approach:	They propose a framework which renders Chinese Spell Checking model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning.
Outcome:	The proposed framework renders the CSC model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning.

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Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, Gongshen Liu

Challenge:	Large language models (LLMs) have shown compelling abilities in reasoning, decision-making, and instruction following.
Approach:	They propose a benchmark to evaluate the proficiency of large language models (LLMs) in judging and identifying safety risks given agent interaction records.
Outcome:	The proposed model outperforms the best-performing model, GPT-4o, while no other models significantly exceed the random.

CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models (2025.acl-long)

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Yongheng Zhang, Xu Liu, Ruoxi Zhou, Qiguang Chen, Hao Fei, Wenpeng Lu, Libo Qin

Uni-Dubbing: Zero-Shot Speech Synthesis from Visual Articulation (2024.acl-long)

Challenge:	Existing studies on hallucinations in large language models are limited to a single scenario, either cross-lingual or cross-modal.
Approach:	They propose a joint Cross-lingual and Cross-modal hallucinations benchmark to fill this gap . they incorporate cross-lingual, cross-modal scenarios to assess hallucinic capabilities .
Outcome:	The proposed benchmark incorporates both cross-lingual and cross-modal hallucination scenarios to assess the cross-linguistic and crossmodal capabilities of LLMs.

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Songju Lei, Xize Cheng, Mengjiao Lyu, Jianqiao Hu, Jintao Tan, Runlin Liu, Lingyu Xiong, Tao Jin, Xiandong Li, Zhou Zhao

BranchNorm: Robustly Scaling Extremely Deep Transformers (2024.findings-acl)

Challenge:	Multimodal speech synthesis is a key challenge due to the scarcity of datasets that pair audio with corresponding video.
Approach:	They propose a method that incorporates modality alignment during the pre-training phase on multimodal datasets and freezes the video modality extraction component and the encoder module within the pretrained weights.
Outcome:	The proposed method achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9% with single-modality audio.

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Yijin Liu, Xianfeng Zeng, Fandong Meng, Jie Zhou

Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models (2026.findings-acl)

Challenge:	Recent work on DeepNorm scales Transformers into extremely deep (1000 layers) due to the training instability of Transformers, the depths of these SOTA models are still relatively shallow.
Approach:	They propose a branch-rescaled model which dynamically rescales the non-residual branch of Transformer in accordance with the training period.
Outcome:	The proposed approach significantly outperforms existing shallow models on multiple translation tasks and achieves better training stability and convergent performance.

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He Xiao, Qingyao Yang, Dirui Xie, Wendong XU, Zunhai Su, Runming Yang, Haobo Liu, Wenyong Zhou, Zhengwu Liu, Ngai Wong

On the Role of Discriminative Models in Generative Relation Extraction (2026.acl-long)

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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Guozheng Li, Peng Wang, Zijie Xu, Jing Zhou, Jiajun Liu, Ziyu Shang

MirrorQA: Benchmarking Multimodal LLMs on Mirror-Orientation Reasoning (2026.acl-long)

Challenge:	Existing methods for relation extraction (RE) are discriminative and generative . previous studies show that discriminative models can support generative RE .
Approach:	They propose a framework that leverages discriminative models to produce a top-k set of candidate relations and integrates this knowledge into generative models via in-context or prompt learning.
Outcome:	The proposed framework achieves state-of-the-art on five widely used RE benchmarks.

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Jingping Liu, Xingchen Peng, Yan Zhou, Ziyan Liu, Jie Zhai, Ronghao Chen, Huacan Wang, Xiaofeng Jia

HMEAE: Hierarchical Modular Event Argument Extraction (D19-1)

Challenge:	Multimodal large language models (MLLMs) have achieved remarkable progress in recent years, yet their ability to perform left–right reasoning in mirror contexts remains underexplored.
Approach:	They propose a benchmark to evaluate MLLMs' ability to distinguish left from right from a subject-centered perspective.
Outcome:	The proposed benchmarks show that even the best performing models achieve only 65.40% accuracy, far below the 99.28% accuracy of humans.

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Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, Xiang Ren

ReasMark: A Robust Watermark for Attributing LLM Reasoning Under Knowledge Distillation Attacks (2026.acl-long)

Challenge:	Existing event extraction methods classify each argument role independently, ignoring conceptual correlations between different argument roles.
Approach:	They propose a Hierarchical Modular Event Argument Extraction model to provide inductive bias from the concept hierarchy of event argument roles.
Outcome:	The proposed model outperforms existing methods on real-world datasets and shows that it leverages useful knowledge from the concept hierarchy.

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Peizhuo Lv, Ruihua Zhou, Yunpeng Li, Ruigang Liang, Xingshuo Han, XiaoFeng Wang, Wei Dong, Yuling Liu

Triangular Architecture for Rare Language Translation (P18-1)

Challenge:	Existing reasoning-enhanced large language models fail to provide reliable attribution of reasoning behavior once it is transferred through knowledge distillation.
Approach:	They propose to embed a reasoning-length gap in a model by querying a target domain and training a local student to imitate its outputs.
Outcome:	et al. show that ReasMark outperforms baselines while preserving task utility.

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Shuo Ren, Wenhu Chen, Shujie Liu, Mu Li, Ming Zhou, Shuai Ma

An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling (2022.naacl-main)

Challenge:	Empirical results show that Neural Machine Translation (NMT) performs poor on low-resource pairs especially when Z is a rare language.
Approach:	They propose a triangular triangulation technique to leverage bilingual data to optimize the translation performance of low-resource pairs.
Outcome:	Empirical results show that the proposed architecture significantly improves translation quality of rare languages on MultiUN and IWSLT2012 datasets and even better when combining back-translation methods.

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Peiyi Wang, Runxin Xu, Tianyu Liu, Qingyu Zhou, Yunbo Cao, Baobao Chang, Zhifang Sui

COF: Adaptive Chain of Feedback for Comparative Opinion Quintuple Extraction (2025.coling-main)

Challenge:	Existing approaches to tagging tasks are limited to predefined classes and require large-scale annotated data.
Approach:	They propose an Enhanced Span-based Decomposition method for Few-Shot Sequence Labeling to generalize on emerging, resource-scare domains.
Outcome:	The proposed method achieves state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS, and is more robust in noisy and nested tagging scenarios.

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Qingting Xu, Kaisong Song, Chaoqun Liu, Yangyang Kang, Xiabing Zhou, Jun Lin, Yu Hong

Chain-of-Quizzes: Pedagogy-inspired Example Selection in In-Context-Learning (2024.findings-acl)

Challenge:	Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text.
Approach:	They propose a model-unaware adaptive chain-of-feedback method to extract quintuples from product review text.
Outcome:	The proposed method improves performance on three benchmarks.

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Yiquan Wu, Anlai Zhou, Yuhang Liu, Yifei Liu, Adam Jatowt, Weiming Lu, Jun Xiao, Kun Kuang

Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt (2024.naacl-long)

Challenge:	In-context learning (ICL) is a powerful tool for enhancing large language models (LLMs) by mimicking the human learning process.
Approach:	They propose a Chain-of-Quizzes framework that uses LLMs to answer a quiz to sift 'good' examples, combine them iteratively with the increasing complexity, and utilize a final exam to gauge the combined example chains.
Outcome:	The proposed framework outperforms baseline models on diverse reasoning datasets and shows that it is scalable and can be used in future research.

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Yongqi Wang, Ruofan Hu, Rongjie Huang, Zhiqing Hong, Ruiqi Li, Wenrui Liu, Fuming You, Tao Jin, Zhou Zhao

Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024.emnlp-main)

Challenge:	Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing.
Approach:	They propose a singing-voice-synthesis method that enables attribute controlling on singer gender, vocal range and volume with natural language.
Outcome:	The proposed method achieves favorable control ability and audio quality.

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Yanchen Liu, Mingyu Ma, Wenna Qin, Azure Zhou, Jiaao Chen, Weiyan Shi, Wei Wang, Diyi Yang

Entropy-based Exploration Conduction for Multi-step Reasoning (2025.findings-acl)

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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Jinghan Zhang, Xiting Wang, Fengran Mo, Yeyang Zhou, Wanfu Gao, Kunpeng Liu

Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding (2022.findings-emnlp)

Challenge:	Existing methods to automatically decide the depth of exploration of the reasoning procedure lead to high cost and a lack of flexibility.
Approach:	They propose a method that dynamically adjusts the exploration depth during multi-step reasoning by monitoring LLM’s output entropy and variance entropic.
Outcome:	The proposed method captures the uncertainty of the current step and the fluctuation of uncertainty across consecutive reasoning steps and then selects whether to deepen, expand, or stop exploration according to the probability.

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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

Incomplete Utterance Rewriting as Semantic Segmentation (2020.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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Qian Liu, Bei Chen, Jian-Guang Lou, Bin Zhou, Dongmei Zhang

TasTe: Teaching Large Language Models to Translate through Self-Reflection (2024.acl-long)

Challenge:	Recent studies focus on the task of incomplete utterance rewriting as a machine translation task.
Approach:	They propose a semantic segmentation task which incorporates edit operations into the problem and predicts a word-level edit matrix.
Outcome:	The proposed approach outperforms existing baselines on several datasets and is four times faster than the standard approach in inference.

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Yutong Wang, Jiali Zeng, Xuebo Liu, Fandong Meng, Jie Zhou, Min Zhang

MedCoT: Medical Chain of Thought via Hierarchical Expert (2024.emnlp-main)

Challenge:	Existing approaches to enhance LLMs' performance in machine translation are unable to fully exploit their instruction-following capabilities.
Approach:	They propose a framework for translating through self-reflection that involves two stages of inference . they propose to use the framework to refine LLMs' preliminary translations .
Outcome:	The proposed framework can produce translation outputs that match the quality of NMT systems.

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Jiaxiang Liu, Yuan Wang, Jiawei Du, Joey Zhou, Zuozhu Liu

TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers’ Guidance (2025.findings-emnlp)

Challenge:	Existing methods for medical visual question answering lack robustness and reasoning paths for real-world medical diagnostics.
Approach:	They propose a hierarchical expert verification reasoning chain method to enhance interpretability and accuracy in medical visual question answering.
Outcome:	The proposed method outperforms existing methods on four standard Med-VQA datasets.

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Jingxian Xu, Mengyu Zhou, Weichang Liu, Hanbing Liu, Shi Han, Dongmei Zhang

Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) have made significant strides in problem-solving by incorporating reasoning processes, but this enhanced reasoning capability results in an increased number of output tokens during inference, leading to higher computational costs.
Approach:	They propose a method that internalizes explicit reasoning into the model’s habitual behavior through a Teacher-Guided compression strategy inspired by human cognition.
Outcome:	The proposed method reduces inference-time costs while maintaining high performance while preserving high quality and diversity of the distillation dataset.

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Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

Contrastive Out-of-Distribution Detection for Pretrained Transformers (2021.emnlp-main)

Challenge:	Existing approaches to grammatical error correction (GEC) are sequence-to-sequence and sequence-edit.
Approach:	They propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally.
Outcome:	The proposed framework outperforms baselines and state-of-the-art methods on English and Chinese datasets.

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Wenxuan Zhou, Fangyu Liu, Muhao Chen

Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation (2021.acl-short)

Challenge:	Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution, but in real-world scenarios, out-of-distribution instances can cause semantic shift problems.
Approach:	They propose to fine-tune the Transformers with a contrastive loss, which improves the compactness of representations, and to use the Mahalanobis distance in the model's penultimate layer to detect OOD instances.
Outcome:	The proposed method outperforms baselines in the real-world and achieves near-perfect OOD detection performance.

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Yangyifan Xu, Yijin Liu, Fandong Meng, Jiajun Zhang, Jinan Xu, Jie Zhou

Identifying and Mitigating Social Bias Knowledge in Language Models (2025.findings-naacl)

Challenge:	Existing approaches to token-level adaptive training only use static word frequency information without considering the source language.
Approach:	They propose a bilingual mutual information based adaptive objective that assigns weights to target tokens with higher BMI . they propose to use this approach to improve token-level adaptive training .
Outcome:	The proposed method improves token-level adaptive training on two languages.

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Ruizhe Chen, Yichen Li, Jianfei Yang, Yang Feng, Joey Tianyi Zhou, Jian Wu, Zuozhu Liu

Towards Robust Temporal Activity Localization Learning with Noisy Labels (2024.lrec-main)

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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Daizong Liu, Xiaoye Qu, Xiang Fang, Jianfeng Dong, Pan Zhou, Guoshun Nan, Keke Tang, Wanlong Fang, Yu Cheng

Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)

Challenge:	Existing methods for temporal activity localization are expensive and difficult to satisfy due to subjective labeling.
Approach:	They propose a new TAL setting where a TAL model should be robust to mixed training data with noisy moment boundaries.
Outcome:	The proposed method is significantly more robust to noisy training data than existing methods.

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Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li

RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2024.findings-acl)

Challenge:	Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored.
Approach:	They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching.
Outcome:	The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions.

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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

FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models (2025.coling-main)

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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Shu Liu, Shangqing Zhao, Chenghao Jia, Xinlin Zhuang, Zhaoguang Long, Jie Zhou, Aimin Zhou, Man Lan, Yang Chong

Instruction Position Matters in Sequence Generation with Large Language Models (2024.findings-acl)

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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Yijin Liu, Xianfeng Zeng, Chenze Shao, Fandong Meng, Jie Zhou

Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)

Challenge:	Large language models (LLMs) can perform conditional sequence generation tasks, such as translation or summarization, through instruction fine-tuning.
Approach:	They propose to shift the position of task instructions after the input sentences to enhance the model's instruction-following capability.
Outcome:	The proposed method outperforms traditional settings across various model scales (1B / 7B & 13B) and different sequence generation tasks (translation and summarization) without any additional data or annotation costs.

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Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)

Challenge:	Existing methods to classify QA text contain rich sentiment information.
Approach:	They propose a task/method to address QA sentiment analysis by annotating QA text pair with annotation guidelines.
Outcome:	The proposed method can learn the matching vectors of each Q-sentence, A-sentent unit.

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Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin

UniDataBench: Evaluating Data Analytics Agents Across Structured and Unstructured Data (2026.acl-long)

Challenge:	Existing methods for event extraction neglect grammatical incorrectness, structure misalignment, and semantic drifting . et al., 2004; Ahn, 2006) show that the proposed method generates more diverse text representations for event extracting compared with the state-of-the-art.
Approach:	They propose a framework for event extraction that generates additional training data and iteratively selects the effective subset from the generated training data.
Outcome:	The proposed method generates more diverse representations of training data and achieves comparable results with the state-of-the-art.

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Han Weng, Zhou Liu, Yuanfeng Song, Xiaoming Yin, Xing Chen, Wentao Zhang

Event Detection without Triggers (N19-1)

Challenge:	Existing benchmarks do not assess agents’ capabilities across data types . Existing tools only evaluate agents' ability to extract reasonable insights across data formats.
Approach:	They propose a multi-source benchmark to evaluate the performance of data analytics agents in handling diverse data sources.
Outcome:	The proposed agent performs end-to-end analysis over diverse data sources by automatically discovering cross-source linkages, decomposing goals, and generating robust, self-correcting code to extract actionable insights.

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Shulin Liu, Yang Li, Feng Zhang, Tao Yang, Xinpeng Zhou

Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning (2023.eacl-main)

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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Chunyang Jiang, Tianchen Zhu, Haoyi Zhou, Chang Liu, Ting Deng, Chunming Hu, Jianxin Li

Recovering Gold from Black Sand: Multilingual Dense Passage Retrieval with Hard and False Negative Samples (2022.emnlp-main)

Challenge:	Multi-hop reasoning is a common approach for query answering, but can be biased to spurious paths which coincidentally lead to the correct answer with poor explanation.
Approach:	They propose a method that quantitatively estimates to what extent a path is spurious by a metric called Path Spuriousness (PS) they propose KG reasoning, which infers new facts along existing paths in KGs.
Outcome:	The proposed model significantly improves the agent’s ability to prevent spurious paths while keeping comparable to state-of-the-art performance.

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Tianhao Shen, Mingtong Liu, Ming Zhou, Deyi Xiong

ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning (2021.acl-long)

Challenge:	Existing approaches to recover and utilize negative samples have not been effective on all languages.
Approach:	They propose a multilingual dense passage retrieval framework to recover and utilize negative samples.
Outcome:	The proposed framework outperforms strong sparse, dense and hybrid baselines on a high-quality dataset covering eleven typologically diverse languages.

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Yujia Qin, Yankai Lin, Ryuichi Takanobu, Zhiyuan Liu, Peng Li, Heng Ji, Minlie Huang, Maosong Sun, Jie Zhou

CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild (2021.emnlp-main)

Challenge:	Existing pre-training objectives do not explicitly model relational facts in text . Experimental results show that ERICA can improve typical PLMs on several language understanding tasks, including relation extraction, entity typing and question answering.
Approach:	They propose a contrastive learning framework ERICA to obtain a deep understanding of entities and relations in text.
Outcome:	The proposed framework can improve PLMs on several language understanding tasks, especially under low-resource settings.

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Yuan Yao, Jiaju Du, Yankai Lin, Peng Li, Zhiyuan Liu, Jie Zhou, Maosong Sun

CrisPrune: Combining Contextual Relevance and Intrinsic Saliency for Efficient Visual Token Pruning in MLLMs (2026.findings-acl)

Challenge:	Existing relation extraction methods focus on extracting relational facts between entity pairs within single sentences or documents.
Approach:	They present a problem of cross-document relation extraction (CRE) using human annotations.
Outcome:	The proposed dataset is the first human-annotated cross-document RE dataset . it shows that it is challenging to existing RE methods including strong BERT-based models.

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Ziniu Liu, Shuheng Zhou, Mingqing Liu, Hao Deng, Huijia Zhu

Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism (2021.acl-long)

Challenge:	Existing methods for visual token pruning compromise the integrity of visual understanding in pursuit of efficiency.
Approach:	They propose a model-agnostic method that integrates visual saliency and text relevance to reconcile efficiency with understanding by integrating visual salions and text relevant.
Outcome:	The proposed method outperforms state-of-the-art methods on LLaVA-NeXT . it achieves 13 decrease in FLOPs while maintaining 97% of original performance .

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Tong Zhou, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Kun Niu, Weifeng Chong, Shengping Liu

RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation (2025.emnlp-main)

Challenge:	Existing methods for ICD coding ignore the long-tail of code frequency or noisy clinical notes.
Approach:	They propose to use an interactive shared representation network to model code co-occurrences while focusing on the clinical note's noteworthy part and extract valuable information through a self-distillation learning mechanism to solve the long-tail problem.
Outcome:	The proposed model reduces the long-tail of code frequency and noise in clinical notes and extracts valuable information through a self-distillation learning mechanism.

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Sashuai Zhou, Weinan Gan, Qijiong Liu, Ke Lei, Jieming Zhu, Hai Huang, Yan Xia, Ruiming Tang, Zhenhua Dong, Zhou Zhao

MT2: Towards a Multi-Task Machine Translation Model with Translation-Specific In-Context Learning (2023.emnlp-main)

Challenge:	Existing methods for addressing item-level user interests are lacking in cross-domain generalization . RecBase model is domain-agnostic and can be used to enhance recommender systems' effectiveness .
Approach:	They propose a domain-agnostic foundational model pretrained with a recommendation-oriented objective that leverages a large-scale, heterogeneous, cross-domain corpus with unified textual representations and feature mappings to enhance cross- domain generalization.
Outcome:	The proposed model matches or surpasses baselines in zero-shot and cross-domain recommendation tasks on eight real-world datasets.

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Chunyou Li, Mingtong Liu, Hongxiao Zhang, Yufeng Chen, Jinan Xu, Ming Zhou

Sharpness-Aware Minimization with Dynamic Reweighting (2022.findings-emnlp)

Challenge:	Sentence-level translation, document-level and terminology constrained translations are important in machine translation.
Approach:	They propose a multi-task machine translation model that integrates translation memory sentences . they propose 'in-context learning' paradigm that allows translation-specific context learning .
Outcome:	The proposed model improves translation memory, document-level translation, and document-constrained translation tasks.

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Wenxuan Zhou, Fangyu Liu, Huan Zhang, Muhao Chen

RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game (2026.findings-acl)

Challenge:	Deep neural networks are often overparameterized and can overfit training data.
Approach:	They propose an adversarial weight minimization algorithm that conducts adversarials and finds a common adversaria per-batch.
Outcome:	The proposed algorithm finds a common adversarial weight perturbation per-batch.

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Shijia Xu, Yu Wang, Xiaolong Jia, Zhou Wu, Kai Liu, April Xiaowen Dong

Challenge:	despite the adoption of Large Language Models (LLMs), contract revision remains impeded because generic models treat strict legal constraints as mere suggestions.
Approach:	They propose a risk-constrained bilevel Stackelberg framework that models high-stakes revision as a strategic interaction rather than an open-ended conversation.
Outcome:	The proposed framework achieves state-of-the-art performance with an average RRR of 84.21% and enhanced token efficiency.

Skip-Thinking: Chunk-wise Chain-of-Thought Distillation Enable Smaller Language Models to Reason Better and Faster (2025.emnlp-main)

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Xiaoshu Chen, Sihang Zhou, Ke Liang, Xiaoyu Sun, Xinwang Liu

Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark (2022.findings-emnlp)

Challenge:	Existing methods train small language models to learn long rationales in one iteration.
Approach:	They propose a method that uses a heuristic search to divide rationale into internal chunks . they propose CWT, which uses CWt to focus SLM on learning from only one chunk per iteration.
Outcome:	The proposed method can guide a large language model (LLM) in reasoning tasks.

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Jingyan Zhou, Jiawen Deng, Fei Mi, Yitong Li, Yasheng Wang, Minlie Huang, Xin Jiang, Qun Liu, Helen Meng

Latent Learningscape Guided In-context Learning (2024.findings-acl)

Challenge:	a number of safety concerns hinder the deployment of open-domain dialog systems, such as offensive languages and toxic behaviors, such social bias is difficult to detect.
Approach:	They propose a Dial-Bias Framework for analyzing social bias in conversations . they introduce a Chinese social bias dialog dataset and conduct in-depth ablation studies .
Outcome:	The proposed framework is the first annotated Chinese social bias dialog dataset . the proposed framework also provides a fine-grained dialog bias measurement benchmark .

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Anlai Zhou, Sunshine Jiang, Yifei Liu, Yiquan Wu, Kun Kuang, Jun Xiao

Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification (2022.coling-1)

Challenge:	Existing methods to select demonstrations based on surface-level semantic similarities fall short of identifying the most fitting ones.
Approach:	They propose a method that characterizes latent learningscape features of demonstrations and uses them to create more effective prompts.
Outcome:	The proposed method outperforms leading models in arithmetic, commonsense, and symbolic reasoning tasks showing an average increase in scores by 7.4 percentage points.

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Xu Zhang, Zejie Liu, Yanzheng Xiang, Deyu Zhou

DopplerBAS: Binaural Audio Synthesis Addressing Doppler Effect (2023.findings-acl)

Challenge:	Existing frameworks for text classification employing pre-trained models are constrained by the difficulty of the task.
Approach:	They propose a framework which implements a two-stage training strategy to fully exploit the knowledge in pre-trained models.
Outcome:	The proposed framework outperforms state-of-the-art classification models on six text classification corpora.

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Jinglin Liu, Zhenhui Ye, Qian Chen, Siqi Zheng, Wen Wang, Zhang Qinglin, Zhou Zhao

Compilable Neural Code Generation with Compiler Feedback (2022.findings-acl)

Challenge:	Existing methods for binaural audio synthesis are limited in phase estimation, which is crucial for spatial hearing.
Approach:	They propose a method to explicitly address the Doppler effect of the moving speaker . it calculates the radial relative velocity of the speaker in spherical coordinates .
Outcome:	The proposed method improves the representative WarpNet and BinauralGrad backbones in phase error metric and reaches a new state of the art (SOTA) it is compared with the current method which is limited in phase estimation .

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Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, Qun Liu

DiffPO: Diffusion-styled Preference Optimization for Inference Time Alignment of Large Language Models (2025.acl-long)

Challenge:	Existing deep-learning approaches model code generation as text generation, but few of them account for compilability of the generated programs.
Approach:	They propose a three-stage pipeline utilizing compiler feedback for compilable code generation to improve compilability.
Outcome:	The proposed pipeline improves compilability of generated programs by combining compiler feedback, language model fine-tuning, and compilable discrimination.

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Ruizhe Chen, Wenhao Chai, Zhifei Yang, Xiaotian Zhang, Ziyang Wang, Tony Quek, Joey Tianyi Zhou, Soujanya Poria, Zuozhu Liu

Towards Enhancing Health Coaching Dialogue in Low-Resource Settings (2022.coling-1)

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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Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp, Bing Liu, Ben Gerber, Nikolaos Agadakos, Shweta Yadav

Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models (2024.acl-long)

Challenge:	Health coaching is cost-prohibitive due to its highly personalized nature.
Approach:	They propose to build a health coaching dialogue system that converses with patients . they propose to use simplified NLU and NLG frameworks and mechanism-conditioned empathetic response generation.
Outcome:	The proposed system generates more empathetic, fluent, and coherent responses . it outperforms the state-of-the-art in NLU tasks while requiring less annotations.

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Zhiwei He, Binglin Zhou, Hongkun Hao, Aiwei Liu, Xing Wang, Zhaopeng Tu, Zhuosheng Zhang, Rui Wang

AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code (2026.findings-acl)

Challenge:	Existing text watermarking technologies lack consistency when texts are translated into different languages.
Approach:	They propose a cross-lingual watermark removal attack to bypass watermarking by first obtaining a response from an LLM in a pivot language and then translating it into the target language.
Outcome:	The proposed method can remove watermarks without performance loss by obtaining a response from an LLM in a pivot language and then translating it into the target language.

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ShangZhan Li, Xinyu Yin, Xuanyu Jin, Ye He, Yuxin Zhou, Yuxuan Li, Xu Han, Wanxiang Che, Qi Shi, Ting Liu, Maosong Sun

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)

Challenge:	Current development practices face a dichotomy between automation and performance.
Approach:	They propose a framework to empower LLMs with the capability of automated explicit vectorization.
Outcome:	The proposed framework achieves state-of-the-art performance on the SSE and AVX subsets of SimdBench.

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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

Enhancing Explainable Rating Prediction through Annotated Macro Concepts (2024.acl-long)

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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Huachi Zhou, Shuang Zhou, Hao Chen, Ninghao Liu, Fan Yang, Xiao Huang

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)

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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Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao

CLAPSpeech: Learning Prosody from Text Context with Contrastive Language-Audio Pre-Training (2023.acl-long)

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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Zhenhui Ye, Rongjie Huang, Yi Ren, Ziyue Jiang, Jinglin Liu, Jinzheng He, Xiang Yin, Zhou Zhao

DTELS: Towards Dynamic Granularity of Timeline Summarization (2025.naacl-long)

Challenge:	Existing methods for expressive text-to-speech only implicitly learn prosody with masked token reconstruction tasks.
Approach:	They propose a cross-modal contrastive pre-training framework that learns from prosody variance of the same text token under different contexts.
Outcome:	The proposed framework can learn from prosody variance of a text token under different contexts.

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Chenlong Zhang, Tong Zhou, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao

Summarization

Challenge:	Existing timeline summarizations lack flexibility to meet diverse granularity needs . a fine-grained timeline showing the technical details is preferred for news topics .
Approach:	They propose a new paradigm to construct adaptive timelines based on user instructions or requirements.
Outcome:	The proposed timelines are informative and granularly consistent, but they struggle to generate consistent timelines.

KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding (2025.findings-acl)

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Zhangchen Xu, Yang Liu, Yueqin Yin, Mingyuan Zhou, Radha Poovendran

SMR: State Memory Replay for Long Sequence Modeling (2024.findings-acl)

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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Biqing Qi, Junqi Gao, Kaiyan Zhang, Dong Li, Jianxing Liu, Ligang Wu, Bowen Zhou

Challenge:	Existing state space models (SSMs) address non-uniform sampling, but their recursive structures impede efficient SSM computation via convolution.
Approach:	They propose a plug-and-play mechanism to solve the Non-Stable State problem by adjusting input sequences with early memories.
Outcome:	The proposed method overcomes the non-uniform sample processing problem . it can achieve Sampling Step Adaptation (SSA) by adjusting input sequences with early memories.

Improving Constituent Representation with Hypertree Neural Networks (2022.naacl-main)

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Hao Zhou, Gongshen Liu, Kewei Tu

ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding (2020.acl-main)

Challenge:	Existing methods of span representation are based on simple derivations from word representations and do not utilize compositional structures of natural language.
Approach:	They propose a hypertree neural network that is structured with constituency parse trees to improve representations of constituent spans.
Outcome:	The proposed model improves representations of constituent spans using constituency parse trees.

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Zhiwen Xie, Guangyou Zhou, Jin Liu, Jimmy Xiangji Huang

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization (2026.findings-acl)

Challenge:	Existing knowledge graph embedding methods are limited in their expressiveness and lack structural information in the embeddable space.
Approach:	They propose to use a relation-aware network to learn query embedding . they first explore the Inception network to further increase interactions between head and relation embedders .
Outcome:	The proposed network improves performance on WN18RR and FB15k-237 datasets.

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Chenxi Zhou, Pengfei Cao, Jiang Li, Bohan Yu, Jinyu Ye, Jun Zhao, Kang Liu

Challenge:	Existing research on PTQ spans three primary directions.
Approach:	They conduct a systematic analysis of post-training quantization failures using PTQ . they show that targeted repair can mitigate Signal Degradation but remains ineffective for Computation Collapse .
Outcome:	The proposed method mitigates Signal Degradation but remains ineffective for Computation Collapse.

StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback (2024.acl-long)

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Shihan Dou, Yan Liu, Haoxiang Jia, Enyu Zhou, Limao Xiong, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang

Towards Explainable Diagnosis: A Self-learned Explanatory Knowledge Base Approach (2026.acl-long)

Challenge:	Existing work integrates reinforcement learning with compiler feedback to enhance code generation quality but the long code generated by LLMs makes RL exploration ineffective.
Approach:	They propose a framework that integrates reinforcement learning and compiler feedback to enhance code generation quality.
Outcome:	The proposed framework outperforms state-of-the-art approaches in corresponding benchmarks and integrates reinforcement learning with compiler feedback to improve code generation quality.

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Dongqi Huang, Tong Zhou, Zhuoran Jin, Shenghui Shi, null Maoyujiao, Kang Liu, Jun Zhao, Yubo Chen

Enhancing Chat Language Models by Scaling High-quality Instructional Conversations (2023.emnlp-main)

Challenge:	Large language models (LLMs) have great potential to facilitate explainable diagnosis, but their effectiveness is often constrained by insufficient diagnostic expertise.
Approach:	They propose a unified LLM-based framework for faithful and explainable diagnosis that builds a high-quality diagnostic knowledge base through a record-driven explanation learning paradigm.
Outcome:	The proposed framework outperforms baselines on the DiReCT and JAMA benchmarks and improves the explanation completeness metric from 64.5% to 76.9% over the best existing methods.

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Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Shengding Hu, Zhiyuan Liu, Maosong Sun, Bowen Zhou

LogiCoT: Logical Chain-of-Thought Instruction Tuning (2023.findings-emnlp)

Challenge:	a recent study validates the effectiveness of chat language models by fine-tuning instruction data.
Approach:	They propose to use a large-scale dataset of instructional conversations to fine-tune a conversational model on instruction data.
Outcome:	The proposed model outperforms open-source models in key metrics including scale, average length, diversity, coherence, etc.

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Hanmeng Liu, Zhiyang Teng, Leyang Cui, Chaoli Zhang, Qiji Zhou, Yue Zhang

Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling (2025.acl-long)

Challenge:	Recent work on self-instruction tuning has focused on enhancing the general proficiency of models.
Approach:	They propose a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4 that harvests instructions for prompting GPT to generate chain-of thought rationales.
Outcome:	The proposed dataset enables the model to generate chain-of-thought rationales with GPT-4.

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Jiayi Zeng, Yizhe Feng, Mengliang He, Wenhui Lei, Wei Zhang, Zeming Liu, Xiaoming Shi, Aimin Zhou

A Discrete CVAE for Response Generation on Short-Text Conversation (D19-1)

Challenge:	Current error-handling works are performed in a passive manner, with explicit error- handling instructions.
Approach:	They propose a new benchmark to analyze LLMs' performance on a mis-prompt benchmark and a dataset to promote further research.
Outcome:	The proposed benchmark shows that current LLMs show poor performance on proactive error handling, and that SFT improves on error handling instances.

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Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Guodong Zhou, Shuming Shi

Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS (2026.findings-acl)

Challenge:	Neural conversation models are easy to generate bland and generic responses . however, their improvement of generating high-quality responses is still unsatisfactory .
Approach:	They propose to use a discrete latent variable with an explicit semantic meaning to improve the conditional variational autoencoder on short-text conversation.
Outcome:	The proposed model outperforms various kinds of generation models under automatic and human evaluations and generates more diverse and informative responses.

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Shijia Xu, Zhou Wu, Xiaolong Jia, Yu Wang, Kai Liu, April Xiaowen Dong

Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models (2026.findings-acl)

Challenge:	Existing approaches to retrieval-augmented generation still face problems with low context utilization and frequent hallucinations.
Approach:	They propose a framework that reformulates retrieval and generation as constrained optimization and path planning.
Outcome:	The proposed framework significantly improves reasoning accuracy on complex queries while reducing hallucinations.

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Chenxi Zhou, Pengfei Cao, Jiang Li, Bohan Yu, Jinyu Ye, Jun Zhao, Kang Liu

Chain-of-Rewrite: Aligning Question and Documents for Open-Domain Question Answering (2024.findings-emnlp)

Challenge:	Existing scaling laws focus on general performance, overlooking crucial fine-grained factors and how quantization differentially impacts diverse knowledge capabilities.
Approach:	They propose a framework that unifies model size, bit-width, and fine-grained factors into memorization, application, and reasoning.
Outcome:	The proposed framework shows strong fit and cross-architecture consistency on 293 different PTQ configurations.

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Chunlei Xin, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

Incentivizing Parametric Knowledge via Reinforcement Learning with Verifiable Rewards for Cross-Cultural Entity Translation (2026.acl-long)

Challenge:	Existing approaches to answer open-domain question have encountered term mismatch and limited interaction between IR systems and large language models.
Approach:	They propose a method which leverages the guidance and feedback gained from the analysis to provide faithful and consistent extensions for effective question answering.
Outcome:	Experiments on four open-domain question answering datasets show the proposed method performs well under zero-shot settings.

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Jiang Zhou, Xiaohu Zhao, Xinwei Wu, Tianyu Dong, Hao Wang, Yangyang Liu, Heng Liu, Linlong Xu, Longyue Wang, Weihua Luo, Deyi Xiong

CoAug: Combining Augmentation of Labels and Labelling Rules (2023.findings-acl)

Challenge:	Current systems often fall short of this goal in settings where translation hinges on culturally grounded entities such as books, films, places, songs and idioms.
Approach:	They propose a framework that anchors supervision on a verifiable, entity-level reward signal and incorporates lightweight structural gates to stabilize optimization.
Outcome:	The proposed framework improves on XC-Translate and shows that it can learn a robust reasoning process rather than imitating reference translations.

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Rakesh R. Menon, Bingqing Wang, Jun Araki, Zhengyu Zhou, Zhe Feng, Liu Ren

Mitigating Hallucinations in LM-Based TTS Models via Distribution Alignment Using GFlowNets (2025.emnlp-main)

Challenge:	Named Entity Recognition (NER) tasks require large labeled datasets to perform well.
Approach:	They propose a co-augmentation framework that bootstraps predictions from each model to improve few-shot models and rule-augmentation models by bootstrapping them.
Outcome:	The proposed model outperforms strong weak-supervision-based models by 6.5 F1 points . the proposed model can learn from limited labeled data and perform better on small datasets .

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Chenlin Liu, Minghui Fang, Patrick Zhang, Wei Zhou, Jie Gao, Jiqing Han

A Critical Analysis of Document Out-of-Distribution Detection (2023.findings-emnlp)

Challenge:	Existing mitigation strategies for Text-to-Speech systems require excessive training resources or inference latency.
Approach:	They propose a GFlOwNet-guided distribution AlignmenT framework that mitigates hallucinations without relying on massive resources or inference latency.
Outcome:	The proposed framework reduces over 50% character error rates and lowers uncertainty by up to 58% on challenging test cases.

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Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova

MIND: A Large-scale Dataset for News Recommendation (2020.acl-main)

Challenge:	Existing document understanding models focus on single-modal inputs such as images or texts.
Approach:	They propose to use a spatial-aware adapter to adapt transformer-based language models to document domain to exploit multi-modal information.
Outcome:	The proposed model significantly improves the OOD detection performance compared to using a standard language model and to competitive baselines.

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Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou

Unlocking Speech Instruction Data Potential with Query Rewriting (2025.findings-acl)

Challenge:	Personalized news recommendation is an important technique for personalized news service.
Approach:	They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding .
Outcome:	The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body.

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Yonghua Hei, Yibo Yan, Shuliang Liu, Huiyu Zhou, Linfeng Zhang, Xuming Hu

Learning the Beauty in Songs: Neural Singing Voice Beautifier (2022.acl-long)

Challenge:	Existing LLMs lack datasets and biased training tasks to follow speech instructions.
Approach:	They propose a query rewriting framework that uses multiple agents to annotate and validate the synthesized speech.
Outcome:	The proposed framework can transform text instructions into distributions more suitable for TTS models for speech synthesis without human annotation.

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Jinglin Liu, Chengxi Li, Yi Ren, Zhiying Zhu, Zhou Zhao

Speech & Audio in NLP

Challenge:	Existing techniques for pitch correction are limited to intonation but ignore the overall aesthetic quality.
Approach:	They propose a novel time-warping approach for pitch correction to synchronize the amateur recording with the template pitch curve.
Outcome:	The proposed model improves intonation and vocal tone while keeping content and vocal timbre.

A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge (2022.findings-acl)

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Tao Wang, Linhai Zhang, Chenchen Ye, Junxi Liu, Deyu Zhou

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)

Challenge:	Existing methods to predict medical codes from clinical notes lack interpretability due to lengthy and noisy clinical notes.
Approach:	They propose a framework based on medical concept driven attention to integrate external knowledge for explainable medical code prediction from clinical notes.
Outcome:	The proposed framework outperforms state-of-the-art methods on a benchmark dataset showing that it is more accurate than existing methods.

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Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei

Learning to Collaborate for Question Answering and Asking (N18-1)

Challenge:	Existing work shows that pre-trained models can improve in various natural language processing tasks.
Approach:	They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data.
Outcome:	The proposed framework is superior to existing models on speech-to-text processing tasks.

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Duyu Tang, Nan Duan, Zhao Yan, Zhirui Zhang, Yibo Sun, Shujie Liu, Yuanhua Lv, Ming Zhou

Soft Contextual Data Augmentation for Neural Machine Translation (P19-1)

Challenge:	Question answering (QA) and question generation (QG) are closely related tasks.
Approach:	They propose a training algorithm that generalizes both Generative Adversarial Network and Generating Domain-Adaptive Nets under the question answering scenario.
Outcome:	The proposed training algorithm generalizes both Generative Adversarial Network (GAN) and Generating Domain-Adaptive Nets (GDAN) under the question answering scenario.

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Fei Gao, Jinhua Zhu, Lijun Wu, Yingce Xia, Tao Qin, Xueqi Cheng, Wengang Zhou, Tie-Yan Liu

Towards Scalable Lightweight GUI Agents via Multi-role Orchestration (2026.findings-acl)

Challenge:	Existing methods for enhancing training data are limited in natural language tasks due to text characteristics.
Approach:	They propose a data augmentation method that softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words.
Outcome:	The proposed method outperforms baseline methods on small and large scale machine translation 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

Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020.acl-main)

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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Xu Han, Yi Dai, Tianyu Gao, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Challenge:	Existing methods to learn incessantly emerging novel relations are overfitting the few memorized examples of old relations, causing confusion among existing relations.
Approach:	They introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning.
Outcome:	The proposed method outperforms state-of-the-art models in catastrophic forgetting old relations.

Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network (2020.acl-main)

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Yutai Hou, Wanxiang Che, Yongkui Lai, Zhihan Zhou, Yijia Liu, Han Liu, Ting Liu

Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (2020.coling-main)

Challenge:	Existing few-shot learning methods for slot tagging are based on similarity-based methods, but they are difficult to apply to an unseen domain due to the discrepancy of label sets.
Approach:	They propose a label-enhanced task-adaptive projection network to transfer abstract label dependency patterns as transition scores into the conditional random field (CRF) Experimental results show that their model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting.
Outcome:	The proposed model outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting.

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Huiwei Zhou, Yibin Xu, Weihong Yao, Zhe Liu, Chengkun Lang, Haibin Jiang

Learning Query Adaptive Anchor Representation for Inductive Relation Prediction (2023.findings-acl)

Challenge:	Existing approaches to document-level relation extraction are difficult to establish direct connections between distant entity pairs.
Approach:	They propose a global context-enhanced Graph Convolutional Network model which captures rich global context information of entities in a document.
Outcome:	The proposed model captures rich global context information of entities in a document.

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Zhiwen Xie, Yi Zhang, Jin Liu, Guangyou Zhou, Jimmy Huang

Chinese Court Simulation with LLM-Based Agents System (2026.findings-acl)

Challenge:	Existing methods to infer the missing links between entities are limited to the transductive setting . Query Adaptive Anchor Representation (QAAR) model is based on entity-independent features .
Approach:	They propose a query adaptive anchor representation model which extracts one opening subgraph and performs reasoning by one time for all candidate triples.
Outcome:	The proposed model outperforms state-of-the-art models in relation prediction task.

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Kaiyuan Zhang, Jiaqi Li, Yueyue Wu, Haitao Li, Cheng Luo, Shaokun Zou, Yujia Zhou, Weihang Su, Yiqun Liu, Qingyao Ai

Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension (2020.acl-main)

Challenge:	Existing studies have neglected the systematic design and procedure evaluation of court simulations, which are critical to the credibility and usage of court simulators in practice.
Approach:	They propose a court simulation paradigm based on the real-world procedure structure of Chinese courts and a framework that focuses on both legal judgment prediction and court procedure analysis.
Outcome:	The proposed model outperforms judges and lawyers from the real trials in many aspects.

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Bo Zheng, Haoyang Wen, Yaobo Liang, Nan Duan, Wanxiang Che, Daxin Jiang, Ming Zhou, Ting Liu

Language Models Resist Alignment: Evidence From Data Compression (2025.acl-long)

Challenge:	Existing approaches to machine reading comprehension treat documents at their hierarchical nature, ignoring their dependencies.
Approach:	They propose a machine reading comprehension benchmark with two-grained answers . they use graph attention networks to model documents at their hierarchical nature .
Outcome:	The proposed framework outperforms existing systems at long and short answer criteria.

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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

Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation (2024.findings-emnlp)

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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Yuhang Zhou, Jing Zhu, Paiheng Xu, Xiaoyu Liu, Xiyao Wang, Danai Koutra, Wei Ai, Furong Huang

TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models (2025.acl-industry)

Challenge:	Knowledge distillation (KD) is a promising solution for large language models, but their deployment remains computationally expensive.
Approach:	They propose a framework which iteratively balances training data within a fixed computational budget and enables the transfer of knowledge from expensive teacher LLMs to smaller student models.
Outcome:	The proposed framework achieves state-of-the-art performance across diverse long-tailed datasets, enhancing both the efficiency and efficacy of the distilled models.

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Deyin Yi, Yihao Liu, Lang Cao, Mengyu Zhou, Haoyu Dong, Shi Han, Dongmei Zhang

SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment (2026.findings-acl)

Challenge:	Tabular data analysis is crucial in many scenarios, yet its complexity and density can make it challenging to determine the most appropriate analysis operations for a new table.
Approach:	They propose a tabular data analysis framework that recommends query-code-result triplets for new tables . they propose Rec-Align, a method to further improve recommendation quality .
Outcome:	The proposed framework achieves 77.0% top-5 recommendation recall on a dataset designed for tabular data analysis recommendation.

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Tianyu Dong, Yangyang Liu, Jiang Zhou, Xinwei Wu, Xiaohu Zhao, Hao Wang, Heng Liu, Linlong Xu, Longyue Wang, Weihua Luo, Shaolin Zhu, Deyi Xiong

Quantification of Large Language Model Distillation (2025.acl-long)

Challenge:	Low-resource language tokens are often routed to different experts than those activated by high-resourced inputs, which hinders their efficacy in multilingual contexts.
Approach:	They propose a framework to transfer specialized capabilities from high-resource languages as anchors to low-resourced languages by using a symmetric Jensen-Shannon constraint.
Outcome:	The proposed framework outperforms standard instruction tuning on 5 low-resource languages and 3 benchmarks.

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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

Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation (2025.coling-main)

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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Caihong Mu, Keyang Zhang, Jialiang Zhou, Yi Liu

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

Challenge:	Existing contrastive learning-based methods struggle with data sparsity in real-world recommendations . Graph collaborative filtering incorporates contrastive training as an auxiliary task to improve performance .
Approach:	They propose a perturbation-driven dual auxiliary contrastive learning task for collaborative filtering . structure perturbation and weight perturbation are used to construct two graphs .
Outcome:	The proposed model outperforms benchmark models on multiple public datasets.

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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

A Survey of Large Language Model-Based Search Agents (2026.acl-long)

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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Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang

HPE: Answering Complex Questions over Text by Hybrid Question Parsing and Execution (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) have revolutionized web search, but their integration is static and cannot handle complex contexts.
Approach:	They analyze existing research and analyze existing work from the perspectives of architecture, optimization, application, and evaluation.
Outcome:	The proposed models can comprehend user intentions and context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web.

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Ye Liu, Semih Yavuz, Rui Meng, Dragomir Radev, Caiming Xiong, Shafiq Joty, Yingbo Zhou

QA‐LIGN: Aligning LLMs through Constitutionally Decomposed QA (2025.findings-emnlp)

Challenge:	End-to-end neural networks excel at answering natural language questions but fail on complex ones . a proposed framework for question parsing and execution on textual QA is designed to combine the strengths of neural and symbolic methods.
Approach:	They propose a framework for question parsing and execution on textual QA . they parse questions into an intermediate representation and use deterministic rules to translate them .
Outcome:	The proposed framework outperforms existing methods in supervised, few-shot, and zero-shot settings while preserving its underlying reasoning process.

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Jacob Dineen, Aswin Rrv, Qin Liu, Zhikun Xu, Xiao Ye, Ming Shen, Zhaonan Li, Shijie Lu, Chitta Baral, Muhao Chen, Ben Zhou

AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation (2023.acl-long)

Challenge:	QA-LIGN decomposes monolithic rewards into interpretable principle-specific evaluations . scalar rewards obscure which objectives drive the training signal .
Approach:	a new method decomposes monolithic rewards into interpretable principle-specific evaluations . QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate .
Outcome:	QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . the results outperform DPO and GRPO with state-of-the-art reward models given equivalent training .

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Rongjie Huang, Huadai Liu, Xize Cheng, Yi Ren, Linjun Li, Zhenhui Ye, Jinzheng He, Lichao Zhang, Jinglin Liu, Xiang Yin, Zhou Zhao

ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models (2024.findings-acl)

Challenge:	Existing models for speech-to-speech translation suffer from distinct degradation in noisy environments and fail to translate visual speech.
Approach:	They propose a text-based audio-visual speech-to-speech translation model that integrates visual information with audio-only data to improve system robustness.
Outcome:	The proposed model outperforms models trained on audio-only corpus in two languages . it also improves with low-resource audio-visual data, compared with baselines .

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Yanan Wu, Jie Liu, Xingyuan Bu, Jiaheng Liu, Zhanhui Zhou, Yuanxing Zhang, Chenchen Zhang, ZhiqiBai ZhiqiBai, Haibin Chen, Tiezheng Ge, Wanli Ouyang, Wenbo Su, Bo Zheng

Continual Learning in Task-Oriented Dialogue Systems (2021.emnlp-main)

Challenge:	ConceptMath evaluates concept-wise mathematical reasoning of Large Language Models (LLMs) Existing benchmarks that evaluate general mathematical reasoning with an average accuracy fail to probe the fine-grained failure modes of mathematical reasoning on specific datasets.
Approach:	They introduce a bilingual, fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models.
Outcome:	The proposed benchmarks evaluate concept-wise mathematical reasoning of Large Language Models with concept-based accuracies.

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Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou, Seungwhan Moon, Paul Crook, Bing Liu, Zhou Yu, Eunjoon Cho, Pascale Fung, Zhiguang Wang

HyperMem: Hypergraph Memory for Long-Term Conversations (2026.acl-long)

Challenge:	Existing continuous learning systems are not designed to add new domains and functionalities through time without incurring the high cost of retraining the whole system.
Approach:	They propose a first-ever continual learning benchmark for task-oriented dialogue systems . they propose 'architecture' method based on residual adapters to implement continual training .
Outcome:	The proposed architectural method performs better than multitask learning while being 20X faster in learning new domains.

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Juwei Yue, Chuanrui Hu, Jiawei Sheng, Zuyi Zhou, Wenyuan Zhang, Tingwen Liu, Li Guo, Yafeng Deng

DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs (2025.findings-emnlp)

Challenge:	Existing approaches to long-term memory management rely on pairwise relations, causing fragmented retrieval.
Approach:	They propose a hypergraph-based hierarchical memory architecture that explicitly models high-order associations using hyperedges.
Outcome:	Experiments show that HyperMem achieves state-of-the-art performance with 92.73% accuracy for long-term conversations.

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Xiabin Zhou, Wenbin Wang, Minyan Zeng, Jiaxian Guo, Xuebo Liu, Li Shen, Min Zhang, Liang Ding

Challenge:	Existing KV cache compression methods enforce a fixed pattern, neglecting task-specific characteristics, which hampers the effective retention of essential information while discarding less important tokens.
Approach:	They propose a Task-Aware KV cache mechanism that dynamically adjusts the KV caching size across different layers based on the characteristics of the tasks.
Outcome:	The proposed method surpasses state-of-the-art methods by 11% on the LongBench dataset even under extreme compression (0.9%)

CompTab: A Comprehensive Benchmark for Real-World TableQA with Complex Reasoning and Irregular Tables (2026.acl-long)

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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

Lightweight Haar Wavelet Subband Pruning for LLMs (2026.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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Jiang Li, Pengfei Cao, Chenxi Zhou, Tian Lan, Xiangdong Su, Kang Liu, Jun Zhao, Guanglai Gao

Challenge:	Large language models (LLMs) have impressive performance but require computational and memory resources.
Approach:	They propose a post-training framework that uses a Haar wavelet transform to prune weights.
Outcome:	The proposed pruning framework reduces pruning time and computational costs by removing less important weights while preserving model architecture.

NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions (2021.findings-emnlp)

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Zhiyu Chen, Honglei Liu, Hu Xu, Seungwhan Moon, Hao Zhou, Bing Liu

ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution (2026.acl-industry)

Challenge:	Existing conversational systems are agent-centric, which assumes the user utterances will closely follow the system ontology.
Approach:	They build a dataset that maps user preferences to an ontology and model user preferences as estimated distributions over the system ontologies.
Outcome:	The proposed system can be used to push existing research from agent-centric to user-centric.

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Jihong Wang, Jiamu Zhou, Weiming Zhang, Teng Wang, Weiwen Liu, Zhuosheng Zhang, Xingyu Lou, Weinan Zhang, Huarong Deng, Jun Wang

SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning (2025.emnlp-main)

Challenge:	Xue et al., 2025): deploying autonomous web agents in production remains difficult due to site heterogeneity and long-horizon instability.
Approach:	They propose a knowledge-evolving agent that can be used to automate web workflows . they use human-in-the-loop knowledge adaptation and knowledge-aligned progressive summarization .
Outcome:	Experiments on WebArena, WebChoreAren and industrial deployment show it outperforms baselines.

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Kaiwen Zhou, Xuandong Zhao, Jayanth Srinivasa, Gaowen Liu, Aosong Feng, Dawn Song, Xin Eric Wang

Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning (2022.findings-emnlp)

Challenge:	Large Reasoning Models (LRMs) introduce a new paradigm of explicitly reasoning before answering, but they pose great safety risks against harmful queries and adversarial attacks.
Approach:	They propose a safety aha moment that activates safety reasoning and leads to a safe response.
Outcome:	The proposed model can generalize to unseen jailbreak prompts while maintaining general abilities.

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Jing Yi, Weize Chen, Yujia Qin, Yankai Lin, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun, Jie Zhou

Decoupling Reasoning and Knowledge Injection for In-Context Knowledge Editing (2025.findings-acl)

Challenge:	Existing delta tuning algorithms freeze most of the parameters and only optimize minimal adaptive parameters.
Approach:	They propose to decompose DETs into a unified optimization subspace and conduct optimization within the subspace.
Outcome:	The proposed DETs achieve comparable performance to the original DET and can be transferred to another DET with non-trivial performance.

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Changyue Wang, Weihang Su, Qingyao Ai, Yujia Zhou, Yiqun Liu

False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize (2026.findings-acl)

Challenge:	Existing knowledge editing approaches directly edit model context without isolating target knowledge from the reasoning path of model inference, resulting in unreliable and low-quality outputs, especially in multi-hop tasks.
Approach:	They propose a framework that separates model reasoning from knowledge editing and propose 'DecKER' that allows users to modify specific factual associations without retraining the entire model.
Outcome:	The proposed framework significantly improves multi-hop reasoning performance by mitigating knowledge conflicts and preserving reasoning integrity.

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Cheng Wang, Zeming Wei, Qin Liu, Wenxuan Zhou, Muhao Chen

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)

Challenge:	Recent work has leveraged probing-based approaches to study the separability of malicious and benign inputs in Large Language Models’ internal representations.
Approach:	They propose to use probing-based methods to study separability of malicious and benign inputs in LLMs' internal representations to detect harmful and benign content.
Outcome:	The proposed methods show that they learn superficial patterns rather than semantic harmfulness.

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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

Diversity and Consistency: Exploring Visual Question-Answer Pair Generation (2021.findings-emnlp)

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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Sen Yang, Qingyu Zhou, Dawei Feng, Yang Liu, Chao Li, Yunbo Cao, Dongsheng Li

Nirvana: A Specialized Generalist Model With Task-Aware Memory Mechanism (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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Yuhua Jiang, Shuang Cheng, Yihao Liu, Ermo Hua, Che Jiang, Weigao Sun, Yu Cheng, Feifei Gao, Biqing Qi, Bowen Zhou

MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues (2024.acl-long)

Challenge:	Existing models lack task-guided specialized memory mechanisms . specialized generalist models excel at general language tasks but struggle in specialized domains.
Approach:	They propose a specialized generalist model with specialized memory and updater that can optimize for specialized domains.
Outcome:	The proposed model matches or surpasses baselines on general benchmarks and achieves lowest perplexity across specialized domains.

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Ge Bai, Jie Liu, Xingyuan Bu, Yancheng He, Jiaheng Liu, Zhanhui Zhou, Zhuoran Lin, Wenbo Su, Tiezheng Ge, Bo Zheng, Wanli Ouyang

ELLE: Efficient Lifelong Pre-training for Emerging Data (2022.findings-acl)

Challenge:	Large Language Models (LLMs) have greatly enhanced dialogue systems, but evaluation of their capabilities remains a challenge.
Approach:	They propose a model to evaluate the fine-grained abilities of Large Language Models in multi-turn dialogues.
Outcome:	The proposed model evaluates 21 popular chatbots based on MT-Bench-101 . it includes 3 overarching abilities and 13 distinct tasks within multi-turn dialogue scenarios.

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Yujia Qin, Jiajie Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

SciFlow-Bench: Evaluating Structure-Aware Scientific Diagram Generation via Inverse Parsing (2026.acl-long)

Challenge:	Existing pre-trained language models are typically trained with static data, ignoring that streaming data of various sources may continuously grow.
Approach:	They propose to use function preserved model expansion to expand existing PLM's width and depth to improve efficiency of knowledge acquisition.
Outcome:	The proposed model improves pre-training efficiency and performance over existing models on BERT and GPT.

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Tong Zhang, Honglin Lin, Zhou Liu, Chong Chen, Wentao Zhang

FastBERT: a Self-distilling BERT with Adaptive Inference Time (2020.acl-main)

Challenge:	Existing benchmarks for scientific diagram generation rely on image-centric metrics or evaluation of intermediate symbolic representations rather than final rendered images.
Approach:	They propose a structure-first benchmark for evaluating scientific diagram generation from pixel-level outputs.
Outcome:	The proposed benchmark evaluates scientific diagram generation directly from pixel-level outputs.

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Weijie Liu, Peng Zhou, Zhiruo Wang, Zhe Zhao, Haotang Deng, Qi Ju

Exploring and Distilling Multi-Dimensional Clues for Interpretable Social Bot Detection (2026.acl-long)

Challenge:	Pre-trained language models like BERT have proven to be highly performant, but are often computationally expensive in many practical scenarios.
Approach:	They propose a speed-tunable FastBERT with adaptive inference time that can be flexibly adjusted under varying demands.
Outcome:	The proposed model achieves promising results in English and Chinese datasets.

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Yi Han, Haiqi Lu, Lizi Liao, Shuhan Zhou, Yuanxing Liu, Weinan Zhang, Ting Liu

Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability (2025.findings-acl)

Challenge:	Existing research on social bot detection results directly without corresponding supportive explanations, making it difficult to assess the extent to which such predictions are trustworthy.
Approach:	They propose a four-dimensional clue framework that uses outcome-reward reinforcement learning to train inspectors to generate faithful, grounded clues from user information, semantic features, interactive situation, and behavioral pattern.
Outcome:	The proposed framework outperforms baselines in detection performance and significantly improves the performance of large language models.

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Mengliang He, Jiayi Zeng, Yankai Jiang, Wei Zhang, Zeming Liu, Xiaoming Shi, Aimin Zhou

Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos (2021.emnlp-main)

Challenge:	Existing code generation benchmarks neglect flowchart-based code generation . existing benchmarks lack flowcharting-based evaluation, limiting the potential of large language models and minimizing human error.
Approach:	They propose to use flowcharts to evaluate existing LLMs' code generation capabilities.
Outcome:	The proposed benchmarks show that the supervised fine-tuning technique contributes greatly to the models’ performance.

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Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou

STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training (2026.acl-long)

Challenge:	Temporal sentence localization in videos is an important yet challenging task in natural language processing.
Approach:	They propose an Adaptive Proposal Generation Network to maintain the segment-level interaction while speeding up the efficiency.
Outcome:	The proposed model outperforms state-of-the-art methods on three challenging benchmarks.

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Qiuyi Qi, Tian Liang, Mutian Bao, Jinjian Zhang, Dongnan Liu, Wei Zhou, Linjian Mo, Ming Kong, Jie Liu, Feng Zhang, Qiang Zhu

One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment (2020.coling-main)

Challenge:	Prior work has explored step-level supervision using Shannon-entropy-based uncertainty signals, which conflate inherent state complexity with agent confidence.
Approach:	They propose a hierarchical group-based RL framework that leverages normalized entropy to locate outlier steps associated with trajectory neglect and optimizes them via a mechanism of trajectory-aware reward and trajectory-independent penalty.
Outcome:	Experiments on ALFWorld, WebShop, and Search-Augmented QA show that STAPO achieves state-of-the-art performance while substantially alleviating trajectory neglect.

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Tengfei Huo, Zhiqiang Liu, Jinchao Zhang, Jie Zhou

M2Edit: Locate and Edit Multi-Granularity Knowledge in Multimodal Large Language Model (2025.emnlp-main)

Challenge:	Existing methods for automatic comment generation generate common and meaningless comments for music.
Approach:	They propose a multi-perspective strategy to enhance automatic music comment generation by combining different perspectives on a music comment dataset.
Outcome:	The proposed model outperforms state-of-the-art models on two music comment datasets and outperformed existing models by a substantial margin.

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Yang Zhou, Pengfei Cao, Yubo Chen, Qingbin Liu, Dianbo Sui, Xi Chen, Kang Liu, Jun Zhao

Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning (D19-1)

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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Jingjing Wang, Changlong Sun, Shoushan Li, Jiancheng Wang, Luo Si, Min Zhang, Xiaozhong Liu, Guodong Zhou

On-the-fly Denoising for Data Augmentation in Natural Language Understanding (2024.findings-eacl)

Challenge:	Recent neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC) however, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability.
Approach:	They propose a Hierarchical Reinforcement Learning approach to DASC that incorporates clause selection and word selection strategies to tackle the data noise problem.
Outcome:	The proposed approach over the state-of-the-art approaches shows impressive performance over the current baselines.

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Tianqing Fang, Wenxuan Zhou, Fangyu Liu, Hongming Zhang, Yangqiu Song, Muhao Chen

Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically (2021.emnlp-main)

Challenge:	Existing methods to improve data augmentation performance may introduce noisy data that impairs training.
Approach:	They propose an on-the-fly denoising technique that learns from soft augmented labels provided by an organic teacher model trained on the cleaner original dataset.
Outcome:	The proposed method improves on text classification and question-answering tasks on general augmentation techniques and prevents overfitting on noisy labels.

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Xin Liu, Qingcai Chen, Junying Chen, Wenxiu Zhou, Tingyu Liu, Xinlan Yang, Weihua Peng

ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models (2022.findings-emnlp)

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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Jiannan Xiang, Zhengzhong Liu, Yucheng Zhou, Eric Xing, Zhiting Hu

OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)

Challenge:	Existing approaches to data-to-text generation require limited training examples . a data-based approach is based on a set of pre-trained language models with optional finetuning.
Approach:	They propose a data-to-text generation task that makes use of any given (or no) examples.
Outcome:	The proposed approach improves on baselines on a dataset with zero/few/full-shot settings.

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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou

Reasoning Step-by-Step: Temporal Sentence Localization in Videos via Deep Rectification-Modulation Network (2020.coling-main)

Challenge:	Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs.
Approach:	They propose a framework that integrates dialogue, reasoning, and personalized recommendation.
Outcome:	Experiments across public benchmarks show state-of-the-art performance.

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Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou

Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting (2026.acl-short)

Challenge:	Existing methods for temporal sentence localization in videos focus on visual content, but they are insufficient to model complex video contents.
Approach:	They propose a deep rectification-modulation network to correct attention misalignment . they use sentence information to capture frame-to-frame relation .
Outcome:	The proposed method achieves state-of-the-art performance on three public datasets.

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Cheng Wang, Qin Liu, Wenxuan Zhou, Muhao Chen

CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search (2025.naacl-industry)

Challenge:	Excessive exploitation can cause the model to become overconfident in its suboptimal solutions, thereby limiting its capabilities to explore novel reasoning strategies.
Approach:	They propose a method that dynamically down-weights extreme token-level updates via a Gaussian kernel and reduces the instability caused by the trade-off.
Outcome:	The proposed method improves downstream performance across reasoning benchmarks and stabilizes entropy as training progresses.

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Kaixin Wu, Yixin Ji, Zeyuan Chen, Qiang Wang, Cunxiang Wang, Hong Liu, Baijun Ji, Xu Jia, Zhongyi Liu, Jinjie Gu, Yuan Zhou, Linjian Mo

LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)

Challenge:	Relevance modeling between queries and items is a key component of commercial search engines.
Approach:	They propose a framework for continual pre-training of LLMs to enhance domain knowledge . they employ queries and multi-field item to jointly pre-train for enhancing domain knowledge.
Outcome:	The proposed model achieves convincing performance compared to strong baselines.

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Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun

Are LLMs Rational Investors? A Study on the Financial Bias in LLMs (2025.findings-acl)

Challenge:	Existing studies have focused on extending the context length of large language models (LLMs) due to their quadratic computational complexity and a lack of high-quality long training examples, most LLMs are trained with a limited window size.
Approach:	They propose a training-free framework that enables large language models to effectively process long texts using a divide-and-conquer strategy for comprehensive document understanding.
Outcome:	The proposed framework outperforms open-source and commercial long-context LLMs and is compatible with several models.

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Yuhang Zhou, Yuchen Ni, Zhiheng Xi, Zhangyue Yin, Yu He, Gan Yunhui, Xiang Liu, Zhang Jian, Sen Liu, Xipeng Qiu, Yixin Cao, Guangnan Ye, Hongfeng Chai

Situated Embedding Models for Context-Aware Dense Retrieval (2026.acl-short)

Challenge:	Existing studies on biases within specific domains, such as finance, remain limited.
Approach:	They propose a framework to detect, detect, analyze and mitigate financial biases in large language models.
Outcome:	The proposed framework reduces bias by 68% for the most biased model, according to key metrics.

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Junjie Wu, Jiangnan Li, Yuqing Li, Lemao Liu, Liyan Xu, Jiwei Li, Dit-Yan Yeung, Jie Zhou, Mo Yu

Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing (2021.findings-acl)

Challenge:	Existing embedding models are not well-equipped to encode situated context effectively, i.e., situating a chunk’s meaning within its context.
Approach:	They propose to represent short chunks in a way that is conditioned on a broader context window to enhance retrieval performance.
Outcome:	The proposed model outperforms state-of-the-art embedding models on a book-plot retrieval dataset.

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Qian Liu, Dejian Yang, Jiahui Zhang, Jiaqi Guo, Bin Zhou, Jian-Guang Lou

A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment (2020.coling-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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Zhiwen Xie, Runjie Zhu, Kunsong Zhao, Jin Liu, Guangyou Zhou, Jimmy Xiangji Huang

SADA: Bridging In-Context Learning and Fine-Tuning via State-Aligned Distillation Adapters (2026.acl-long)

Challenge:	Existing methods for cross-lingual entity alignment ignore useful pre-aligned links between two KGs.
Approach:	They propose a novel method that jointly learns embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments.
Outcome:	The proposed method achieves remarkable performance gains on three benchmark cross-lingual entity alignment datasets.

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Wenhao Gao, Tianlong Wang, Wei Jia, Linhao Zhang, Aiwei Liu, Miao Fan, Zhou Xiao

GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling (P19-1)

Challenge:	Prompt-based in-context learning and parameter fine-tuning are dominant paradigms for incorporating external information into large language models, but they incur high inference costs or require expensive retraining.
Approach:	They propose to convert prompts into temporary adapter weights to bridge this gap by converting prompts to temporary adapters.
Outcome:	The proposed model outperforms baselines on long-context language modeling and downstream NLU and summarization benchmarks while significantly reducing memory footprint and latency.

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Yijin Liu, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen, Jie Zhou

Chunking

Challenge:	Existing systems for sequence labeling are limited by shallow connections between consecutive hidden states and insufficient modeling of global information.
Approach:	They propose a global context enhanced deep transition architecture for sequence labeling . they deepen the state transition path at each position in a sentence and assign tokens with global representations .
Outcome:	The proposed architecture outperforms the best reported results on two standard sequence labeling tasks.

Learning from Context or Names? An Empirical Study on Neural Relation Extraction (2020.emnlp-main)

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

R3-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL (2024.findings-emnlp)

Challenge:	Existing datasets may leak shallow heuristics via entity mentions, thus contributing to the high performance on RE benchmarks.
Approach:	They propose an entity-masked contrastive framework for relation extraction to gain a deeper understanding on textual context and type information while avoiding rote memorization of entities.
Outcome:	The proposed framework improves the effectiveness and robustness of neural models in different RE scenarios.

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Yuhang Zhou, Yu He, Siyu Tian, Yuchen Ni, Zhangyue Yin, Xiang Liu, Chuanjun Ji, Sen Liu, Xipeng Qiu, Guangnan Ye, Hongfeng Chai

Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency (2022.findings-naacl)

Challenge:	Adapting existing approaches for converting natural language to SQL encounters hurdles due to distinct nature of GQL compared to SQL.
Approach:	They propose a method that integrates both small and large Foundation Models for ranking, rewriting, and refining tasks.
Outcome:	The proposed approach integrates both small and large Foundation Models for ranking, rewriting, and refining tasks while capitalizing on the superior generalization and query generation prowess of larger models for the final transformation of natural language queries into GQL formats.

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Jin Liu, Chongfeng Fan, Zhou Fengyu, Huijuan Xu

Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA (2022.findings-emnlp)

Challenge:	Existing knowledge graph-to-text generation methods focus on sequence-to sequence generation, but the linearized order of KG is obtained through a heuristic search without data-driven optimization.
Approach:	They propose to generate easy-to-understand sentences from the knowledge graph . they incorporate part-of-speech syntactic tags to constrain the positions to copy words from the KG and employ a semantic context scoring function to evaluate the semantic fitness for each word in its local context.
Outcome:	The proposed method achieves state-of-the-art on two datasets, WebNLG and DART, and achieves high consistency.

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Qingyi Si, Fandong Meng, Mingyu Zheng, Zheng Lin, Yuanxin Liu, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou

Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval (2025.acl-long)

Challenge:	Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution.
Approach:	They propose a dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.
Outcome:	The proposed dataset considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.

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Zheng Liu, Ze Liu, Zhengyang Liang, Junjie Zhou, Shitao Xiao, Chao Gao, Chen Jason Zhang, Defu Lian

Exploration-Exploitation Reshaping towards Efficient Reasoning for Large Language Models (2026.findings-acl)

Challenge:	Existing multimodal retrieval models are lacking in visual representations of multimodal data.
Approach:	They propose a visualized information retrieval paradigm where multimodal information is represented by a unified visual format called Screenshots for various retrieval applications.
Outcome:	The proposed model is based on a large dataset of screenshots from diverse sources . it is compared with existing models and lays a solid foundation for the new model .

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Yufeng Shi, Weilin Luo, Yuxiang Zhang, Zongmeng Zhang, Haoyang Liu, Yubing Wang, Bin Wang, Wengang Zhou, Houqiang Li

LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence (2026.findings-acl)

Challenge:	Large Reasoning Models (LRMs) are constrained by the overthinking issue.
Approach:	They propose a policy optimization framework that reshapes the exploration and exploitation through two core components: self-imitation and self-guidance exploration.
Outcome:	The proposed model achieves superior reasoning efficiency without compromising overall accuracy.

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Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Shirui Pan, Erik Cambria

VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)

Challenge:	Existing benchmarks for legal general intelligence (GI) are result-oriented and do not evaluate the legal intelligence of large language models (LLMs).
Approach:	They propose a Chinese legal benchmark for evaluating legal GI in large language models . they use recent legal cases and exam questions to create multiple-choice questions .
Outcome:	The proposed benchmarks lack a systematic evaluation of the legal intelligence of large language models (LLMs) the results show that even the best LLMs lagging behind human legal professionals.

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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

DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing (2026.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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Qian Cao, Yahui Liu, Wei Bi, Yi Zhao, Ruihua Song, Xiting Wang, Ruiming Tang, Guorui Zhou, Han Li

Challenge:	Existing methods for enhancing large language models (LLMs) lack explicit mechanisms for guiding diverse exploration and instead prioritize efficiency and performance over diversity.
Approach:	They propose a reinforcement learning-based framework that decomposes the generation process into explicitly planned intermediate steps and introduces divergence at the planning phase based on diversity variation.
Outcome:	The proposed method significantly outperforms existing baselines on creative writing benchmarks on a semi-structured long chain-of-thought (CoT) it introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories.

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang

DavIR: Data Selection via Implicit Reward for Large Language Models (2025.acl-long)

Challenge:	Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction.
Approach:	They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack.
Outcome:	The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses.

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Haotian Zhou, Tingkai Liu, Qianli Ma, Yufeng Zhang, Jianbo Yuan, Pengfei Liu, Yang You, Hongxia Yang

Transferring General Multimodal Pretrained Models to Text Recognition (2023.findings-acl)

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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Junyang Lin, Xuancheng Ren, Yichang Zhang, Gao Liu, Peng Wang, An Yang, Chang Zhou

Safety Alignment via Constrained Knowledge Unlearning (2025.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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Zesheng Shi, Yucheng Zhou, Jing Li, Yuxin Jin, Yu Li, Daojing He, Fangming Liu, Saleh Alharbi, Jun Yu, Min Zhang

I run as fast as a rabbit, can you? A Multilingual Simile Dialogues Datasets (2023.findings-acl)

Challenge:	Existing defense mechanisms have not fully deleted harmful knowledge in large language models (LLMs) Existing methods to address safety alignment have not completely deleted harmful information in LLMs.
Approach:	They propose a safety alignment strategy that uses scoring neurons to identify useful knowledge in LLMs and pruning the gradients of neurons in U to preserve beneficial information.
Outcome:	The proposed method significantly improves model safety while maintaining utility compared to existing methods.

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Longxuan Ma, Wei-Nan Zhang, Shuhan Zhou, Churui Sun, Changxin Ke, Ting Liu

MedThink: A Rationale-Guided Framework for Explaining Medical Visual Question Answering (2025.findings-naacl)

Challenge:	A simile is a figure of speech that compares two different things via shared properties.
Approach:	They propose a multilingual simile dialogue dataset that can be used to study similes in real-life scenarios.
Outcome:	The proposed dataset is the largest manually annotated simile dataset and contains both English and Chinese data.

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Xiaotang Gai, Chenyi Zhou, Jiaxiang Liu, Yang Feng, Jian Wu, Zuozhu Liu

MSMO: Multimodal Summarization with Multimodal Output (D18-1)

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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Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang, Chengqing Zong

Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models (2023.acl-long)

Challenge:	Existing studies show that multimodal summarization can improve user satisfaction for informativeness of summaries by using information in visual modality.
Approach:	They propose a task to generate text and select the most relevant image from the multimodal input and a novel multimodal automatic evaluation method to evaluate multimodal outputs.
Outcome:	The proposed method improves user satisfaction by 12.4% compared to the current system .

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Qinhong Zhou, Zonghan Yang, Peng Li, Yang Liu

G3R: A Graph-Guided Generate-and-Rerank Framework for Complex and Cross-domain Text-to-SQL Generation (2023.findings-acl)

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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Yanzheng Xiang, Qian-Wen Zhang, Xu Zhang, Zejie Liu, Yunbo Cao, Deyu Zhou

A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification (N19-1)

Challenge:	Existing approaches to complex and cross-domain Text-to-SQL generation lack domain knowledge . domain knowledge is not incorporated to enhance their ability to generalise to unseen databases.
Approach:	They propose a framework called G3R for complex and cross-domain Text-to-SQL generation . they propose re-ranking SQL queries based on domain knowledge and a graph-guided SQL generator .
Outcome:	The proposed framework achieves state-of-the-art results on the Spider and Spider-DK benchmarks.

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Ziqian Zeng, Wenxuan Zhou, Xin Liu, Yangqiu Song

GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation (2025.findings-acl)

Challenge:	Existing weakly supervised methods for document-level multi-aspect sentiment classification are not easy to obtain.
Approach:	They propose a variational approach to weakly supervised document-level multi-aspect sentiment classification using target-opinion word pairs as "supervision" they aim to learn a sentiment polarity classifier by optimizing the lower bound .
Outcome:	The proposed method outperforms weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to state-of-the-art supervised methods with hundreds of labels per aspect.

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Jie He, Jennifer Neville, Mengting Wan, Longqi Yang, Hui Liu, Xiaofeng Xu, Xia Song, Jeff Z. Pan, Pei Zhou

CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models (2026.findings-acl)

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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Zhenhong Zhou, Zherui Li, Jie Zhang, Yuanhe Zhang, Kun Wang, Yang Liu, Qing Guo

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)

Challenge:	Existing security models rely on open-ended communication, but the collaborative process itself can be exploited and disrupted.
Approach:	They propose a new threat class, called Denial-of-Collaboration, which corrupts collaborative structure and transforms communication topology into self-sabotage.
Outcome:	The proposed attacks bypass conventional safety alignments that are not designed to detect behavioral or systemic attacks.

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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen

AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis (2022.coling-1)

Challenge:	Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance.
Approach:	They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents.
Outcome:	The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain.

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Ziming Li, Yan Zhou, Weibo Zhang, Yaxin Liu, Chuanpeng Yang, Zheng Lian, Songlin Hu

Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer (2020.acl-main)

Challenge:	Existing methods treat three modal features equally, without distinguishing the importance of different modalities. Existing models split the video into frames, leading to missing the global acoustic information.
Approach:	They propose a global Acoustic feature enhanced Modal-Order-Aware network to address these problems.
Outcome:	The proposed model outperforms state-of-the-art models on two public datasets.

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Chulun Zhou, Liangyu Chen, Jiachen Liu, Xinyan Xiao, Jinsong Su, Sheng Guo, Hua Wu

Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking (2026.findings-acl)

Challenge:	Existing methods to unsupervised style transfer lack fine-grained control of the influence from the target style.
Approach:	They propose a model that exploits the relevance of each output word to the target style . they pretrain a style classifier and train an attentional Seq2seq model to reconstruct input sentences .
Outcome:	The proposed model achieves state-of-the-art performance in terms of transfer accuracy and content preservation.

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Songbo Hu, Yinhong Liu, Ej Zhou, Evgeniia Razumovskaia, Xiaobin Wang, Alexander Fraser, Ivan Vulić, Anna Korhonen

Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation (2026.findings-acl)

Challenge:	Creating spoken dialogue datasets is methodologically challenging due to the personally identifiable nature of speech signals.
Approach:	They propose a large-scale, multilingual, and multi-parallel dataset for developing and evaluating retrieval-augmented generation-based spoken dialogue systems.
Outcome:	The proposed dataset includes 6,000 information-seeking dialogues and 163 hours of user speech recorded from native speakers of four official WHO languages.

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Hanqi Jiang, Junhao Chen, Yi Pan, Ling Chen, Weihang You, Yifan Zhou, Ruidong Zhang, Yohannes Abate, Tianming Liu

Wav2SQL: Direct Generalizable Speech-To-SQL Parsing (2024.findings-acl)

Challenge:	Large Language Models excel at generalized reasoning, but lack the ability to accumulate experiences and maintain narrative coherence over long horizons.
Approach:	They propose a unified memory architecture that transcends static vector similarity.
Outcome:	The proposed model outperforms state-of-the-art methods in temporal and multihop reasoning tasks.

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Huadai Liu, Rongjie Huang, Jinzheng He, Gang Sun, Ran Shen, Xize Cheng, Zhou Zhao

ContextCheck: Sentence-Level Faithfulness Verification with Context-Aware Disambiguation (2026.findings-acl)

Challenge:	Existing models for speech-driven SQL parsing are based on a cascaded approach, resulting in data scarcity and inconsistent performance.
Approach:	They propose a direct generalizable speech-to-SQL parsing model which avoids error compounding across cascaded systems.
Outcome:	The proposed model avoids error compounding and achieves state-of-the-art results by 4.7% improvement over baseline.

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Yueqin Yin, Yaxi Li, Xin Liu, Xun Wang, Kaiqiang Song, Simin Ma, Shujian Liu, Sathish Reddy Indurthi, Haoyun Deng, Pengcheng He, Mingyuan Zhou, Song Wang

Challenge:	Large language models often hallucinate, producing content that is factually incorrect or not grounded in the sources.
Approach:	They propose a framework for sentence-level faithfulness verification with context-aware disambiguation.
Outcome:	The proposed framework improves Macro F1 by over 10 points compared to baselines on three context-dependent datasets.

Re3: Relevance & Recency Retrieval for Mitigating Temporal Hallucination (2026.acl-long)

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Jiawei Cao, Jie Ouyang, Mingyue Cheng, Zhaomeng Zhou, Chunli Liu, Yupeng Li, Zirui Liu, Shijin Wang

Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models (2024.acl-long)

Challenge:	Existing retrievers suffer from temporal-semantic misalignment and outdated-document interference . Existing frameworks suffer from both temporal validity and outdated factual versions .
Approach:	They propose a framework that mitigates temporal hallucinations by embedding heterogeneous temporal signals into the semantic space to ensure retrieval fidelity.
Outcome:	Experiments show that Re3 outperforms baselines by 9.7% in generation accuracy . the framework outperformed strongest baselines on challenging dynamic tasks .

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Yuchong Sun, Che Liu, Kun Zhou, Jinwen Huang, Ruihua Song, Xin Zhao, Fuzheng Zhang, Di Zhang, Kun Gai

SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation (2026.acl-long)

Challenge:	Existing studies overlook the multi-turn instruction following ability of large language models (LLMs) Extensive experiments show that Parrot improves current LLMs by up to 7.2% in multi- turn instruction following.
Approach:	They propose a method for collecting multi-turn instructions that feature human-like queries, such as anaphora and ellipsis, and a context-aware preference optimization strategy to further enhance LLMs for complex queries.
Outcome:	The proposed method improves existing LLMs by up to 7.2% in multi-turn instruction following.

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Hui Wang, Jinghua Zhao, Yifan Yang, Shujie Liu, Junyang Chen, Yanzhe Zhang, Shiwan Zhao, Jinyu Li, Jiaming Zhou, Haoqin Sun, Yan Lu, Yong Qin

CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels (2026.acl-long)

Challenge:	Existing methods for evaluating the perceptual quality of synthetic speech are limited due to the complexity of perceptual quality factors and the diversity of speech generation tasks.
Approach:	They propose a new paradigm for enabling large language models to conduct structured speech quality evaluation using a large-scale dataset.
Outcome:	The proposed model performs well across tasks and languages.

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Xing Ma, Yangjie Zhou, Wu Sun, Zihan Liu, Jingwen Leng, Yun Lin, Shixuan Sun, Minyi Guo, Jin Song Dong

QUEST: Efficient Extreme Multi-Label Text Classification with Large Language Models on Commodity Hardware (2024.findings-emnlp)

Challenge:	Existing approaches to support diverse attention variants trade performance for flexibility . expert-written kernels achieve high efficiency but are difficult to adapt .
Approach:	They propose a framework that adapts expert-written attention kernels to GPUs . they use a structured lift–transfer–lower workflow to make execution explicit .
Outcome:	The proposed framework outperforms existing frameworks and compilers on diverse variants and GPU platforms.

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Chuang Zhou, Junnan Dong, Xiao Huang, Zirui Liu, Kaixiong Zhou, Zhaozhuo Xu

The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking (2022.findings-acl)

Challenge:	Extreme multi-label text classification (EMTC) involves predicting multiple labels from a vast pool of candidates based on a user’s textual query.
Approach:	They propose a Quantized and Efficient Learning with Sampling Technique that uses a hash sampling module to reduce the data volume to one-fourth of its original size.
Outcome:	Extensive experiments show that QUEST outperforms existing methods while requiring fewer computational resources.

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Yinghui Li, Qingyu Zhou, Yangning Li, Zhongli Li, Ruiyang Liu, Rongyi Sun, Zizhen Wang, Chao Li, Yunbo Cao, Hai-Tao Zheng

DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)

Challenge:	Chinese Spell Checking (CSC) aims to detect and correct spelling errors, which are caused by the phonological or visual similarity.
Approach:	They propose an Error-driven COntrastive Probability Optimization framework to refine the knowledge representations of pre-trained language models to avoid predicting common characters.
Outcome:	Extensive experiments and detailed analyses on SIGHAN datasets demonstrate that ECOPO is simple yet effective.

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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun

Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space (2020.emnlp-main)

Challenge:	Existing relation extraction methods focus on extracting intra-sentence relations for single entities.
Approach:	They propose a relation extraction dataset from Wikipedia and Wikidata with three features . document-level relation extraction is a task to identify relational facts between entities .
Outcome:	The proposed dataset is the largest human-annotated dataset for document-level RE from plain text.

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Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Jiancheng Lv, Nan Duan, Ming Zhou

Crabs: Consuming Resource via Auto-generation for LLM-DoS Attack under Black-box Settings (2025.findings-acl)

Challenge:	Existing data augmentation techniques for natural language processing tasks are difficult to design.
Approach:	They propose a controllable rewriting based question data augmentation method for machine reading comprehension, question generation and question-answering natural language inference tasks.
Outcome:	The proposed method generates high-quality, high-quality question data samples on machine reading comprehension, question generation, and question-answering natural language inference tasks.

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Yuanhe Zhang, Zhenhong Zhou, Wei Zhang, Xinyue Wang, Xiaojun Jia, Yang Liu, Sen Su

Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation (2025.emnlp-main)

Challenge:	Existing studies on white-box attacks focus on black-box LLMs, leaving black- box scenarios underexplored.
Approach:	They propose an automated algorithm designed for black-box LLMs that constructs the DoS Attack Tree and expands the node coverage to achieve effectiveness under black- box conditions.
Outcome:	The proposed algorithm can be used to build a DoS Attack Tree and expand the node coverage to achieve effectiveness under black-box conditions.

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Haijian Ma, Daizong Liu, Xiaowen Cai, Pan Zhou, Yulai Xie