Papers by Shi Wang

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

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

Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs (2026.acl-long)

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

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Wu Li, Yigeng Zhou, Zesheng Shi, Yequan Wang, Min Zhang, Jing Li

UniVocal: Unified Speech-Singing Code-Switching Synthesis (2026.acl-long)

Challenge:	Recent self-training approaches have reduced reliance on human-labeled data, which limits their scalability.
Approach:	They propose a team-based self-play algorithm that iteratively refines alignment without additional human supervision.
Outcome:	The proposed algorithm outperforms baselines and LLM benchmarks in the self-supervised setting.

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YuFei Shi, Qian Chen, Wen Wang, Xiangang Li, Zhen-Hua Ling, Yang Ai

Speech & Audio in NLP

Challenge:	Existing systems cannot automatically determine when to switch between modes based on text content.
Approach:	They propose a unified framework that implicitly infers vocal modes from text context to pioneer SCS Synthesis.
Outcome:	The proposed framework infers vocal modes solely from text context to pioneer SCS Synthesis.

Multiview Clickbait Detection via Jointly Modeling Subjective and Objective Preference (2023.findings-emnlp)

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Chongyang Shi, Yijun Yin, Qi Zhang, Liang Xiao, Usman Naseem, Shoujin Wang, Liang Hu

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

Challenge:	Existing clickbait detection models rely on analyzing the objective semantics of posts or correlating posts with article content only, but fail to identify and exploit the manipulation intention of clickbaiting from a user’s subjective perspective.
Approach:	They propose a multiview clickbait detection model to model subjective and objective preferences simultaneously to capture clickbaiting from a user's subjective perspective.
Outcome:	The proposed model outperforms state-of-the-art models on two real-world datasets and shows that it integrates subjective and objective preferences simultaneously.

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

Topic-Aware Neural Keyphrase Generation for Social Media Language (P19-1)

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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Yue Wang, Jing Li, Hou Pong Chan, Irwin King, Michael R. Lyu, Shuming Shi

K-order Ranking Preference Optimization for Large Language Models (2025.findings-acl)

Challenge:	Existing methods to extract words from source posts to form keyphrases do not exploit latent topics.
Approach:	They propose a sequence-to-sequence-based neural keyphrase generation framework . it allows absent keyphrases to be created, and it allows joint modeling of latent topic representations .
Outcome:	The proposed model outperforms extraction and generation models without exploiting latent topics.

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Shihao Cai, Chongming Gao, Yang Zhang, Wentao Shi, Jizhi Zhang, Keqin Bao, Qifan Wang, Fuli Feng

A Survey on Zero Pronoun Translation (2023.acl-long)

Challenge:	Existing list-wise methods focus on optimizing list ranking consistency for LLMs to improve ranking abilities.
Approach:	They propose to extend the Plackett-Luce model to accommodate top-K ranking by extending the DPO’s Plact-Lucer model to dynamically determine appropriate K for different samples.
Outcome:	The proposed model can be extended to accommodate top-K ranking and improve training efficiency.

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Longyue Wang, Siyou Liu, Mingzhou Xu, Linfeng Song, Shuming Shi, Zhaopeng Tu

MaPPER: Multimodal Prior-guided Parameter Efficient Tuning for Referring Expression Comprehension (2024.emnlp-main)

Challenge:	Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language.
Approach:	They propose to analyze the literature on zero pronoun translation after the neural revolution . they uncover that data limitation causes learning bias in languages and domains .
Outcome:	The proposed method and methods are compared to other models and evaluation metrics on different benchmarks.

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Ting Liu, Zunnan Xu, Yue Hu, Liangtao Shi, Zhiqiang Wang, Quanjun Yin

TIGER: A Unified Generative Model Framework for Multimodal Dialogue Response Generation (2024.lrec-main)

Challenge:	Existing methods for Referring Expression Comprehension (REC) lack specific domain abilities for precise local visual perception and visual-language alignment.
Approach:	They propose a framework for Parameter-Efficient Transfer Learning to localize a visual region via natural language using a prior-guided prior.
Outcome:	The proposed framework achieves the best accuracy compared to the current methods with only 1.41% tunable backbone parameters.

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Fanheng Kong, Peidong Wang, Shi Feng, Daling Wang, Yifei Zhang

All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG (2026.acl-long)

Challenge:	Existing research on multimodal dialogues focuses on textual response generation and visual response selection based on the dialogue context.
Approach:	They propose a generative model framework for multimodal dialogue response generation that ground the conversation on an image.
Outcome:	The proposed system provides users with an enhanced conversational experience.

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Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang, Bo Zheng, Boxi Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun

MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs (2024.acl-long)

Challenge:	Existing mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language.
Approach:	They propose a language-agnostic utility-driven reranker alignment technique to mitigate language bias during re-ranking.
Outcome:	The proposed approach mitigates language bias and consistently improves mRAG performance across languages.

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Zimu Lu, Aojun Zhou, Houxing Ren, Ke Wang, Weikang Shi, Junting Pan, Mingjie Zhan, Hongsheng Li

Removing Prompt-template Bias in Reinforcement Learning from Human Feedback (2025.findings-acl)

Challenge:	Existing models have demonstrated outstanding capabilities in mathematical reasoning, but there is a performance gap between open-source models and closed-source ones.
Approach:	They propose a method for generating diverse and reliable math problems by leveraging the ground-truth solutions of the seed data.
Outcome:	The proposed model outperforms open-source models across five representative mathematical reasoning datasets.

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Chaojie Wang, Haonan Shi, Long Tian, Bo An, Shuicheng Yan

Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation (2026.acl-long)

Challenge:	Reinforcement Learning from Human Feedback (RLHF) has shown promise for enhancing pre-trained large language models to generate responses that align with human preferences and societal values.
Approach:	They propose a method to estimate prompt-template bias term during reward modeling and use it to calibrate reward scores.
Outcome:	The proposed method can be flexibly combined with existing algorithms of removing length bias, leading to a further improvement in the aspect of enhancing the quality of generated responses.

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Minjing Shi, Junling Wang, Jingwei Ni, Sankalan Pal Chowdhury, Mrinmaya Sachan

Retrieval Models Aren’t Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models (2025.findings-acl)

Challenge:	Existing systems that detect logical fallacies in public discourse do not help people recognize them independently.
Approach:	They propose an intelligent tutoring system which uses large language models to help humans learn about logical fallacies.
Outcome:	The proposed system outperforms baseline LLMs lacking such pedagogical strategies.

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Zhengliang Shi, Yuhan Wang, Lingyong Yan, Pengjie Ren, Shuaiqiang Wang, Dawei Yin, Zhaochun Ren

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

Challenge:	Large language models (LLMs) suffer from inherent inabilities to interact with the physical world and access vast, up-to-date knowledge.
Approach:	They propose a tool retrieval benchmark for large language models (LLMs) that includes 7.6k diverse retrieval tasks and a corpus of 43k tools.
Outcome:	The proposed model performs poorly on the heterogeneous tool retrieval benchmark, resulting in low pass rate and low retrieval quality.

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

TR-Rules: Rule-based Model for Link Forecasting on Temporal Knowledge Graph Considering Temporal Redundancy (2023.findings-emnlp)

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

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Ningyuan Li, Haihong E, Shi Li, Mingzhi Sun, Tianyu Yao, Meina Song, Yong Wang, Haoran Luo

Direct Multi-Turn Preference Optimization for Language Agents (2024.emnlp-main)

Challenge:	Existing models suffer from temporal redundancy when leveraged under dynamic settings.
Approach:	They propose a temporal knowledge graph extrapolation method which solves temporal redundancy issues by using cyclic rules to capture more information lurking in TKGs.
Outcome:	The proposed model captures more information lurking in TKGs, and also mines and properly leverages acyclic rules, which has not been explored by existing models.

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Wentao Shi, Mengqi Yuan, Junkang Wu, Qifan Wang, Fuli Feng

Challenge:	Extensive experiments on three multi-turn agent task datasets confirm the effectiveness and superiority of the DMPO loss function.
Approach:	They propose a novel loss function for multi-turn agent tasks that replaces the policy constraint with the state-action occupancy measure constraint and adds length normalization to the Bradley-Terry model.
Outcome:	Experiments on three multi-turn agent task datasets confirm the effectiveness and superiority of the proposed loss function.

SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator (2026.acl-long)

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Xueyang Zhou, Weidong Wang, Lin Lu, Jiawen Shi, Guiyao Tie, Xu Yongtian, Lixing Chen, Pan Zhou, Neil Zhenqiang Gong, Lichao Sun

Challenge:	SafeAgent improves agent safety through fully automated synthetic data generation.
Approach:	They propose a framework that improves agent safety through fully automated synthetic data generation.
Outcome:	The proposed framework outperforms closed-source models on two safety benchmarks and one real-world task.

v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound (2026.acl-long)

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Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou, Yuxuan Wang, Qinrong Cui, Wei Bi, Song-Chun Zhu, Bo Zhao, Zilong Zheng

Breaking Down Power Barriers in On-Device Streaming ASR: Insights and Solutions (2025.naacl-industry)

Challenge:	Humor enriches our daily lives and appears in many forms, from jokes and cartoons to comedies and viral videos.
Approach:	They introduce a video humor understanding benchmark to test their ability to understand humor from visual cues.
Outcome:	The proposed video humor understanding benchmark is based on a collection of short videos . it features rich annotations and a study of environmental sound that can enhance humor .

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Yang Li, Yuan Shangguan, Yuhao Wang, Liangzhen Lai, Ernie Chang, Changsheng Zhao, Yangyang Shi, Vikas Chandra

ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback (2023.findings-emnlp)

Challenge:	Streaming automatic speech recognition models use high power consumption to improve usability and accuracy.
Approach:	They propose to optimize on-device speech recognition models by adjusting component energy sensitivities based on their specific energy sensitities to reduce power consumption.
Outcome:	The proposed approach achieves up to 47% lower energy usage while preserving comparable model accuracy and improving real-time performance compared to leading methods.

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Wenxiang Jiao, Jen-tse Huang, Wenxuan Wang, Zhiwei He, Tian Liang, Xing Wang, Shuming Shi, Zhaopeng Tu

STICKERCONV: Generating Multimodal Empathetic Responses from Scratch (2024.acl-long)

Challenge:	Large language models (LLMs) like ChatGPT are only accessible through restricted APIs, which creates barriers to new research and advancements in the field.
Approach:	They propose a framework to enhance and regulate the translation abilities during chat . they reformulate translation data into the instruction-following style and introduce a "Hint" field .
Outcome:	The proposed framework enhances and regulates the translation abilities during chat . it reformulates translation data into the instruction-following style and introduces a "Hint" field .

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Yiqun Zhang, Fanheng Kong, Peidong Wang, Shuang Sun, SWangLing SWangLing, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song

Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation (2026.findings-acl)

Challenge:	Prior studies on stickers focused on sentiment analysis and recommendation systems, overlooking their vast potential in empathetic response generation.
Approach:	They propose a multimodal empathetic dialogue dataset, STICKERCONV, which simulates human behavior with stickers, and propose evaluative metrics based on LLM.
Outcome:	The proposed framework generates contextually relevant and emotionally resonant multimodal empathetic responses, contributing to the advancement of more nuanced and engaging e-dialog systems.

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Haonan Shangguan, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, Feiliang Ren, Ge Yu

RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)

Challenge:	Current approaches for Multimodal Sentiment Analysis (MSA) rely on parameter-heavy LLMs for classification, overlooking multimodal sentiment reasoning generation in resource-limited environments.
Approach:	They propose a multimodal sentiment reasoning distillation model that employs a teacher-assistant-student paradigm to address deployment constraints in resource-limited environments.
Outcome:	The proposed model performs well on a resource-limited JMSRC task with only 3B parameters and shows generalization and interpretability.

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Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models (2022.findings-aacl)

Challenge:	Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy.
Approach:	They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses .
Outcome:	The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.

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Yizhi Li, Ge Zhang, Bohao Yang, Chenghua Lin, Anton Ragni, Shi Wang, Jie Fu

LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient (2026.acl-long)

Challenge:	Existing methods do not examine social groups categorised by geographical information, leaving the region-related biases in pre-trained LMs unexplored.
Approach:	They propose a hierarchical regional bias evaluation method to quantify regional bias in pre-trained language models.
Outcome:	The proposed method evaluates regional bias with regard to comprehensive topics and measures potential regional bias that can be propagated to downstream tasks.

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Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Jiayi Shi, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li

RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models (2025.findings-emnlp)

Challenge:	Using generic and efficient benchmark generators, human annotators are limited by inefficiency . current benchmark generator methods rely on seed signals, leading to long cycles and high costs .
Approach:	They propose a framework to evaluate LLMs as generic benchmark generators and integrate them as BenchMaker.
Outcome:	The proposed framework achieves comparable performance to human-annotated benchmarks on most metrics.

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Jingjing Liu, Zeming Liu, Zihao Cheng, Mengliang He, Xiaoming Shi, Yuhang Guo, Xiangrong Zhu, Yuanfang Guo, Yunhong Wang, Haifeng Wang

Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning (D18-1)

Challenge:	Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair.
Approach:	They propose a repository-level code debugging dataset with 22 subtypes of errors that supports 8 commonly used programming languages and 3 debug tasks.
Outcome:	The proposed dataset supports 8 commonly used programming languages and 3 debugging tasks.

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Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Video-Text Retrieval by Supervised Sparse Multi-Grained Learning (2023.findings-emnlp)

Challenge:	Existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning.
Approach:	They propose to use user-attention-based Convolutional Neural Networks to capture individuality and opinion bias in microblog posts and a novel adversarial cross-lingual learning framework to enrich the user post representation.
Outcome:	The proposed method outperforms state-of-the-art baseline algorithms with large margins on English and Chinese microblog datasets.

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Yimu Wang, Peng Shi

ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue (2026.findings-acl)

Challenge:	Recent advances in video-text retrieval have led to improved representation learning methods.
Approach:	They propose a multi-grained sparse learning framework to learn an aligned sparsen space shared between video and text for video-text retrieval.
Outcome:	The proposed framework is superior to existing methods on video-text retrieval benchmarks.

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Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, Jiajie Xu

Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons (D19-1)

Challenge:	Existing approaches to multi-turn dialogues lack contextual consistency and dependencies, and models struggle to maintain factual faithfulness as interaction turns increase.
Approach:	They propose an adaptive context refactoring framework that monitors and reshapes the interaction history to mitigate contextual inertia and state drift.
Outcome:	The proposed model outperforms baselines while reducing token consumption.

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Jie Hao, Xing Wang, Shuming Shi, Jinfeng Zhang, Zhaopeng Tu

MicroC-KT: Modeling Community Effect via Learning Micro-Environment for Evidence-Grounded Explainable Knowledge Tracing (2026.acl-long)

Challenge:	Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work.
Approach:	They propose to use an advanced variant of self-attention networks (SANs) to enhance the strength of hybrid models by introducing a syntax-oriented inductive bias to perform tree-like composition.
Outcome:	The proposed model outperforms both individual models and a standard hybrid model on a machine translation task.

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Zhiyi Duan, Zixing Shi, Bing Jia, Qi Wang

Large Language Models in Bioinformatics: A Survey (2025.findings-acl)

Challenge:	Existing graph-based methods focus on exercise-concept relations, but lack the broader context of group references and contrastive evidence.
Approach:	They propose a framework that incorporates learning micro-environments to provide social-cognitive anchors for KT by extracting contrastive group evidence.
Outcome:	The proposed framework outperforms state-of-the-art models on four public datasets while providing more reliable and evidence-based explanations.

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Zhenyu Wang, Zikang Wang, Jiyue Jiang, Pengan Chen, Xiangyu Shi, Yu Li

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators (2025.findings-acl)

Challenge:	Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data.
Approach:	They examine the evolution of Large Language Models (LLMs) in bioinformatics and precision medicine by focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics.
Outcome:	The proposed models are capable of predicting RNA structure and function and predicting single-cell transcriptomics.

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Jianling Li, ShangZhan Li, Zhenye Gao, Qi Shi, Yuxuan Li, Zefan Wang, Jiacheng Huang, WangHaojie WangHaojie, Jianrong Wang, Xu Han, Zhiyuan Liu, Maosong Sun

Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework (D19-1)

Challenge:	Triton is a high-level Python-like programming language for building efficient GPU kernels.
Approach:	They propose a TritonBench benchmark that provides a comprehensive evaluation of Tritonic operators on widely deployed GPUs.
Outcome:	The proposed benchmarks show that current LLMs struggle to generate efficient Triton operators on widely deployed GPUs aligned with industry applications.

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Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Shuming Shi

Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems (2025.findings-emnlp)

Challenge:	generative models for end-to-end sequence generation have been shown promising for this task . however, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator is still challenging.
Approach:	They propose a framework where skeleton extraction is made by an interpretable matching model and a retrieval-guided response generator is followed by a separate generator.
Outcome:	The proposed framework outperforms baseline models in a variety of experiments.

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Minghang Zhu, Zhengliang Shi, Zhiwei Xu, Shiguang Wu, Lingjie Wang, Pengjie Ren, Zhaochun Ren, Zhumin Chen

Challenge:	Existing methods for fine-tuning agents are often inadequate . a multi-agent system can solve complex tasks by dividing responsibilities among specialized agents .
Approach:	a new framework is proposed to improve agents collaboration through iterative alignment.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on held-in and held-out tasks.

From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models (2026.acl-long)

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Ling Shi, Xinwei Wu, Xiaohu Zhao, Hao Wang, Heng Liu, Yangyang Liu, Linlong Xu, Longyue Wang, Deyi Xiong, Weihua Luo

TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation (2025.findings-emnlp)

Challenge:	Recent research in mechanistic interpretability has revealed that Large Language models contain disentangled, human-understandable components.
Approach:	They propose a framework that first identifies causal task features through frequency recall and interventional filtering, then selects “Feature-Resonant Data” that maximally activates task features for fine-tuning.
Outcome:	The proposed framework outperforms existing models on mathematical reasoning, summarization, and translation tasks while using only 50% of the data.

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Chengrui Huang, Shen Gao, Zhengliang Shi, Dongsheng Wang, Shuo Shang

SafetyQuizzer: Timely and Dynamic Evaluation on the Safety of LLMs (2025.naacl-long)

Challenge:	Existing tool-learning methods often overlook fine-grained optimization of internal tool call details.
Approach:	They propose a training paradigm for constructing token-level tool-use preference datasets . reversed dataset construction is a method for creating high-quality, multi-turn tool-user datasets by reversing the generation flow.
Outcome:	a new training paradigm improves tool-using performance and generalizes results.

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Zhichao Shi, Shaoling Jing, Yi Cheng, Hao Zhang, Yuanzhuo Wang, Jie Zhang, Huawei Shen, Xueqi Cheng

MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation (2022.coling-1)

Challenge:	Large Language Models (LLMs) have been used to evaluate the safety of their users . however, evaluation questions in current benchmarks are too straightforward and difficult to update with practical relevance due to their lack of correlation with real-world events.
Approach:	They propose a question-generation framework to evaluate the safety of LLMs in the Chinese context.
Outcome:	The proposed framework reduces decline rate while maintaining similar attack success rate.

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Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang

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

Challenge:	Existing zero-shot dialogue generation systems rely on large-scale pre-trained language models.
Approach:	They propose a multilingual learning framework for zero-shot dialogue generation that can transfer knowledge from an English corpus to a non-English corpus with zero samples.
Outcome:	The proposed framework can transfer knowledge from an English corpus to a non-English corpus with zero samples.

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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

Spiral of Silence in Large Language Model Agents (2025.findings-emnlp)

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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Mingze Zhong, Meng Fang, Zijing Shi, Yuxuan Huang, Shunfeng Zheng, Yali Du, Ling Chen, Jun Wang

MedAdapter: Efficient Test-Time Adaptation of Large Language Models Towards Medical Reasoning (2024.emnlp-main)

Challenge:	Existing theories of Spiral of Silence do not apply to large language models .
Approach:	They propose an evaluation framework for examining SoS in large language models . they consider four controlled conditions that vary the availability of "History" and "Persona" signals .
Outcome:	The proposed framework examines the SoS-like dynamics in large language models . it shows that history and persona together produce strong majority dominance .

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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Haotian Sun, Hang Wu, Carl Yang, May Dongmei Wang

EventRAG: Enhancing LLM Generation with Event Knowledge Graphs (2025.acl-long)

Challenge:	Large language models (LLMs) have improved generation and reasoning capabilities compared to traditional BERT-sized models due to massive number of parameters and extensive pre-training on vast textual corpora.
Approach:	They propose a unified post-hoc adapter for test-time adaptation of large language models . they propose to fine-tune only a small BERT-sized adapter to rank candidate LLMs .
Outcome:	The proposed adapter improves performance on four biomedical tasks without requiring computational resources or sharing data with third parties.

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Zairun Yang, Yilin Wang, Zhengyan Shi, Yuan Yao, Lei Liang, Keyan Ding, Emine Yilmaz, Huajun Chen, Qiang Zhang

GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization (2025.acl-long)

Challenge:	Existing approaches to text generation often neglect event structures that shape real-world narratives.
Approach:	They propose a framework that integrates structured event semantics with iterative retrieval and inference to enhance text generation.
Outcome:	Experiments on UltraDomain and MultiHopRAG show that the proposed framework outperforms baseline RAG systems in generation effectiveness, logical consistency, and multi-hop reasoning accuracy.

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Zhouhong Gu, Xingzhou Chen, Xiaoran Shi, Tao Wang, Suhang Zheng, Tianyu Li, Hongwei Feng, Yanghua Xiao

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

Challenge:	Existing methods for achieving this require a limited understanding of constraints and can be hallucinating or brittle.
Approach:	They propose a framework that combines adversarial training dynamics with an encoder-only reward model to progressively learn and adapt to increasingly complex constraints.
Outcome:	Extensive experiments show that GAPO significantly outperforms existing methods like PPO, DPO, and KTO in fine-grained constraints.

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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

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

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

DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection (2026.acl-long)

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

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Junchao Wu, Yefeng Liu, Chenyu Zhu, Hao Zhang, Zeyu Wu, Tianqi Shi, Yichao Du, Longyue Wang, Weihua Luo, Jinsong Su, Derek F. Wong

MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning (2025.findings-acl)

Challenge:	Existing detectors are limited in their ability to detect large language models generated content in multilingual environments.
Approach:	They propose a multilingual benchmark to evaluate advanced detectors across 8 dimensions to better align with real-world applications.
Outcome:	The proposed benchmark encompasses 8 languages commonly used in commercial contexts and collects human-written texts from 6 domains highly susceptible to LLM misuse.

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Ke Wang, Junting Pan, Linda Wei, Aojun Zhou, Weikang Shi, Zimu Lu, Han Xiao, Yunqiao Yang, Houxing Ren, Mingjie Zhan, Hongsheng Li

Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning (2022.findings-emnlp)

Challenge:	Large Language Models (LMMs) struggle with simple tasks such as geometry, e.g., arithmetic, and reasoning.
Approach:	They propose to leverage code as supervision for cross-modal alignment . they propose to use FigCodifier and ImgCode-8.6M to synthesize novel mathematical figures .
Outcome:	The proposed model surpasses GPT-4o and Claude 3.5 Sonnet in the geometry problem-solving subset of MathVista, achieving improvements of 8.9% and 9.2%.

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Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang

A Thorough Examination of Decoding Methods in the Era of LLMs (2024.emnlp-main)

Challenge:	Existing approaches to learning KG triplets ignore ternary propagation patterns and ignore zero-shot, few-shot and synonymity problems.
Approach:	They propose a framework for contrastive learning based on ternary propagation patterns among head, relation and tail.
Outcome:	Experiments on benchmarks show that TernaryCL is superior to state-of-the-art models.

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Chufan Shi, Haoran Yang, Deng Cai, Zhisong Zhang, Yifan Wang, Yujiu Yang, Wai Lam

Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models (2025.naacl-long)

Challenge:	Decoding methods are essential for converting language models from next-token predictors into practical task solvers.
Approach:	They propose to evaluate decoding methods in general-purpose large language models . they find that decoding method performance is notably task-dependent .
Outcome:	The proposed methods perform task-dependently and are influenced by alignment, model size, and quantization.

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Hengyi Wang, Haizhou Shi, Shiwei Tan, Weiyi Qin, Wenyuan Wang, Tunyu Zhang, Akshay Nambi, Tanuja Ganu, Hao Wang

BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (2024.emnlp-main)

Challenge:	Multimodal Large Language Models have shown significant promise in various applications, but a comprehensive evaluation of their long-context capabilities remains underexplored.
Approach:	They propose a benchmark to assess the long-context capabilities of multimodal large language models.
Outcome:	The proposed benchmark compared MLLMs with API-based and open-source models in a long-context scenario.

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Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce Ho, Chao Zhang, Carl Yang

PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback (2026.findings-acl)

Challenge:	Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedically tasks but still challenging due to the lack of sufficient publicly annotated biomedic data and computational resources.
Approach:	They propose a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedically corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs.
Outcome:	Experiments on 5 biomedical tasks across 11 datasets confirm the performance of the retrieval model on various biomedically demanding tasks.

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Wei Xia, Jin Wu, Haoran Shi, Xiangyu Wang, Chanjin Zheng

Explainability & Interpretability in NLP

Challenge:	Existing approaches to Automated Essay Scoring (AES) treat scoring and feedback as separate components, resulting in fragmentation.
Approach:	They propose a psychometrically-aware framework that integrates diagnostic assessment with instructional scaffolding through a shared latent ability representation.
Outcome:	The proposed framework integrates diagnostic assessment with instructional scaffolding through a shared latent ability representation.

Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning (D18-1)

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Chen Shi, Qi Chen, Lei Sha, Sujian Li, Xu Sun, Houfeng Wang, Lintao Zhang

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

Challenge:	Existing dialog datasets rely on human labeling, which is expensive, limited in size, and in low coverage.
Approach:	They propose a framework to automatically cluster dialogue intents and slots . they collect context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling.
Outcome:	The proposed framework can promote human labeling cost to a great extent and achieve good intent clustering accuracy (84.1%) it also provides reasonable and instructive slot labeling results.

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

EVM-QuestBench: An Execution-Grounded Benchmark for Natural-Language Transaction Code Generation (2026.acl-long)

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

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Pei Yang, Wanyi Chen, Ke Wang, Lynn Ai, Eric Yang, Tianyu Shi

Towards Unified Prompt Tuning for Few-shot Text Classification (2022.findings-emnlp)

Challenge:	Existing evaluations of large language models overlook execution accuracy and safety.
Approach:	They propose an execution-grounded benchmark for natural-language transaction-script generation on EVM-compatible chains.
Outcome:	The proposed benchmark finds large performance gaps in the models with 5 independent rounds.

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Jianing Wang, Chengyu Wang, Fuli Luo, Chuanqi Tan, Minghui Qiu, Fei Yang, Qiuhui Shi, Songfang Huang, Ming Gao

Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)

Challenge:	Prompt-based fine-tuning has boosted performance of Pre-trained Language Models (PLMs) on few-shot text classification, but PLMs are unfamiliar with prompt-style expressions during pre-training, which limits the few- shot learning performance on downstream tasks.
Approach:	They propose a framework for prompt-based fine-tuning that captures prompting semantics from non-target NLP datasets and propose 'Prompt-Options-Verbalizer' for joint prompt learning across different NLP tasks.
Outcome:	Experiments show that the proposed framework outperforms state-of-the-art prompt-based fine-tuning frameworks on few-shot text classification tasks.

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Fobo Shi, Peijun Qing, Dong Yang, Nan Wang, Youbo Lei, Haonan Lu, Xiaodong Lin, Duantengchuan Li

On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)

Challenge:	Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions.
Approach:	They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts.
Outcome:	The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks.

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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)

Challenge:	Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning.
Approach:	They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized.
Outcome:	The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized .

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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang

LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following (2025.coling-main)

Challenge:	EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers.
Approach:	They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs.
Outcome:	The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets.

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Kaize Shi, Xueyao Sun, Dingxian Wang, Yinlin Fu, Guandong Xu, Qing Li

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

Challenge:	E-commerce authoring requires engaging, diverse, and targeted content . Large language models lack memorization of domain-specific features in e-commerce applications .
Approach:	They propose a unified e-commerce authoring models that address contextual preferences of customers, sellers, and platforms . they propose to integrate interleaved features presented by participating objects into the models to empower authoring applications with comprehensive scenario understanding .
Outcome:	The proposed models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications.

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

Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation (2021.findings-emnlp)

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

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Jinfa Yang, Yongjie Shi, Xin Tong, Robin Wang, Taiyan Chen, Xianghua Ying

Counterfactual Adversarial Learning with Representation Interpolation (2021.findings-emnlp)

Challenge:	Existing knowledge graph embedding methods use k-dimensional vectors to represent each entity in a knowledge graph.
Approach:	They propose to use affine transformations to embed knowledge graphs using previous methods . they propose to add k additional variables to the existing methods to perform embedding .
Outcome:	The proposed method outperforms RotatE, Distmult and ComplEx on various data sets.

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Wei Wang, Boxin Wang, Ning Shi, Jinfeng Li, Bingyu Zhu, Xiangyu Liu, Rong Zhang

Defending LLMs against Jailbreaking Attacks via Backtranslation (2024.findings-acl)

Challenge:	Existing models with statistical bias are prone to memorized correlations . large pre-trained models such as BERT have revolutionized the model development paradigm in natural language processing .
Approach:	They propose a framework to tackle the problem from a causal perspective using a latent space interpolation approach.
Outcome:	Extensive experiments show that CAT achieves substantial performance improvement over SOTA across different downstream tasks, including sentence classification, natural language inference and question answering.

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Yihan Wang, Zhouxing Shi, Andrew Bai, Cho-Jui Hsieh

BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving (2025.acl-long)

Challenge:	Recent advancement in large language models (LLMs) has shown their extensive applications and transformative potential to reshape people's lives.
Approach:	They propose a method which uses backtranslation to infer an input prompt from an input input prompt and then run it again on the backtranslated prompt.
Outcome:	The proposed method outperforms baselines and has little impact on the generation quality for benign input prompts.

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Teng Wang, Wing Yin Yu, Zhenqi He, Zehua Liu, HaileiGong HaileiGong, Han Wu, Xiongwei Han, Wei Shi, Ruifeng She, Fangzhou Zhu, Tao Zhong

Self-Attention with Structural Position Representations (D19-1)

Challenge:	Existing datasets in operations research domain lack detailed annotations of the modeling process, focusing only on objective values.
Approach:	They propose an annotation-based tree-of-thought tree-based reasoning algorithm that integrates reinforcement learning into a tree- of-though.
Outcome:	The proposed algorithm outperforms state-of-the-art methods on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets.

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Xing Wang, Zhaopeng Tu, Longyue Wang, Shuming Shi

Understanding and Improving the Robustness of Terminology Constraints in Neural Machine Translation (2023.acl-long)

Challenge:	Experimental results show that SANs can't encode positions of input words . SAN's are currently lacking in encoding positions of words based on position-unaware "bagof-words" theory .
Approach:	They propose to augment SANs with structural position representations to capture latent structure of input sentence.
Outcome:	The proposed approach consistently outperforms the sequential representations on translation tasks.

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Huaao Zhang, Qiang Wang, Bo Qin, Zelin Shi, Haibo Wang, Ming Chen

Contrastive Learning with Generated Representations for Inductive Knowledge Graph Embedding (2023.findings-acl)

Challenge:	Existing terminology constraint test sets are blind to this issue due to oversimplified settings . PH methods retain high constraint accuracy but lower translation quality .
Approach:	They propose a method that replaces terminology terms with ordered labels . placeholder methods are better at retaining high constraint accuracy but lower translation quality .
Outcome:	The proposed method achieves high accuracy and translation quality regardless of the number or length of constraints.

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Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang, Chengwei Qin

AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation (2025.findings-acl)

Challenge:	Existing methods for inductive knowledge Graphs are limited by sparsity and implicit transfer.
Approach:	They propose a Contrastive Learning framework with graph guided Variational autoencoder on Meta-KGs to capture and transfer entities.
Outcome:	The proposed framework outperforms state-of-the-art methods with extensive experiments.

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Ming Wang, Peidong Wang, Lin Wu, Xiaocui Yang, Daling Wang, Shi Feng, Yuxin Chen, Bixuan Wang, Yifei Zhang

WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning (2026.findings-eacl)

Challenge:	Existing models of seeker simulations are limited by the cost and ethical concerns of involving real seekers in mental health research.
Approach:	They propose an emotional and cognitive dynamic agent system equipped with tertiary memory to enable dynamic control of the simulator's configurations.
Outcome:	The proposed system achieves more realistic seeker simulation compared to baselines.

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Yuchen Zhuang, Di Jin, Jiaao Chen, Wenqi Shi, Hanrui Wang, Chao Zhang

Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020.findings-emnlp)

Challenge:	Existing web agents relying on supervised fine-tuning struggle with generalization and robustness due to insufficient reasoning capabilities when handling the inherently dynamic nature of web interactions.
Approach:	They propose a large language model-empowered web agent that trains using a rule-based reinforcement learning framework to enhance single-step reasoning and planning for business-oriented web navigation tasks.
Outcome:	The proposed agent outperforms baseline LLM-based agents on the WorkArena benchmark by 10.26–16.59%.

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Ke Wang, Jiayi Wang, Niyu Ge, Yangbin Shi, Yu Zhao, Kai Fan

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

Challenge:	Using neural machine translation to approximate human parity is difficult due to the lack of parallel training corpora.
Approach:	They propose an end-to-end deep learning framework for quality estimation and automatic post-editing of machine translation output.
Outcome:	The proposed framework achieves state-of-the-art performance on the English–German dataset and human translators can significantly expedite their post-editing processing with the model.

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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

Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detection (2026.acl-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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Xiaowei Zhu, Yubing Ren, Fang Fang, Shi Wang, Yanan Cao, Li Guo

InsLogicBench: An Argumentation Logic Grounded Benchmark for Complex Insurance Claims Adjudication (2026.acl-long)

Challenge:	Existing methods for AI-generated text detection assume uniform token contributions, making them less robust under short sequences or localized token modifications.
Approach:	They propose a training-free method for AI-generated text detection based on an exon-aware token reweighting perspective.
Outcome:	The proposed method achieves state-of-the-art detection performance and robustness to adversarial attacks and varying input lengths.

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Jin Liu, Yunpeng Liu, Keyi Wang, Jie Shi, Xiao Xu, Wenkang Huang, Xingzhong Xu, Xin Liang, Yanghua Xiao

ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation (2025.acl-long)

Challenge:	Existing benchmarks for insurance claims adjudication are limited to information retrieval or simple multiple-choice setups.
Approach:	They propose a benchmark that provides complete reasoning traces linking factual inputs, relevant policy clauses, and final verdicts.
Outcome:	The proposed benchmark shows that models often produce correct decisions but fail to provide precise justifications, highlighting a critical discrepancy between decision accuracy and logical reasoning capabilities.

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Xuanle Zhao, Xianzhen Luo, Qi Shi, Chi Chen, Shuo Wang, Zhiyuan Liu, Maosong Sun

InsBank: Evolving Instruction Subset for Ongoing Alignment (2025.findings-emnlp)

Challenge:	Existing open-source MLLMs fail to fully capture dense information embedded in charts . current models still face significant challenges in understanding and analyzing visual tasks such as captioning and question answering.
Approach:	They propose a chart-to-code MLLM which leverages Code LLMs as the language backbone to enhance the executability of the generated code.
Outcome:	The proposed model surpasses existing open-source models on chart-to-code benchmarks with only 7B parameters and provides lossless representations that contain all critical details.

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Jiayi Shi, Yiwei Li, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Huan Ren, Yao Hu, Kan Li

Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model (2024.naacl-long)

Challenge:	Recent studies emphasize that quality and diversity of instruction data are more crucial than quantity, highlighting the need to select diverse, high-quality subsets to reduce training costs.
Approach:	They propose to use a continuously updated repository to integrate the latest valuable instruction data with a progressive evolution framework to evolve InsBank over time.
Outcome:	The proposed framework outperforms baselines in InsBank evolution and extracts budget-specific subsets.

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Zhiwei He, Xing Wang, Wenxiang Jiao, Zhuosheng Zhang, Rui Wang, Shuming Shi, Zhaopeng Tu

HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference (2026.acl-long)

Challenge:	Existing methods to improve translation quality using human feedback have not been validated.
Approach:	They propose to use quality estimation to predict human preferences for feedback training . they propose to detect incorrect translations and assign a penalty term to the reward scores .
Outcome:	The proposed method outperforms systems using larger parallel corpora by a small amount of monolingual data.

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Zhiyuan Shi, Qibo Qiu, null Xuefeng, Zhonglin Jiang, Li Yu, Jian Jiang, Xiaofei He, Wenxiao Wang

Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network (2020.emnlp-main)

Challenge:	Existing static compression methods suffer from coarse-grained caching and high I/O overhead.
Approach:	They propose a training-free dynamic compression framework that uses a sparse attention mechanism to categorize attention heads based on stability and similarity.
Outcome:	The proposed framework achieves state-of-the-art performance on long-context benchmarks and accelerates decoding by up to 3 compared to the original model with a 224K context.

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Ruipeng Jia, Yanan Cao, Hengzhu Tang, Fang Fang, Cong Cao, Shi Wang

DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference (2026.findings-acl)

Challenge:	Existing extractive summarization methods focus on balancing salience and redundancy between sentences.
Approach:	They propose a hierarchical attentive heterogeneous graph for text summarization that models sentences . they propose to iteratively refine the sentence representations and deliver the labels by message passing .
Outcome:	The proposed method outperforms existing extractive summarization methods on large corpus.

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Zihan Wang, Hao Wang, Shi Feng, Xiaocui Yang, Daling Wang, Yiqun Zhang, Jinghao Lin, Xiaozhong Ji, Haihua Yang

AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering (2026.acl-long)

Challenge:	Medical reasoning models are constrained by parametric knowledge and can induce hallucinations and spurious attributions.
Approach:	They propose a model that uses a multi-hop med-search QA synthesis method to apply the DR paradigm in medical contexts.
Outcome:	The proposed model outperforms larger medical reasoning models on medical benchmarks.

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Zheyuan Zhang, Kaiwen Shi, Zhengqing Yuan, Zehong Wang, Tianyi Ma, Keerthiram Murugesan, Vincent Galassi, Chuxu Zhang, Yanfang Ye

DualGuard: Dual-stream Large Language Model Watermarking Defense against Paraphrase and Spoofing Attack (2026.findings-acl)

Challenge:	Existing approaches to agent routing emphasize cost efficiency while overlooking the fine-grained contextual and relational structure inherent in QA tasks.
Approach:	They propose a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals.
Outcome:	The proposed framework outperforms single-agent and ensemble baselines while generalizing across benchmarks and LLM backbones.

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Hao Li, Yubing Ren, Yanan Cao, Yingjie Li, Fang Fang, Shi Wang, Li Guo

MIMIR: A Customizable Agent Tuning Platform for Enhanced Scientific Applications (2024.emnlp-demo)

Challenge:	Existing watermarking algorithms focus on defending against paraphrase and piggyback spoofing attacks, which can inject harmful content, compromise reliability, and undermine trust in attribution.
Approach:	They propose an algorithm capable of defending against paraphrase and spoofing attacks.
Outcome:	Experiments on large language models and language models show that DualGuard is the first watermarking algorithm capable of defending against both paraphrase and spoofing attacks.

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Xiangru Tang, Chunyuan Deng, Hanminwang Hanminwang, Haoran Wang, Yilun Zhao, Wenqi Shi, Yi Fung, Wangchunshu Zhou, Jiannan Cao, Heng Ji, Arman Cohan, Mark Gerstein

KBAlign: Efficient Self Adaptation on Specific Textual Knowledge Bases (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have evolved into interactive agents capable of planning, tool use, and task execution across various tasks.
Approach:	They propose a platform that leverages large language models to generate agent-tuning data for fine-tuneing smaller, specialized models.
Outcome:	MIMIR enables large models to simulate various roles and create interaction data, which can then be used to fine-tune open-source models like LLaMA2.

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Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun

One Model to Learn Both: Zero Pronoun Prediction and Translation (D19-1)

Challenge:	Existing methods for retrieval-augmented generation (RAG) are limited and fine-tuning incurs prohibitive costs of external signals.
Approach:	They propose a self-supervised framework that enhances RAG systems through efficient model adaptation.
Outcome:	The proposed framework achieves 90% of the performance gain obtained through GPT-4-supervised adaptation while relying entirely on self-annotation of much smaller models.

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Longyue Wang, Zhaopeng Tu, Xing Wang, Shuming Shi

Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)

Challenge:	Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language.
Approach:	They propose a unified and discourse-aware ZP translation approach for neural MT models . they jointly learn to predict and translate ZPs in an end-to-end manner .
Outcome:	The proposed method improves translation performance and ZP prediction accuracy over baseline models and external models.

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

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

Challenge:	Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Approach:	They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Outcome:	The proposed framework maps incomplete learning to causes using observable training and inference signals.

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

EmpCRL: Controllable Empathetic Response Generation via In-Context Commonsense Reasoning and Reinforcement Learning (2024.lrec-main)

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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Mingxiu Cai, Daling Wang, Shi Feng, Yifei Zhang

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

Challenge:	Existing studies lack the perception of fine-grained dialogue emotion propagation, and have limitations in reasoning about the intentions of users on cognition, which affect the quality of empathetic response.
Approach:	They propose to use commonsense reasoning and reinforcement learning to generate empathetic response based on in-context commonsensing and contextual reasoning to broaden cognitive boundaries.
Outcome:	The proposed model outperforms state-of-the-art models in automatic and human evaluation.

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

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

Static or Dynamic: Towards Query-Adaptive Token Selection for Video Question Answering (2025.emnlp-main)

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Yumeng Shi, Quanyu Long, Wenya Wang

Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking (2023.acl-long)

Challenge:	Existing approaches to compress video inputs ignore the importance of static and dynamic information in long videos, leading to inefficient token usage within limited budgets.
Approach:	They propose a token selection strategy that adaptively adjusts static and dynamic information based on question requirements.
Outcome:	The proposed method achieves performance improvements (up to 5.8%) on multiple video question answering benchmarks.

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Qingyue Wang, Liang Ding, Yanan Cao, Yibing Zhan, Zheng Lin, Shi Wang, Dacheng Tao, Li Guo

ReactXT: Understanding Molecular “Reaction-ship” via Reaction-Contextualized Molecule-Text Pretraining (2024.findings-acl)

Challenge:	Existing methods to enhance the zeroshot generalization of DST fail to effectively decouple semantics of samples, limiting the zero-shot performance of the system.
Approach:	They propose a new learning schema that explicitly disentangles the semantics of seen data and leverages the performance and robustness with the mixture-of-experts mechanism.
Outcome:	The proposed model achieves state-of-the-art on multiWOZ2.1 with 10M trainable parameters and is robust to the mixture-of experts mechanism.

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Zhiyuan Liu, Yaorui Shi, An Zhang, Sihang Li, Enzhi Zhang, Xiang Wang, Kenji Kawaguchi, Tat-Seng Chua

Efficient Learned Data Compression via Dual-Stream Feature Decoupling (2026.acl-long)

Challenge:	Molecular-text modeling is an emerging research field that aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge.
Approach:	They propose a new method for reaction-text modeling that uses three types of input contexts to incrementally pretrain LMs.
Outcome:	The proposed method improves experimental procedure prediction and molecule captioning and offers competitive results in retrosynthesis.

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Huidong Ma, Xinyan Shi, Sun Hui, Xiaofei Yue, Xiaoguang Liu, Gang Wang, Wentong Cai

DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery (2023.emnlp-main)

Challenge:	Learned data compression has achieved superior compression ratios, but balancing precise probability modeling with system efficiency remains challenging.
Approach:	They propose a Dual-Stream Multi-Scale Decoupler that disentangles local and global contexts to replace deep serial processing with shallow parallel streams.
Outcome:	The proposed method achieves state-of-the-art performance in both compression ratio and throughput while maintaining the lowest latency and memory usage.

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Wenbin An, Feng Tian, Wenkai Shi, Yan Chen, Qinghua Zheng, QianYing Wang, Ping Chen

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods to learn compact cluster representations from coarsely labeled data are noisy and degrade the quality of learning.
Approach:	They propose a framework that encodes semantic structures of data into the embedding space . they retrieve k-nearest neighbors of a query as positive keys to capture similarities .
Outcome:	The proposed framework can retrieve more accurate neighbors and outperform state-of-the-art models by a large margin.

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Feiyang Li, Peng Fang, Zhan Shi, Arijit Khan, Fang Wang, Weihao Wang, null Zhangxin-hw, Cui Yongjian

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

Challenge:	Chain-of-thought reasoning has two key limitations: lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference logic.
Approach:	They propose a chain-of-thought reasoning framework with three key designs to address these issues.
Outcome:	The proposed framework improves the performance of large language models on complex tasks by incorporating knowledge graphs and learnable knowledge case-aware RAG.

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

Automatic Error Analysis for Document-level Information Extraction (2022.acl-long)

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

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Aliva Das, Xinya Du, Barry Wang, Kejian Shi, Jiayuan Gu, Thomas Porter, Claire Cardie

RACQC: Advanced Retrieval-Augmented Generation for Chinese Query Correction (2025.findings-emnlp)

Challenge:	Document-level information extraction (IE) tasks have been revisited in earnest . evaluation of the approaches has been limited in a number of dimensions .
Approach:	They propose a transformation-based framework for automating error analysis in document-level event and (N-ary) relation extraction.
Outcome:	The proposed framework compares two state-of-the-art document-level template-filling approaches on datasets from three domains and four systems from the MUC-4 evaluation.

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Jinbo Su, Lingzhe Gao, Wei Li, Shihao Liu, Haojie Lei, Xinyi Wang, Yuanzhao Guo, Ke Wang, Daiting Shi, Dawei Yin

Benchmarking and Learning Real-World Customer Service Dialogue (2026.acl-long)

Challenge:	Large language models (LLMs) exhibit remarkable capabilities across many tasks, but face critical challenges in the CSC scenario: (1) poor generalization to rare entities in open-domain searches; and (2) failure to adapt to temporal entity variations due to static parameters, resulting in serious over-correction issues.
Approach:	They propose a Chinese Spelling Check system with RAG and multi-task learning that integrates dynamic knowledge retrieval and entity-centric RAG to address rare entities.
Outcome:	The proposed system outperforms existing baselines in the CSC task and achieves a maximum improvement of +9.92% on the search scenario benchmark and +3.2% on the general-domain dataset.

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Tianhong Gao, Jundong Shen, Jiapeng Wang, Bei Shi, Ying Ju, Junfeng Yao, Huiyu Yu

Reasoning Makes Good Annotators : An Automatic Task-specific Rules Distilling Framework for Low-resource Relation Extraction (2023.findings-emnlp)

Challenge:	Existing benchmarks and training pipelines for industrial intelligent customer service (ICS) focus on task completion and tool correctness.
Approach:	They propose a benchmark-to-optimization loop that bridges offline gains to deployment . they propose OlaMind, which distills reusable reasoning patterns from expert dialogues .
Outcome:	The proposed benchmark surpasses GPT-5.2 and Gemini 3 Pro on OlaBench . it delivers an average +23.67% issue resolution and -6.6% human transfer rate versus baseline .

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Yilin Lu, Juncheng Li, Xiaoqiang Wang, Haochen Shi, Tao Chen, Siliang Tang

MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation (2026.findings-acl)

Challenge:	Existing methods to extract knowledge from unlabeled data generate noise labels.
Approach:	They propose an automatic task-specific rules distilling framework to generate a logic rule from unlabeled data.
Outcome:	The proposed framework could power the labeling ability by discovering reliable model-labeled data.

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Haoyuan Shi, Yunxin li, Nanhao Deng, Zhenran Xu, Xinyu Chen, Longyue Wang, Baotian Hu, Min Zhang

R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling (2026.acl-long)

Challenge:	Existing evaluation methods for complex multi-shot video are anchored to single-shot paradigms, lacking comprehensive story assets and cross-shot metrics.
Approach:	They propose a framework that synergizes the high-level semantic reasoning of Large Multimodal Models with the fine-grained perceptual rigor of domain-specific expert models.
Outcome:	The proposed framework synergizes the high-level semantic reasoning of Large Multimodal Models with the fine-grained perceptual rigor of domain-specific expert models.

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Aijia Cheng, Kailong Wang, Ling Shi, Yongxin Zhao

Memory-Guided Hard Data Augmentation for Multimodal Named Entity Recognition (2026.findings-acl)

Challenge:	Existing RL-based approaches to function calling are misaligned between reasoning processes and tool-call decisions.
Approach:	They propose a reasoning-aware RL framework for interpretable function calling . they integrate a composite reward integrating format/correctness constraints, CER, and SMV .
Outcome:	Experiments on BFCL/ACEBench show R2IF outperforms baselines by 34.62% with positive Average CoT Effectiveness.

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Xinyu Liu, Kai fu, Yinghan Shi, Quanyou Chu, Ming Du, Hongya Wang, Xiaojun Meng, Jiansheng Wei, Yanghua Xiao, Bo Xu

KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus (2025.findings-naacl)

Challenge:	Existing methods for Named Entity Recognition (NER) ignore the internal state of the target model.
Approach:	They propose a framework to repair model-specific errors by using a model-based approach . they employ cross-validation to identify model- specific Hard Data and a memory tree to induce macro-level error patterns from micro-level failures.
Outcome:	The proposed framework yields significant performance gains on Twitter and other platforms.

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Xiaoming Shi, Zeming Liu, Yiming Lei, Chenkai Zhang, Haitao Leng, Chuan Wang, Qingjie Liu, Wanxiang Che, Yunhong Wang

GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing (2026.acl-long)

Challenge:	Currently, video-based dialogue systems rely on a single dialogue type, hindering their versatility in practical applications.
Approach:	They propose to generate video-driven multilingual mixed-type dialogues using KwaiChat . they propose to create a video-based multilingual mix of 4 dialogue types, 30 domains, 4 languages, 13 topics .
Outcome:	The proposed model performs best on KwaiChat but is not perfect in this situation.

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Ming Wang, Shuang Wu, Bixuan Wang, Lu Lin, Yuxin Chen, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yufan Sun

Challenge:	Large language models (LLMs) inherit contamination from training corpora, directional bias under social-desirability framing, and limited responsiveness to context beyond the item text.
Approach:	They propose a paradigm that reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline.
Outcome:	The proposed paradigm reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline.

ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction (2023.findings-emnlp)

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Yaorui Shi, An Zhang, Enzhi Zhang, Zhiyuan Liu, Xiang Wang

Review-Instruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models (2025.findings-acl)

Challenge:	Existing methods for predicting chemical reactions are limited by insufficient training data and inability to utilize textual information.
Approach:	They propose a framework that leverages chemical knowledge encoded in language models to assist GNNs, thereby enhancing the accuracy of real-world chemical reaction predictions.
Outcome:	The proposed framework improves state-of-the-art GNN-based methods across chemical reaction datasets especially in out-of distribution settings.

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Jiangxu Wu, Cong Wang, Tianhuang Su, Lin Haozhi, JunYang JunYang, Zhangchao Zhangchao, Binqiang Pan, SongpanYang SongpanYang, Mingpeng Mingpeng, Kai Shi, Zixian Li

You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL (2025.naacl-long)

Challenge:	Existing methods for generating multi-turn dialogue data struggle to ensure both diversity and quality in instructions.
Approach:	They propose a framework that synthesizes multi-turn conversations through an iterative "Ask-Respond-Review" process involving three agent roles: a Candidate, multiple Reviewers, and a Chairman.
Outcome:	The proposed framework synthesizes multi-turn conversations through an iterative "Ask-Respond-Review" process involving three agent roles: a Candidate, multiple Reviewers, and a Chairman.

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Hideo Kobayashi, Wuwei Lan, Peng Shi, Shuaichen Chang, Jiang Guo, Henghui Zhu, Zhiguo Wang, Patrick Ng

QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)

Challenge:	Existing text-to-SQL systems encode the same schema for every question, resulting in unnecessary high inference cost and missing crucial database knowledge.
Approach:	They propose a paradigm that directly internalizes database knowledge into the parametric knowledge of a text-to-SQL model during training and eliminates the need for schema encoding during inference.
Outcome:	The proposed paradigm significantly reduces the input token length by 66%-98% and outperforms traditional systems on three benchmarks.

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Haochen Shi, Weiqi Wang, Tianqing Fang, Baixuan Xu, Wenxuan Ding, Xin Liu, Yangqiu Song

RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts (2024.lrec-main)

Challenge:	Existing approaches to QA fine-tune language models on QA pairs constructed from CommonSense Knowledge Bases (CSKBs) however, current QA synthesis protocols introduce noise from the CSKB and generate ungrammatical questions and false negative options, which impede the model’s ability to generalize.
Approach:	They propose a framework to analyze the training dynamics of each QA pair at both the question level and option level, discarding machine-detectable artifacts and mislabeled or false-negative options.
Outcome:	The proposed framework outperforms baseline approaches while using only 33% of the synthetic data.

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Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, Jinkun Lin, Yangguang Mei, Linnan Xu

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)

Challenge:	RAAMove is a comprehensive multi-domain corpus dedicated to the annotation of move structures in Research Article (RA) abstracts.
Approach:	They propose a multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
Outcome:	The proposed corpus is based on a human-annotated dataset and a BERT-based model to verify its effectiveness.

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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

Mixture of Heterogeneous Grouped Experts for Language Modeling (2026.acl-industry)

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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Zhicheng Ma, Xiang Liu, Zhaoxiang Liu, Ning Wang, Yi Shen, Kai Wang, Shuming Shi, Shiguo Lian

Knowledge Graph Embedding by Adaptive Limit Scoring Loss Using Dynamic Weighting Strategy (2022.findings-acl)

Challenge:	Large Language Models (LLMs) based on Mixture-of-Experts (MoE) enforce uniform expert sizes, creating a rigidity that fails to align computational costs with varying token-level complexity.
Approach:	They propose a mixture of heterogeneous grouped experts (MoHGE) that allows for flexible, resource-aware expert combinations.
Outcome:	The proposed model matches the performance of existing Mixture-of-Experts architectures while maintaining balanced GPU utilization.

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Jinfa Yang, Xianghua Ying, Yongjie Shi, Xin Tong, Ruibin Wang, Taiyan Chen, Bowei Xing

PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)

Challenge:	Existing knowledge graph embedding models use a loss framework to distinguish between correct and incorrect triplets.
Approach:	They propose a loss framework that reweights each triplet to highlight the less-optimized triplets.
Outcome:	The proposed method performs on several knowledge graph embedding models, including TransE, TransH and ComplEx.

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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang

RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)

Challenge:	Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed .
Approach:	They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision.
Outcome:	The proposed framework reduces token usage while improving accuracy on math benchmarks.

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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang

Leveraging Unpaired Feedback for Long-Term LLM-based Recommendation Tuning (2025.findings-emnlp)

Challenge:	Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting.
Approach:	They propose a representation-aware model merging framework for continual learning without access to historical data.
Outcome:	The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios.

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Jizhi Zhang, Chongming Gao, Wentao Shi, Xin Chen, Jingang Wang, Xunliang Cai, Fuli Feng

Challenge:	a recent study highlights unpaired feedback as a key challenge for long-term LLM-based recommenders . unpaired user feedback is crucial for improving LLMs in dynamic user environments, authors say .
Approach:	They propose a framework that incorporates unpaired feedback into LLMs to improve long-term recommendation performance.
Outcome:	The proposed framework improves long-term recommendation performance by incorporating unpaired feedback without requiring paired supervision.

Model Surgery: Modulating LLM’s Behavior Via Simple Parameter Editing (2025.naacl-long)

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Huanqian Wang, Yang Yue, Rui Lu, Jingxin Shi, Andrew Zhao, Shenzhi Wang, Shiji Song, Gao Huang

Challenge:	Current approaches for detoxification or preventing jailbreaking involve fine-tuning billions of parameters through gradient descent with substantial computational cost.
Approach:	They propose to use supervised fine-tuning and Reinforcement Learning from human feedback to modify LLMs' behavior by directly editing a small subset of parameters.
Outcome:	Experiments show that editing a small subset of parameters can modulate specific behaviors of LLMs, such as detoxification and resistance to jailbreak, with only inference-level computational resources.

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling (2026.acl-long)

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Jianghao Lin, Yuanyuan Shi, Xin Peng, Renjie Ding, Hairui Wang, Yuxuan Peng, Bizhe Bai, Weixi Song, Fengshuo Bai, Huacan Chai, Weinan Zhang, Fei Huang, Ying Wen

Challenge:	Existing research on inference scaling focuses on unstructured output generation tasks, such as mathematical problems.
Approach:	They propose an inference-scaling framework that combines fine-grained beam search with ToolPRM, a process reward model scoring each intra-call decision.
Outcome:	The proposed framework outperforms outcome and coarse-grained reward models in predictive accuracy and yields consistent test-time gains on multiple function-calling benchmarks.

Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)

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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang

On the Sub-layer Functionalities of Transformer Decoder (2020.findings-emnlp)

Challenge:	Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains.
Approach:	They propose several strategies for deploying RAG that balance performance and efficiency.
Outcome:	The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy.

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Yilin Yang, Longyue Wang, Shuming Shi, Prasad Tadepalli, Stefan Lee, Zhaopeng Tu

Structured Episodic Event Memory (2026.acl-long)

Challenge:	Existing efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation have focused on assessing the encoded representations or interpreting the multi-head self-attentions.
Approach:	They propose to use Transformer-based encoder-decoder architectures to analyze how information is propagated through each module of each decoder layer.
Outcome:	The proposed model can be dropped with minimal loss of performance on three translation datasets and can be used to train and inference faster.

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Zhengxuan Lu, Dongfang Li, Yukun Shi, Beilun Wang, Longyue Wang, Baotian Hu

Probability-Consistent Preference Optimization for Enhanced LLM Reasoning (2025.findings-acl)

Challenge:	Current approaches to memory in Large Language Models (LLMs) rely on static Retrieval-Augmented Generation (RAG) this lacks the cognitive organization necessary to model the dynamic and associative nature of long-term interaction.
Approach:	They propose a hierarchical framework that transforms interaction streams into structured Episodic Event Frames (EEFs) anchored by precise provenance pointers.
Outcome:	The proposed framework outperforms baseline approaches on LoCoMo and LongMemEval benchmarks.

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Yunqiao Yang, Houxing Ren, Zimu Lu, Ke Wang, Weikang Shi, Aojun Zhou, Junting Pan, Mingjie Zhan, Hongsheng Li

TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM (2025.coling-main)

Challenge:	Recent advances in preference optimization have demonstrated significant potential for improving mathematical reasoning capabilities in large language models.
Approach:	They propose a framework that establishes two quantitative metrics for preference selection: surface-level answer correctness and intrinsic token-level probability consistency.
Outcome:	The proposed framework outperforms existing outcome-only criterion approaches across a diverse range of LLMs and benchmarks.

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Huiying Cao, Yiqun Zhang, Shi Feng, Xiaocui Yang, Daling Wang, Yifei Zhang

Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling (2020.coling-main)

Challenge:	Empathetic conversation is a crucial characteristic in daily conversations between individuals.
Approach:	They propose an Emotional Knowledge Tool Calling framework which encapsulates commonsense knowledge bases as empathetic tools, enabling LLMs to integrate external knowledge flexibly.
Outcome:	The proposed framework can generate empathetic responses effectively on the TOOL-ED dataset.

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Xu Cao, Deyi Xiong, Chongyang Shi, Chao Wang, Yao Meng, Changjian Hu

Semantic Parsing

Challenge:	Existing joint models for intent detection and slot filling show insufficient robustness . however, some small changes of inputs can fool the models to produce wrong predictions .
Approach:	They propose a joint adversarial training model that generates adversarials to attack the joint model and trains the model to defend against the adversarial examples.
Outcome:	The proposed model achieves significantly higher scores and improves robustness on two datasets.

Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)

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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang

Automatic Article Commenting: the Task and Dataset (P18-2)

Challenge:	Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences .
Approach:	They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification .
Outcome:	The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization.

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Lianhui Qin, Lemao Liu, Wei Bi, Yan Wang, Xiaojiang Liu, Zhiting Hu, Hai Zhao, Shuming Shi

Challenge:	Existing methods to make comments on articles are based on human-annotated subsets, but they are not suitable for online forums.
Approach:	They propose to use a large-scale Chinese corpus with millions of real comments and a human-annotated subset characterizing the comments’ varying quality to generalize a broad set of popular reference-based metrics.
Outcome:	The proposed model incorporates human-annotated subset characterizing the comments’ varying quality and shows that it is more accurate than previous models.

Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems (2023.emnlp-main)

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Tianyuan Shi, Liangzhi Li, Zijian Lin, Tao Yang, Xiaojun Quan, Qifan Wang

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

Challenge:	Current approaches to task-oriented dialogue systems integrate knowledge retrieval and response generation, which poses scalability challenges when dealing with extensive knowledge bases.
Approach:	They propose a retriever-generator architecture that harnesses a retrieval and a generator to generate system responses by using feedback from the generator as pseudo-labels.
Outcome:	The proposed architecture shows superior performance on three benchmark datasets.

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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 .

MoLAN: A Unified Modality-Aware Noise Dynamic Editing Framework for Multimodal Sentiment Analysis (2026.findings-acl)

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Xingle Xu, YongKang Liu, Dexian Cai, Shi Feng, Xiaocui Yang, Daling Wang, Yifei Zhang

SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning (2026.findings-acl)

Challenge:	Existing approaches to multimodal sentiment analysis treat entire modality as an independent unit for feature enhancement or denoising, which often suppresses redundant noise at the cost of weakening critical information.
Approach:	They propose a ModaLity-aware noise dynAmic editiNg framework that performs modality-awful block partitioning by dividing features of each modality into multiple blocks.
Outcome:	Experiments on five models and four datasets show that MoLAN+ achieves the state-of-the-art performance.

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Boyan Shi, Wei Chen, Shuyuan Zhao, Junfeng Shen, Shengnan Guo, Shaojiang Wang, Huaiyu Wan

MCPG: A Flexible Multi-Level Controllable Framework for Unsupervised Paraphrase Generation (2022.findings-emnlp)

Challenge:	Existing methods for multitask learning fail to match input semantics with expert capabilities, leading to weak expert specialization.
Approach:	They propose a parameter-efficient mixture-of-experts framework for task-adaptive learning that aligns textual semantics with the most suitable experts for precise routing.
Outcome:	The proposed framework outperforms the state-of-the-art methods and holds excellent task generalization capabilities.

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Yi Chen, Haiyun Jiang, Lemao Liu, Rui Wang, Shuming Shi, Ruifeng Xu

SocialEval: Evaluating Social Intelligence of Large Language Models (2025.acl-long)

Challenge:	Existing studies on controllable unsupervised paraphrase generation are expensive and require supervised training on large parallel corpora.
Approach:	They propose a method for controllable unsupervised paraphrase generation that is flexible to adapt to specific domains without extra training.
Outcome:	The proposed method outperforms state-of-the-art unsupervised baselines by a margin.

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Jinfeng Zhou, Yuxuan Chen, Yihan Shi, Xuanming Zhang, Leqi Lei, Yi Feng, Zexuan Xiong, Miao Yan, Xunzhi Wang, Yaru Cao, Jianing Yin, Shuai Wang, Quanyu Dai, Zhenhua Dong, Hongning Wang, Minlie Huang

PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)

Challenge:	Existing work on LLMs does not address their social intelligence (SI) and their discrepancy with humans.
Approach:	They propose a script-based bilingual SI benchmark that integrates outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation by manually crafting narrative scripts.
Outcome:	The proposed model is based on a script-based bilingual evaluation paradigm that integrates outcome- and process-oriented evaluation by manually crafting narrative scripts.

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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei

Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025.acl-long)

Challenge:	Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning.
Approach:	They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios.
Outcome:	The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics.

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Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S. Yu, Xu Chu, Xiao-Ming Wu

Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator (2026.findings-acl)

Challenge:	Continual learning (CL) is crucial for large language models without costly retraining.
Approach:	They propose a framework for recurrent knowledge identification and fusion that enables dynamic estimation of parameter importance distributions to enhance knowledge transfer.
Outcome:	The proposed framework mitigates catastrophic forgetting and enhances knowledge transfer.

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Zhengliang Shi, Ruotian Ma, Jen-tse Huang, Xinbei Ma, Xingyu Chen, Mengru Wang, Qu Yang, Yue Wang, Fanghua Ye, Ziyang Chen, Shanyi Wang, Cixing LI, Wenxuan Wang, Zhaopeng Tu, Xiaolong Li, Zhaochun Ren, Liefeng Bo

MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models (2026.findings-acl)

Challenge:	Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare.
Approach:	They evaluate 20 state-of-the-art Large language models (LLMs) and 20 LLM dictators to create a social welfare function benchmark.
Outcome:	The proposed model creates dilemma between maximizing collective efficiency and ensuring distributive fairness.

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Junjie Ye, Caishuang Huang, Zhuohan Chen, Wenjie Fu, Chenyuan Yang, Leyi Yang, Yilong Wu, Peng Wang, Meng Zhou, Xiaolong Yang, Tao Gui, Qi Zhang, Zhongchao Shi, Jianping Fan, Xuanjing Huang

Decoding-Unlearning: Fact Forgetting via Entropy-Guided Inference (2026.acl-long)

Challenge:	Existing research has focused on constraint categories, offering little guidance for improving instruction following abilities.
Approach:	They propose a multi-dimensional constraint framework that allows for instruction following . they construct 9,106 code-verifiable samples and evaluate 18 LLMs .
Outcome:	The proposed framework improves instruction following performance without compromising general performance.

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Jingwen Pu, Mingjun Shi, Xinrui Ren, Yizhe Wang, Xinyu Zhang, Zhaokun Wang, Kun She

From Conversation to Evaluation: Benchmarking LLMs on Development Knowledge via SimpleDevQA (2026.findings-acl)

Challenge:	Existing methods for large-scale modeling memorize sensitive information . however, they are limited in real-world scenarios and require updating parameters .
Approach:	They propose a training-free, plug-and-play inference-time unlearning strategy that uses a probe to detect queries involving forgettable concepts and applies entropy-guided decoding to suppress target knowledge.
Outcome:	Experiments on MUSE, RWKU, and WMDP datasets show that SEGUE outperforms existing methods.

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Jing Zhang, Lianghong Guo, Yanlin Wang, Terry Yue Zhuo, Yong Wang, Mingwei Liu, Jiachi Chen, Ensheng Shi, Yuchi Ma, Hongyu Zhang, Zibin Zheng

HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy (2024.emnlp-main)

Challenge:	Existing Dev Knowledge QA benchmarks are limited in development knowledge scope and often not built from real user queries.
Approach:	They conduct preliminary analysis of real user–LLM dialogues from WildChat to investigate the importance of Dev Knowledge QA in AI-assisted software development scenarios.
Outcome:	The proposed benchmark is based on real user–LLM dialogues from WildChat.

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YongKang Liu, Yiqun Zhang, Qian Li, Tong Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schuetze

Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning (2026.findings-acl)

Challenge:	Existing approaches to fine-tuning language models use zeroth-order optimizers to conserve GPU memory.
Approach:	They propose a full-parameter fine-tuning strategy which updates a subset of parameters at each training step.
Outcome:	The proposed approach reduces the amount of gradients and optimizer state parameters residing in GPU memory at the same time, thereby reducing GPU memory usage.

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Yue Guo, Fanfu Wang, Jianwei Lv, Xincheng Shi, Yuchen Li, Youya Wang, Yunsheng Zeng, Yujing Liu, Yunhao Qiao, Gen Li, Junfeng Wang, Bo Yuan

SemanticCamo: Jailbreaking Large Language Models through Semantic Camouflage (2025.findings-acl)

Challenge:	Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face high maintenance costs and low generalization capability.
Approach:	They propose a clinical diagnostic model with clinical reasoning and inquiry skills, the Dr. Assistant, and a pipeline to capture abstract reasoning logic.
Outcome:	The proposed model outperforms open-source models and achieves competitive performance to closed-source model.

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Jihui Yan, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yinzhi Zhao

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

Challenge:	Large Language Models (LLMs) have made safety issues of LLMs more prominent and critical.
Approach:	They propose a framework which attacks LLMs through semantic camouflage and replaces unsafe content with semantic features to conceal malicious intent .
Outcome:	The proposed framework outperforms existing models in over 80% of cases and is highly effective against various defenses.

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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

MUSE: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles (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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Zihan Wang, Xiaocui Yang, YongKang Liu, Shi Feng, Daling Wang, Yifei Zhang

SAD: A Large-Scale Strategic Argumentative Dialogue Dataset (2026.acl-long)

Challenge:	Existing research focuses solely on text, leaving a gap with practical applications.
Approach:	They propose to synthesize a multimodal conversational recommendation dataset using multimodal large language models to automatically synthesized data from 7,000 conversations in the Clothing domain.
Outcome:	The proposed dataset contains 83,148 utterances from 7,000 conversations centered around the Clothing domain.

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YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze

AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph (2024.findings-naacl)

Challenge:	Argumentation is a key part of human reasoning and decision-making . existing argumentative corpora focus on single-turn settings, but multi-turn dialogues are often realized as multi-turned dialogues .
Approach:	They present a dataset for strategic multi-turn argumentation dialogues . they annotate each utterance with five strategy types, allowing multiple strategies per utterrance .
Outcome:	The proposed dataset shows that explicit prompting improves fluency, stylistic coherence and persuasiveness.

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Zhaowei Wang, Haochen Shi, Weiqi Wang, Tianqing Fang, Hongming Zhang, Sehyun Choi, Xin Liu, Yangqiu Song

A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness (D18-1)

Challenge:	Existing language models only touch nouns or verbs within simplified events or specific domains.
Approach:	They propose an entailment graph that collects abstract knowledge for 3 components of diverse events to comprehensively evaluate the abstraction ability of language models.
Outcome:	The proposed benchmark improves LLMs across two previous abstraction tasks.

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Xiangju Li, Kaisong Song, Shi Feng, Daling Wang, Yifei Zhang

Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)

Challenge:	Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue.
Approach:	They propose a co-attention neural network model for emotion cause analysis with emotional context awareness.
Outcome:	The proposed model outperforms the state-of-the-art methods.

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

Training LLMs to be Better Text Embedders through Bidirectional Reconstruction (2025.emnlp-main)

Challenge:	Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs.
Approach:	They propose an efficient generative reward modeling framework grounded in model-internal uncertainty.
Outcome:	The proposed framework reduces inference cost while improving answer accuracy.

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Chang Su, Dengliang Shi, Siyuan Huang, Jintao Du, Changhua Meng, Yu Cheng, Weiqiang Wang, Zhouhan Lin

Spotting AI’s Touch: Identifying LLM-Paraphrased Spans in Text (2024.findings-acl)

Challenge:	Existing text embedding approaches often leverage the embeddment of the final token, typically a reserved special token such as ‘[EOS]‘.
Approach:	They propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding.
Outcome:	The proposed training stage improves performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-of-the-art results across different LLM base models and scales.

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Yafu Li, Zhilin Wang, Leyang Cui, Wei Bi, Shuming Shi, Yue Zhang

Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement (2025.emnlp-industry)

Challenge:	Existing work focuses on detecting (partially) AI-generated texts, but paraphrasing is commonly employed in various application scenarios for text refinement and diversity.
Approach:	They propose a framework for paraphrased text span detection that takes in the full text and assigns each sentence with a score indicating the paraphrasing degree.
Outcome:	The proposed framework can detect paraphrased text spans within a text . it takes in the full text and assigns each sentence with a score indicating the paraphrasing degree.

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Haotan Guo, Jianfei He, Jiayuan Ma, Hongbin Na, Zimu Wang, Haiyang Zhang, Qi Chen, Wei Wang, Zijing Shi, Tao Shen, Ling Chen

Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment (2021.acl-long)

Challenge:	Phonetic Cloaking Replacement (PCR) is a problem in content moderation in China.
Approach:	They organize PCR into a four-way surface-form taxonomy and compile PCR-ToxiCN, a dataset of 500 phonetically cloaked offensive posts gathered from the RedNote platform.
Outcome:	The proposed model achieves only an F1-score and zero-shot chain-of-thought prompting pushes performance even lower.

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Haoyue Shi, Luke Zettlemoyer, Sida I. Wang

Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient Evaluation (2025.acl-long)

Challenge:	Existing methods for bilingual lexicon induction are linear and require simplifying assumptions.
Approach:	They propose methods that combine unsupervised bitext mining and unsupervised word alignment to produce higher quality lexicons.
Outcome:	The proposed method outperforms the state-of-the-art on the BUCC 2020 task by 14 F1 points . further analysis suggests they are comparable quality .

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Peiwen Yuan, Yueqi Zhang, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Jiayi Shi, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li

From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)

Challenge:	Existing efficient methods estimate performance of models on large benchmarks, but these methods rely on the assumption that target models have high prediction consistency with source models.
Approach:	They propose a method that conducts customized evaluation tailored to each target model.
Outcome:	The proposed method reduces the MAE of estimates by 31.4% on benchmarks across 300 models.

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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong

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

Challenge:	Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages.
Approach:	They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English.
Outcome:	The proposed model outperforms open-source and Tibetan-focused models on diverse 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

AGENTVIGIL: Automatic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents (2025.findings-emnlp)

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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Zhun Wang, Vincent Siu, Zhe Ye, Tianneng Shi, Yuzhou Nie, Xuandong Zhao, Chenguang Wang, Wenbo Guo, Dawn Song

Boundary Detection with BERT for Span-level Emotion Cause Analysis (2021.findings-acl)

Challenge:	AGENTVIGIL is a black-box optimization framework to exploit indirect prompt injection vulnerabilities . indirect prompts compromise the core of LLM agents by manipulating contextual information rather than direct user prompts.
Approach:	They propose a black-box optimization framework to exploit indirect prompt injection vulnerabilities . they use a Monte Carlo tree-based algorithm to iteratively refine inputs .
Outcome:	The proposed framework achieves 71% and 70% success rates against two public benchmarks .

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Xiangju Li, Wei Gao, Shi Feng, Yifei Zhang, Daling Wang

Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation (2020.findings-emnlp)

Challenge:	Emotion cause analysis (ECA) is an emerging topic in natural language processing, which aims to identify the reasons behind a given emotion.
Approach:	They propose to detect the precise boundaries of text spans conveying accurate emotion causes from the given context by a sequence labeling and position identification problem.
Outcome:	The proposed methods outperform existing models on two benchmark datasets on the emotion cause analysis task.

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Shiyao Cui, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Xuebin Wang, Jinqiao Shi

DeepNote: Note-Centric Deep Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Event detection (ED) is a key subtask of information extraction.
Approach:	They propose an architecture that exploits syntactic structure and typed dependency label information to perform event detection.
Outcome:	The proposed architecture exploits syntactic structure and typed dependency label information to perform ED.

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Ruobing Wang, Qingfei Zhao, Yukun Yan, Daren Zha, Yuxuan Chen, Shi Yu, Zhenghao Liu, Yixuan Wang, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun

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

Challenge:	. - (EN)
Approach:	. - (EN)
Outcome:	. - (EN)

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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

iAgent: LLM Agent as a Shield between User and Recommender Systems (2025.findings-acl)

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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Wujiang Xu, Yunxiao Shi, Zujie Liang, Xuying Ning, Kai Mei, Kun Wang, Xi Zhu, Min Xu, Yongfeng Zhang

Challenge:	Traditional recommender systems focus on the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms.
Approach:	They propose a user-agent-platform paradigm where agent serves as the protective shield between user and recommender system that enables indirect exposure.
Outcome:	The proposed model improves 16.6% over baselines on four datasets and mitigates echo chamber effects and reduces model bias in disadvantaged users.

Causal Intervention for Abstractive Related Work Generation (2023.findings-emnlp)

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Jiachang Liu, Qi Zhang, Chongyang Shi, Usman Naseem, Shoujin Wang, Liang Hu, Ivor Tsang

Challenge:	Existing models ignore the inherent causality during related work generation, leading to spurious correlations which downgrade the models’ generation quality and generalizability.
Approach:	They propose a Causal Intervention Module for Related Work Generation (CaM) that captures causal relationships in related work generation and implements causal interventions to mitigate the negative impact of spurious correlations.
Outcome:	The proposed framework improves the quality and coherence of generated related work by capturing causalities in the generation process.

Identifying the Achilles’ Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models (2026.findings-acl)

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Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling, Shi Juluan, Youliang Yuan, Jen-tse Huang, Yifei Zhang, Wenxiang Jiao, Zhaopeng Tu, Michael R. Lyu

PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection (2023.findings-emnlp)

Challenge:	Current methods for evaluating LLMs’ veracity are limited by the need for extensive human labor, test data contamination, or limited scope, hindering efficient and effective exposure of errors.
Approach:	They propose a framework that extracts fact triplets to generate diverse question types using rule-based natural language processing techniques.
Outcome:	The proposed framework can trigger factual errors in up to 55% of questions in large LLMs while maintaining coverage of questions.

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Tao Yang, Tianyuan Shi, Fanqi Wan, Xiaojun Quan, Qifan Wang, Bingzhe Wu, Jiaxiang Wu

Pixel-Level Reasoning Segmentation via Multi-turn Conversations (2025.acl-long)

Challenge:	Recent advances in large language models (LLMs) have demonstrated remarkable zero-shot performance across various NLP tasks.
Approach:	They propose a method which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner and prompts an LLM to rate individual items at each turn.
Outcome:	The proposed method improves the performance and robustness of the standard GPT-3.5 personality detection task on two benchmark datasets.

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Dexian Cai, Xiaocui Yang, YongKang Liu, Daling Wang, Shi Feng, Yifei Zhang, Soujanya Poria

How to Build User Simulators to Train RL-based Dialog Systems (D19-1)

Challenge:	Existing visual perception systems focus on region-level segmentation in single-turn dialogues . existing systems cannot reason at the pixel level and comprehend dynamic user intent .
Approach:	They propose a task that tracks evolving user intent via multi-turn interactions for fine-grained segmentation.
Outcome:	The proposed method outperforms existing baselines in segmentation and reasoning metrics.

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Weiyan Shi, Kun Qian, Xuewei Wang, Zhou Yu

ReIFE: Re-evaluating Instruction-Following Evaluation (2025.naacl-long)

Challenge:	RL-based dialog systems require interaction with the environment and obtaining real human users to interact with the system is time-consuming and labor-intensive.
Approach:	They propose a method to standardize user simulator building to compare dialog system quality using the same set of user simulators.
Outcome:	The proposed method can be used by the community to compare dialog system quality using the same set of user simulators fairly.

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Yixin Liu, Kejian Shi, Alexander Fabbri, Yilun Zhao, PeiFeng Wang, Chien-Sheng Wu, Shafiq Joty, Arman Cohan

Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback (2024.findings-acl)

Challenge:	Existing evaluations of large language models (LLMs) for instruction following are incomplete.
Approach:	They propose to use 25 base LLMs and 15 recently proposed evaluation protocols to evaluate instruction following on 4 human-annotated datasets.
Outcome:	The proposed evaluations identify the best-performing base LLMs and evaluation protocols with a high degree of robustness.

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Zhangqian Bi, Yao Wan, Zheng Wang, Hongyu Zhang, Batu Guan, Fangxin Lu, Zili Zhang, Yulei Sui, Hai Jin, Xuanhua Shi

LM2Protein: A Structure-to-Token Protein Large Language Model (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) generate code for given contexts, such as incomplete code, class, data structure, or project-specific information.
Approach:	They propose a compiler feedback-based code generation approach that leverages static analysis to identify mismatches between the generated code and the project's context.
Outcome:	The proposed model outperforms retrieval-based code generation baselines and significantly outperfies the existing large language models.

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Chang Zhou, Yuheng Shan, Pengan Chen, Xiangyu Shi, Zikang Wang, Yanting Li, Jiyue Jiang

LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech (2026.acl-long)

Challenge:	RNA-binding proteins are critical for various molecular functions, relying on their precise tertiary structures.
Approach:	They propose a method to integrate protein 3D structural data within a sequence processing framework.
Outcome:	The proposed method achieves high sequence recovery in inverse folding and protein-conditioned RNA design.

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Bingshen Mu, Xian Shi, Xiong Wang, Hexin Liu, Jin Xu, Lei Xie

MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models (2026.acl-long)

Challenge:	Existing methods for forcing alignment are language-specific and prone to temporal shifts.
Approach:	They propose a slot-filling paradigm that uses time indices to predict slot positions.
Outcome:	The proposed method reduces accumulated temporal shifts by 69% compared with prior methods.

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Yang Shi, Yifeng Xie, Minzhe Guo, Liangsi Lu, Mingxuan Huang, Jingchao Wang, Zhihong Zhu, Boyan Xu, Zhiqi Huang

Data Swarms: Optimizable Generation of Synthetic Evaluation Data (2026.findings-acl)

Challenge:	Recent advances in vision-language models have improved performance in multi-modal learning.
Approach:	They propose a multi-modal benchmark that embeds a single coherent reasoning error in 1997 samples.
Outcome:	The proposed benchmark is based on a set of 1997 samples embedding a single coherent reasoning error.

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Shangbin Feng, Yike Wang, Weijia Shi, Yulia Tsvetkov

Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models (2025.tacl-1)

Challenge:	Extensive experiments demonstrate that Data Swarms outperforms eight data generation baselines across five evaluation objectives.
Approach:	They propose an algorithm to optimize the generation of synthetic evaluation data and advance quantitative desiderata of LLM evaluation.
Outcome:	The proposed algorithm outperforms baseline evaluations and Adversarial Swarms generates harder data while learning from such data.

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Jianhui Pang, Fanghua Ye, Derek Fai Wong, Dian Yu, Shuming Shi, Zhaopeng Tu, Longyue Wang

DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection (2022.emnlp-main)

Challenge:	a recent study revisits six core challenges that have influenced the evolution of Neural Machine Translation (NMT) domain mismatch, amount of parallel data, rare word prediction, translation of long sentences and sub-optimal beam search remain challenges in LLMs.
Approach:	They revisit core challenges that have acted as benchmarks for progress in NMT . they propose to revisit these challenges and offer insights into their relevance .
Outcome:	The proposed models significantly improve translation of sentences containing approximately 80 words, even translating documents up to 512 words.

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Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

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

Challenge:	Existing retrieval-based dialogue systems suffer from slow inference or huge number of parameters.
Approach:	They propose a lightweight fully convolutional architecture for response selection using convolution.
Outcome:	The proposed architecture extracts matching features of context and response from 3D views.

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Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu

RACE: Retrieval-augmented Commit Message Generation (2022.emnlp-main)

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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Ensheng Shi, Yanlin Wang, Wei Tao, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, Hongbin Sun

MdEval: Massively Multilingual Code Debugging (2026.findings-acl)

Challenge:	Existing approaches to automatically generate commit messages are repetitive or redundant.
Approach:	They propose a retrieval-augmented neural commit message generation method which treats the retrieved similar commit as an exemplar and leverages it to generate an accurate commit message.
Outcome:	The proposed method outperforms baselines on a large dataset with five programming languages and can boost existing Seq2Seq models in commit message generation.

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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li

LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoning (2025.emnlp-main)

Challenge:	Existing benchmarks primarily focus on Python and are limited in terms of language diversity.
Approach:	They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions.
Outcome:	The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task.

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Tianshi Zheng, Cheng Jiayang, Chunyang Li, Haochen Shi, Zihao Wang, Jiaxin Bai, Yangqiu Song, Ginny Wong, Simon See

Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization (2022.acl-long)

Challenge:	Modern large language models (LLMs) employ diverse logical inference mechanisms for reasoning.
Approach:	They analyze the comparative dynamics of inductive (System 1) versus abductive/deductive (system 2) inference in large language models by using a controlled analogical reasoning environment and a MCQ/free-text task format.
Outcome:	The proposed methods can significantly scale LLM reasoning.

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Ruipeng Jia, Xingxing Zhang, Yanan Cao, Zheng Lin, Shi Wang, Furu Wei

Benchmarking and Improving Long-Text Translation with Large Language Models (2024.findings-acl)

Challenge:	Existing methods to translate sentences to other languages using heuristics are challenging.
Approach:	They propose a model that learns hierarchical weights for different sets of labels and applies them to other languages to translate them.
Outcome:	The proposed model can translate English datasets to other languages and obtain different sets of labels again using heuristics.

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Longyue Wang, Zefeng Du, Wenxiang Jiao, Chenyang Lyu, Jianhui Pang, Leyang Cui, Kaiqiang Song, Derek Wong, Shuming Shi, Zhaopeng Tu

WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent (2026.findings-acl)

Challenge:	Recent studies have illuminated the promising capabilities of large language models (LLMs) in handling long texts.
Approach:	They construct a benchmark dataset specifically designed for the finetuning and evaluation of large language models (LLMs) they compare LLMs with MT models and find they exhibit shortcomings in long-text domains .
Outcome:	The proposed model performs better in long-text translation, and its performance diminishes as document size increases.

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Lingfeng Zhang, Yongan Sun, Jinpeng Hu, Hui Ma, Ying Yang, Kuien Liu, Zenglin Shi, Meng Wang

DR-HM: Distill-then-Reinforce Training with Cognition-Aware Data Synthesis for Harmful Meme Detection (2026.findings-acl)

Challenge:	Existing web agents struggle with complex tasks due to rigid planning strategies and hallucination-prone reasoning.
Approach:	They propose a task-uncertainty-driven Adaptive Planning Mechanism that adaptively selects planning modes to navigate unknown environments.
Outcome:	The proposed framework performs better on the WebArena and WebVoyager benchmarks than existing frameworks.

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Zihan Cheng, Jianxiang Ma, Xiaocui Yang, Peidong Wang, Wen Zhang, Shi Feng, Daling Wang, Yifei Zhang, Mingfu Zhang

InferenceDynamics: Adaptive LLM Routing through Structured Capability and Knowledge Profiling (2026.acl-long)

Challenge:	Current methods for harmful meme detection lack the knowledge required to identify such hate . current methods lack the ability to identify cultural stereotypes and visual metaphors .
Approach:	They propose a framework that decomposes meme analysis into a human-inspired reasoning process . they propose DR-HM to transfer knowledge from closed-source models while mitigating biases .
Outcome:	The proposed framework outperforms existing methods on three benchmark datasets.

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Haochen Shi, Tianshi Zheng, Weiqi Wang, Baixuan Xu, Chunyang Li, Chunkit Chan, Tao Fan, Yangqiu Song

Document-Level Machine Translation with Large Language Models (2023.emnlp-main)

Challenge:	Large Language Model (LLM) routing is a pivotal technique for navigating a diverse landscape of LLMs.
Approach:	They propose a flexible and scalable multi-dimensional routing framework that models the capability and knowledge of models.
Outcome:	The proposed framework can be used to generalize and identify top-performing models for group-level routing using modern benchmarks including MMLU-Pro, GPQA, BigGenBench, and LiveBench.

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Longyue Wang, Chenyang Lyu, Tianbo Ji, Zhirui Zhang, Dian Yu, Shuming Shi, Zhaopeng Tu

AHVE-CNER: Aligned Hanzi Visual Encoding Enhance Chinese Named Entity Recognition with Multi-Information (2025.coling-main)

Challenge:	Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing tasks.
Approach:	They examine the impact of different prompts on document-level translation quality and discourse phenomena using figures and lines, which are invisible to GPT-4.
Outcome:	The proposed models outperform commercial MT systems and advanced document-level MT methods on a number of benchmarks and show potential to become a new paradigm for document- level translation.

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Xuhui Zheng, Zhiyuan Min, Bin Shi, Hao Wang

BSCodec: A Band-Split Neural Codec for High-Quality Universal Audio Reconstruction (2026.findings-eacl)

Challenge:	Existing glyph-based models neglect the relationship between pictorial elements and radicals for Named Entity Recognition (NER) tasks.
Approach:	They propose a model that integrates multi-source visual and phonetic information of Hanzi . they propose combining pictographic features with radicals to facilitate integration .
Outcome:	The proposed model improves performance on benchmark datasets.

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Haoran Wang, Jiatong Shi, Jinchuan Tian, Bohan Li, Kai Yu, Shinji Watanabe

Learning to Ask: When LLM Agents Meet Unclear Instruction (2025.emnlp-main)

Challenge:	Neural audio codecs have enabled high-fidelity reconstruction of speech, music and sound . however, speech-optimized codec systems suffer degradation on music or sound if they ignore spectral differences .
Approach:	They propose a neural audio codec that splits the spectral dimension into separate bands and compresses each band independently.
Outcome:	Experimental results show that BSCodec achieves better reconstruction quality on music and sound compared to existing codecs.

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Wenxuan Wang, Shi Juluan, Zixuan Ling, Yuk-Kit Chan, Chaozheng Wang, Cheryl Lee, Youliang Yuan, Jen-tse Huang, Wenxiang Jiao, Michael R. Lyu

Challenge:	Despite their impressive capabilities, LLMs struggle with complex computations and delivering accurate, timely information.
Approach:	They propose a framework that prompts LLM agents to ask questions when they encounter obstacles due to unclear instructions and an automated evaluation tool called ToolEvaluator.
Outcome:	The proposed framework outperforms existing frameworks for tool learning in the Noisy ToolBench.

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)

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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong

Answer-focused and Position-aware Neural Question Generation (D18-1)

Challenge:	Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models.
Approach:	They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Outcome:	The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.

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Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma, Shi Wang

Natural Language to Code Translation with Execution (2022.emnlp-main)

Challenge:	Recent neural network-based approaches generate interrogative words that do not match the answer type.
Approach:	They propose an answer-focused and position-aware neural question generation model to address these issues.
Outcome:	The proposed model outperforms the baseline and outperformed the state-of-the-art system.

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Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, Sida I. Wang

R3: End-to-End Reasoning-based Planning for Multi-step Retrosynthesis via Reinforcement Learning (2026.acl-long)

Challenge:	Generative code models do not explicitly incorporate program semantics during training, but they are able to generate correct solutions for many problems.
Approach:	They introduce execution result-based minimum Bayes risk decoding for program selection . they select output programs from a generated candidate set by marginalizing over implementations that share the same semantics .
Outcome:	The proposed model outperforms all other methods on natural language-to-code translation.

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YiFei Wang, Qizhi Pei, Jiangtao Feng, Yuntian Shi, Yi Duan, Lihao Wang, Lei Bai, Lijun Wu, Wei-Ying Ma, Hao Zhou

ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling (2026.findings-eacl)

Challenge:	Experimental results show that R3 is a superior alternative to traditional search algorithms for multistep retrosynthesis planning.
Approach:	They propose a framework that reformulates multistep retrosynthetic planning as a generative reasoning task.
Outcome:	The proposed framework achieves state-of-the-art Top-1 accuracy of 43.7% on retrobench . it leverages Large Language Models to reformulate multistep retrosynthesis as a generative reasoning task.

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Zhipeng Xu, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng, Chaojun Xiao, Zhiyuan Liu, Ge Yu, Chenyan Xiong

Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense (L18-1)

Challenge:	Large Language Models (LLMs) exhibit suboptimal behaviors and inconsistencies when exposed to unfamiliar external information, underscoring their limitations in effectively leveraging such knowledge.
Approach:	They propose a framework that enhances the external knowledge utilization of Large Language Models through a two-stage constructivist cognitive modeling process.
Outcome:	The proposed framework achieves a 10% improvement over baseline methods on various question-answering benchmarks.

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Haoyue Shi, Xihao Wang, Yuqi Sun, Junfeng Hu

Representation Learning

Challenge:	Existing word embedding frameworks distinguish different senses of words by their contexts.
Approach:	They propose a framework for unsupervised corpus sense tagging which trains multi-sense word embeddings on a given corpus.
Outcome:	The proposed framework detects pseudo multi-senses without extra language resources without additional language resources.

AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage (2026.acl-long)

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Xuanle Zhao, Zilin Sang, Yuxuan Li, Qi Shi, Weilun Zhao, Shuo Wang, Duzhen Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

Challenge:	Efficient reproduction of research papers requires deep domain expertise.
Approach:	They propose a framework that systematically mines implicit knowledge from the cited literature to reproduce experimental code in a complete, end-to-end manner.
Outcome:	The proposed framework surpasses baselines across all metrics and reproduces experimental code in a complete, end-to-end manner.

ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM (2025.findings-emnlp)

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Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo, Bingjing Xu, Weiwei Deng, Qingwei Lin, Dongmei Zhang

Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation (2022.acl-long)

Challenge:	In-context learning (ICL) is a common practice to enhance LLM performance on domain-specific tasks.
Approach:	They propose a method that leverages large language models to enhance query-ad relevance labeling . they identify and provide superior demonstrations for ICL, thereby improving labeling performance .
Outcome:	The proposed method improves query-ad relevance labeling performance by providing demonstrations.

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Zhiwei He, Xing Wang, Rui Wang, Shuming Shi, Zhaopeng Tu

TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)

Challenge:	Experimental results show that backtranslation improves UNMT performance by reducing the data gap between training and inference.
Approach:	They propose an online method to remedy the source discrepancy between training and inference . they use pseudo parallel data with translated source and translated target to mimic inference scenario .
Outcome:	The proposed method outperforms baselines on several widely-used language pairs by remedying the style and content gaps.

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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang

Dynamic Prefix-Tuning for Generative Template-based Event Extraction (2022.acl-long)

Challenge:	Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools.
Approach:	They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems.
Outcome:	The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems.

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Xiao Liu, Heyan Huang, Ge Shi, Bo Wang

SWE-QA: Can Language Models Answer Repository-level Code Questions? (2026.findings-acl)

Challenge:	Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005.
Approach:	They propose a generative template-based event extraction method with dynamic prefix . they integrate context information with type-specific prefixes to learn a context-specific name for each context .
Outcome:	The proposed method achieves competitive results with state-of-the-art model OneIE on ACE 2005 and performs well on ERE.

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Weihan Peng, Yuling Shi, Yuhang Wang, Xinyun Zhang, Beijun Shen, Xiaodong Gu

Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives (2025.findings-acl)

Challenge:	Existing benchmarks for understanding and reasoning about entire soft-ware repositories focus on small, self-contained code snippets.
Approach:	They propose a repository-level code question answering benchmark to facilitate research on automated QA systems in real-world repositories.
Outcome:	The proposed benchmarks are designed to facilitate research on automated QA systems in real-world repositories.

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Zhihu Wang, Shiwan Zhao, Yu Wang, Heyuan Huang, Sitao Xie, Yubo Zhang, Jiaxin Shi, Zhixing Wang, Hongyan Li, Junchi Yan

Challenge:	Existing approaches to solving complex tasks with large language models (LLMs) fail to decompose tasks accurately or execute subtasks effectively.
Approach:	They propose a Chain-of-Learning (CoL) paradigm that highlights task dependencies on specific capability items, further broken down into their constituent knowledge and skill components.
Outcome:	The proposed model improves Yi-1.5-9B and Llama3-Chinese-8B for legal tasks by 45.00% and 24.50% on different domains.

TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos (2025.acl-long)

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Fanheng Kong, Jingyuan Zhang, Hongzhi Zhang, Shi Feng, Daling Wang, Linhao Yu, Xingguang Ji, Yu Tian, V. W., Fuzheng Zhang

Towards Objective Fine-tuning: How LLMs’ Prior Knowledge Causes Potential Poor Calibration? (2025.acl-long)

Challenge:	Existing benchmarks for video understanding often focus on specific aspects, overlooking the holistic nature of video content.
Approach:	They propose a temporal-oriented benchmark for fine-grained understanding on dense dynamic videos with two complementary tasks: captioning and QA.
Outcome:	The proposed model performs well on diverse video scenarios and dynamic videos, with interpretable and robust evaluation criteria.

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Ziming Wang, Zeyu Shi, Haoyi Zhou, Shiqi Gao, Qingyun Sun, Jianxin Li

GuoFeng: A Benchmark for Zero Pronoun Recovery and Translation (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) have enabled powerful domain-specific applications through supervised fine-tuning.
Approach:	They propose a cognition-aware framework that applies targeted learning strategies according to the model’s prior knowledge to improve calibration.
Outcome:	The proposed framework significantly improves calibration while maintaining performance, achieving an average 57% reduction in ECE compared to standard fine-tuning in Llama3-8B.

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Mingzhou Xu, Longyue Wang, Derek F. Wong, Hongye Liu, Linfeng Song, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

ToNER: Type-oriented Named Entity Recognition with Generative Language Model (2024.lrec-main)

Challenge:	ZPs are often omitted when they can be pragmatically or grammatically inferred from intraand inter-sentential contexts.
Approach:	They propose a benchmark testset for target evaluation on Chinese-English ZP translation.
Outcome:	The proposed testset covers five genres and identifies current challenges for evaluation.

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Guochao Jiang, Ziqin Luo, Yuchen Shi, Dixuan Wang, Jiaqing Liang, Deqing Yang

On the Copying Behaviors of Pre-Training for Neural Machine Translation (2021.findings-acl)

Challenge:	Input too many potential entity types would distract the model inevitably.
Approach:	They propose to use a generative model to exploit entity types' merit on promoting NER task by appending a type matching model to identify the entity types most likely to appear in the sentence.
Outcome:	The proposed framework exploits entity types' merit on promoting NER task by adding auxiliary task to the model to discover the entity types.

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Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

Deep Differential Amplifier for Extractive Summarization (2021.acl-long)

Challenge:	Existing studies show that initializing NMT models with pre-trained language models (LM) can speed up the model training and boost the model performance.
Approach:	They propose a method to control copying behaviors in NMT models by initializing them with pre-trained language models (LM) they propose to use a metric called copy ratio to control the copying behavior in decoding.
Outcome:	The proposed method improves translation performance by controlling copying behaviors for pre-training based models.

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Ruipeng Jia, Yanan Cao, Fang Fang, Yuchen Zhou, Zheng Fang, Yanbing Liu, Shi Wang

LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding (2025.emnlp-main)

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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Yuxuan Hu, Jihao Liu, Ke Wang, Jinliang Zheng, Weikang Shi, Manyuan Zhang, Qi Dou, Rui Liu, Aojun Zhou, Hongsheng Li

Redistributing Low-Frequency Words: Making the Most of Monolingual Data in Non-Autoregressive Translation (2022.acl-long)

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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Liang Ding, Longyue Wang, Shuming Shi, Dacheng Tao, Zhaopeng Tu

Decoding in Latent Spaces for Efficient Inference in LLM-based Recommendation (2025.findings-emnlp)

Challenge:	Knowledge distillation (KD) is the preliminary step for training non-autoregressive translation models, but it can lose important information for translating low-frequency words.
Approach:	They propose a knowledge distillation method which trains NAT student on external monolingual data with AT teacher trained on the original bilingual data.
Outcome:	Extensive experiments on eight WMT benchmarks show that monolingual KD outperforms the standard KD by improving low-frequency word translation without introducing any computational cost.

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Chengbing Wang, Yang Zhang, Zhicheng Wang, Tianhao Shi, Keqin Bao, Fuli Feng, Tat-Seng Chua

Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement (2022.coling-1)

Challenge:	Light Latent-space Decoding (L2D) is an efficient and efficient latent- space decoding method.
Approach:	They propose to bypass language-space decoding by matching candidate items with LLM's internal thought representations in the latent space.
Outcome:	The proposed method is 10x faster than language-space decoding while maintaining or enhancing performance.

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Dongshi Ju, Shi Feng, Pengcheng Lv, Daling Wang, Yifei Zhang

RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis (2025.emnlp-main)

Challenge:	Existing methods suffer from incomprehensive persona tags that have unique and obscure meanings to describe human’s personality.
Approach:	They propose a graph convolution network model with addressee selecting mechanism that integrates personas, dialogue utterances, and external text knowledge in a unified graph.
Outcome:	The proposed model outperforms baselines by large margins and improves persona consistency in the generated responses.

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Jianwei Wang, Chengming Shi, Junyao Yang, Haoran Li, Qianli Ma, Huiping Zhuang, Cen Chen, Ziqian Zeng

Better Language Model with Hypernym Class Prediction (2022.acl-long)

Challenge:	Existing solutions to fine-tune large language models for domain-specific tasks are ineffective in addressing privacy concerns.
Approach:	They propose a privacy-preserving framework that fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation.
Outcome:	The proposed framework fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation.

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He Bai, Tong Wang, Alessandro Sordoni, Peng Shi

MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification (2026.findings-acl)

Challenge:	Class-based language models (LMs) have been devised to address context sparsity in n-gram LMs for decades.
Approach:	They propose to use class-based prediction to improve generalization for rare words by annealing from predicting the class to token prediction during training.
Outcome:	The proposed model improves perplexity without sacrificing performance on rare words.

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Jingwei Song, Xinyu Wang, Hanbin Wang, Xiaoxuan Lei, Tianyu Shi, Shixin Han, Eric Yang, Xiao-Wen Chang, Lynn Ai

Challenge:	Autoregressive large language models suffer from high inference latency due to memorybandwidth constraints.
Approach:	They propose a method that decouples generation and verification by decoupling tokens and a lightweight draft model.
Outcome:	The proposed method delivers consistent and significant speedups over state-of-the-art baselines while preserving generation quality across diverse benchmarks.

KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features (2022.coling-1)

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Minghao Xu, Daling Wang, Shi Feng, Zhenfei Yang, Yifei Zhang

TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild (2024.findings-acl)

Challenge:	Existing research results on explicit sentiment analysis are limited . implicit sentiment analysis is a process of analyzing text based on whether it contains explicit sentiment words.
Approach:	They propose a model that integrates external knowledge and contextual features . they use a knowledge graph to supplement implicit sentiment expression .
Outcome:	The proposed model can achieve better results on the SMP2019 implicit sentiment analysis dataset.

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Huayang Li, Siheng Li, Deng Cai, Longyue Wang, Lemao Liu, Taro Watanabe, Yujiu Yang, Shuming Shi

AMR-TST: Abstract Meaning Representation-based Text Style Transfer (2023.findings-acl)

Challenge:	Large language models with instruction-following capabilities have revolutionized the field of artificial intelligence.
Approach:	They propose an annotation-free framework for empowering large language models with instruction-following capabilities.
Outcome:	The proposed framework generates multi-turn multimodal instruction-response conversations from a language model.

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Kaize Shi, Xueyao Sun, Li He, Dingxian Wang, Qing Li, Guandong Xu

GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? (2025.emnlp-main)

Challenge:	Abstract Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input.
Approach:	They propose an AMR-based text style transfer technique that converts source text to an AML graph and generates transferred text based on the AMR graph modified by a TST policy named style rewriting.
Outcome:	The proposed method achieves state-of-the-art results compared with baseline models in automatic and human evaluations.

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Yiyang Zhou, Linjie Li, Shi Qiu, Zhengyuan Yang, Yuyang Zhao, Siwei Han, Yangfan He, Kangqi Li, Haonian Ji, Zihao Zhao, Haibo Tong, Lijuan Wang, Huaxiu Yao

On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (2021.findings-emnlp)

Challenge:	Existing video benchmarks often resemble image-based questions with scans of only a few key frames, without deep temporal reasoning.
Approach:	They propose a video benchmark to assess whether large vision-language models can genuinely think with videos rather than perform superficial frame-level analysis.
Outcome:	The proposed benchmark consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection.

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Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function (2023.acl-long)

Challenge:	Experimental results show that PT and BT are nicely complementary to each other.
Approach:	They introduce two probing tasks for PT and BT respectively and investigate their complementarity.
Outcome:	The proposed methods establish state-of-the-art on the WMT16 English-Romanian and English-Russian benchmarks.

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Haiming Wang, Ye Yuan, Zhengying Liu, Jianhao Shen, Yichun Yin, Jing Xiong, Enze Xie, Han Shi, Yujun Li, Lin Li, Jian Yin, Zhenguo Li, Xiaodan Liang

Context-Aware Tracking and Dynamic Introduction for Incomplete Utterance Rewriting in Extended Multi-Turn Dialogues (2024.findings-acl)

Challenge:	Recent advances in neural theorem-proving resort to large language models and tree searches.
Approach:	They propose a Dynamic-Tree Driven Theorem Solver to accommodate general theoremes by guiding the search procedure with state confidence and proof-level values.
Outcome:	The proposed method outperforms state-of-the-art methods on two popular theorem-proving datasets with a 6.65% improvement on average in terms of success rate.

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Xinnan Guo, Qian Zhu, Qiuhui Shi, Xuan Lin, Liubin Wang, DaqianLi DaqianLi, Yongrui Chen

Non-Autoregressive Math Word Problem Solver with Unified Tree Structure (2023.emnlp-main)

Challenge:	Existing methods to reconstruct utterance with omitted information and pronouns are limited to brief multi-turn dialogues.
Approach:	They propose a method to reconstruct utterance with omitted information and pronouns to be standalone and complete based on context.
Outcome:	The proposed method improves existing models and achieves state-of-the-art on three benchmarks.

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Yi Bin, Mengqun Han, Wenhao Shi, Lei Wang, Yang Yang, See-Kiong Ng, Heng Shen

Numerical Reasoning

Challenge:	Existing MWP solvers do not handle variants that can be derived via mathematical manipulation.
Approach:	They propose a non-autoregressive solver to present a solution expression and decode it from a given problem description.
Outcome:	The proposed solver is able to decode multiple expression variants and correct them . it is based on a unified tree structure and is available on Math23K and MAWPS.

CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation (2024.lrec-main)

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Yujie Shao, Xinrong Yao, Xingwei Qu, Chenghua Lin, Shi Wang, Wenhao Huang, Ge Zhang, Jie Fu

Challenge:	Metaphors are a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication.
Approach:	They propose a large-scale high quality annotated Chinese Metaphor Corpus . they use a set of guidelines to ensure the accuracy and consistency of their annotations .
Outcome:	The proposed corpus generates metaphors that resonate more with real-world intuition.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation (2025.acl-industry)

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Zixuan Wang, Jinghao Shi, Hanzhong Liang, Xiang Shen, Vera Wen, Zhiqian Chen, Yifan Wu, Zhixin Zhang, Hongyu Xiong

Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding (2022.emnlp-main)

Challenge:	Effective content moderation is essential for video platforms to safeguard user experience and uphold community standards.
Approach:	They propose a method to transform a generative MLLM into a multimodal classifier using minimal discriminative training data.
Outcome:	The proposed method improves F1 score by 66.50% over traditional classifiers while requiring only 2% of the fine-tuning data.

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Jianing Wang, Wenkang Huang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Xiang Li, Ming Gao

McQueen: a Benchmark for Multimodal Conversational Query Rewrite (2022.emnlp-main)

Challenge:	Existing knowledge-enhanced pre-trained language models (PLMs) introduce redundant factual knowledge from knowledge bases and require complex modules.
Approach:	They propose a knowledge prompting-based PLM framework that incorporates factual knowledge into PLMs.
Outcome:	The proposed framework can be flexibly combined with existing mainstream PLMs.

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Yifei Yuan, Chen Shi, Runze Wang, Liyi Chen, Feijun Jiang, Yuan You, Wai Lam

The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection (2020.emnlp-main)

Challenge:	Recent studies have focused on conversational-related tasks that involve drawing information from more than one modality.
Approach:	They propose a task of multimodal conversational query rewrite which performs query . they collect a large-scale visual conversation dataset and benchmark it against other tasks .
Outcome:	The proposed task performs on a large-scale visual conversation dataset . it eliminates coreference and ellipsis in the original query without changing its semantic information.

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Zibo Lin, Deng Cai, Yan Wang, Xiaojiang Liu, Haitao Zheng, Shuming Shi

One Embedder, Any Task: Instruction-Finetuned Text Embeddings (2023.findings-acl)

Challenge:	Existing approaches to learning-to-rank response selection are suboptimal due to ignorance of diversity of response quality.
Approach:	They propose to use off-the-shelf response retrieval models as automatic grayscale data generators to train response selection models.
Outcome:	The proposed approach can be automated without human effort on grayscale data.

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Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, Tao Yu

EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction (2022.naacl-main)

Challenge:	a new method for embedding text is developed for tasks that require specialized encoders . INSTRUCTOR is a single embedder that can generate text embeddables tailored to different tasks and domains based on instruction finetuning .
Approach:	They introduce a new method for computing text embeddings given task instructions . they first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture .
Outcome:	The proposed method improves on 70 embedding evaluation tasks with fewer parameters than the previous best model.

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Benfeng Xu, Quan Wang, Yajuan Lyu, Yabing Shi, Yong Zhu, Jie Gao, Zhendong Mao

Distorted or Fabricated? A Survey on Hallucination in Video LLMs (2026.findings-acl)

Challenge:	Existing studies only explore entity representations, but propose a novel triple perspective for relation extraction.
Approach:	They propose to explicitly introduce relation representation and jointly represent it with entities to identify valid triples.
Outcome:	The proposed method is based on ablations and document-level relation extraction and joint entity and relation extraction.

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Yiyang Huang, Yitian Zhang, Yizhou Wang, Mingyuan Zhang, Liang Shi, Huimin Zeng, Yun Fu

MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)

Challenge:	Despite significant advances in video-language modeling, hallucinations remain a persistent challenge in video large language models.
Approach:	They present a systematic taxonomy that categorizes hallucinations into two core types: dynamic distortion and content fabrication.
Outcome:	The proposed taxonomy categorizes hallucinations into two core types: dynamic distortion and content fabrication.

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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

HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation (2022.acl-long)

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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Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, Dongmei Zhang

JX4MEI: Multimodal Semantically-Enhanced LLM for Joint Multimodal Emotion-Intent Explanation and Classification (2026.findings-acl)

Challenge:	Existing studies on table reasoning focus on flat tables and hierarchical tables . a new dataset, HiTab, aims to examine numerical reasoning over hierarchic tables based on hierarchically structured tables - a strong challenge for existing baselines and a valuable benchmark for future research.
Approach:	They propose a hierarchical question answering and natural language generation dataset to study hierarchic tables.
Outcome:	The proposed model shows that it is effective in QA and natural language generation over hierarchical tables.

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YiJie Huang, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, Ning Yuan, Zhuoyue Jia, Wen Zhang

AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation (2026.findings-acl)

Challenge:	Existing multimodal emotion and intent recognition tasks focus on classification, not rationale and intrinsic connections between these states.
Approach:	They propose a task that requires models to jointly predict emotion and intent while generating natural language explanations for why they co-occur.
Outcome:	The proposed model outperforms baseline models in prediction and explanation generation.

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Wentao Shi, Yu Wang, Yuyang Zhao, Yuxin Chen, Fuli Feng, Xueyuan Hao, Xi Su, Qi GU, Hui Su, Xunliang Cai, Xiangnan He

Challenge:	Existing approaches to verify agent behaviors in complex environments rely on rule-based verifiers or LLM-as-a-Judge models.
Approach:	They propose a benchmark to evaluate Agent-as-a-Judge across three domains . the benchmark covers search, data systems, and graphical user interfaces - with 155 tasks and 516 trajectories .
Outcome:	The proposed benchmark outperforms existing benchmarks in search, data systems, and GUI domains while revealing open challenges in agent-based verification.

Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition (2026.findings-acl)

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Rujing Yao, Yufei Shi, Yang Wu, Ang Li, Zhuoren Jiang, XiaoFeng Wang, Haixu Tang, Xiaozhong Liu

LegalChainReasoner: Grounding Criminal Judicial Opinion Generation via Structured Legal Chains (2026.acl-long)

Challenge:	Cloud-hosted Large Language Models (LLMs) offer unmatched reasoning capabilities and dynamic knowledge, yet submitting raw queries to these services can expose sensitive user intent.
Approach:	They propose a framework that formulates the trade-off between knowledge utility and privacy as a strategic game.
Outcome:	The proposed framework reduces intent leakage while maintaining high-fidelity answer quality.

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Weizhe Shi, Qiqi Wang, Yihong Pan, Qian Liu, Kaiqi Zhao

Graph of Trace: Visualizing Execution Traces of Scientific Agents (2026.acl-demo)

Challenge:	Current legalAI tasks divide sentencing and legal reasoning into two separate tasks, resulting in inconsistency between the reasoning and predictions.
Approach:	They propose a new task that generates both legal reasoning and sentencing decisions using a framework that applies structured legal chains to guide the model through comprehensive case assessments.
Outcome:	The proposed model outperforms baseline models on real-world, open-source Chinese legal case datasets.

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Tianci Gao, Haoxuan Li, Jian He Li, Tianxiang Zhao, Shi Runze, Weiran Wang, Zezhao Wu, Lu Mi

CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees (2021.emnlp-main)

Challenge:	Scientific AI agents can perform complex research tasks, but these unfolded workflows are difficult for humans to inspect and review, limiting interpretable, controllable and effective human–AI collaboration.
Approach:	They propose a monitoring and visualization framework that records fine-grained execution events and organizes them into a directed graph that makes agent workflows explicit as they proceed.
Outcome:	The proposed framework records intermediate steps (e.g. tool calls and code executions) and renders them as real-time updated visual traces that expose workflow structure.

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Ensheng Shi, Yanlin Wang, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, Hongbin Sun

An Empirical Study of Many-to-Many Summarization with Large Language Models (2025.acl-long)

Challenge:	Existing methods for code summarization do not capture rich information in ASTs . existing methods are labor-intensive and time-consuming to document code with good summaries manually.
Approach:	They propose a model that hierarchically splits and reconstructs ASTs by a neural network . they propose to use AST embeddings and a vanilla code token encoder to generate the model .
Outcome:	The proposed model splits and reconstructs ASTs into subtrees and then aggregates embeddings of subtreas to get the complete AST.

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Jiaan Wang, Fandong Meng, Zengkui Sun, Yunlong Liang, Yuxuan Cao, Jiarong Xu, Haoxiang Shi, Jie Zhou

Faster MoE LLM Inference for Extremely Large Models (2026.findings-acl)

Challenge:	Recent studies have shown that large language models (LLMs) have strong multilingual abilities, giving them the potential to perform M2MS in real applications.
Approach:	They propose to use many-to-many summarization (M2MS) to generate a brief summary in any language given a document also in any other language.
Outcome:	The proposed model outperforms zero-shot LLMs in terms of automatic evaluations.

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Haoqi Yang, Luohe Shi, Qiwei Li, Zuchao Li, Ping Wang, Hao Huang, Hai Zhao

Towards Understanding Neural Machine Translation with Word Importance (D19-1)

Challenge:	Existing inference optimizations for coarse-grained Mixture-of-Experts models implicitly assume a fixed activation budget, which is poorly understood.
Approach:	They propose a training-free policy that adapts token-level activation using router confidence and entropy while remaining within the model’s original budget.
Outcome:	The proposed skipping policy can provide substantial throughput gains, but optimal static schedules vary significantly across models and routing mechanisms.

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Shilin He, Zhaopeng Tu, Xing Wang, Longyue Wang, Michael Lyu, Shuming Shi

ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework (2026.acl-long)

Challenge:	Neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, but the interpretability of NMT remains unsatisfactory.
Approach:	They propose to attribute NMT output to every input word using a gradient-based method to measure word importance.
Outcome:	The proposed method is superior on identifying input words with higher influence on translation performance.

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Kai Qin, Liangxin Liu, Yu Liang, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Houde Liu, Daiting Shi

Challenge:	Existing methods for generating reward models focus on outcome-level supervision, neglecting analytical process quality, which constrains their potential.
Approach:	They propose a novel reward model that leverages self-reflection to assess analytical quality and enhance preference modeling.
Outcome:	The proposed model improves performance on four benchmarks and significantly mitigates positional bias.

From Parameters to Performance: A Data-Driven Study on LLM Structure and Development (2025.emnlp-main)

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Suqing Wang, Zuchao Li, Shi Luohe, Bo Du, Hai Zhao, Yun Li, Qianren Wang

Challenge:	Large language models have revolutionized a wide range of domains, driving significant advancements in both technology and real-world applications.
Approach:	They present a large-scale dataset encompassing diverse open-source LLM structures and their performance across multiple benchmarks.
Outcome:	The proposed model validates the relationship between structural configurations and performance across multiple benchmarks and further corroborates the findings using mechanistic interpretability techniques.

Pre-training with Meta Learning for Chinese Word Segmentation (2021.naacl-main)

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Zhen Ke, Liang Shi, Songtao Sun, Erli Meng, Bin Wang, Xipeng Qiu

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization (2025.findings-emnlp)

Challenge:	Recent studies show that pre-trained models are beneficial to Chinese Word Segmentation (CWS). However, these models lack task-specific prior segmentation knowledge.
Approach:	They propose a pre-trained Chinese word segmentation model MetaSeg which incorporates meta learning into a multi-criteria pre-training task.
Outcome:	Empirical results show that MetaSeg can achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in low-resource settings.

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Zhensheng Jin, Xinze Li, Yifan Ji, Chunyi Peng, Zhenghao Liu, Qi Shi, Yukun Yan, Shuo Wang, Furong Peng, Ge Yu

Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory (N19-1)

Challenge:	Existing methods to train LLMs suffer from overthinking, leading to lengthy reasoning traces . Existing approaches to train large language models suffer from this problem .
Approach:	They propose a method to combine multiple reasoning chains for training LLMs . they use stepwise exploration and long-short switched sampling to evaluate reasoning paths .
Outcome:	The proposed method reduces reasoning lengths by approximately 30-50% . it also maintains or improves reasoning accuracy compared to baselines .

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Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Wai Lam, Shuming Shi

Alignment with Fill-In-the-Middle for Enhancing Code Generation (2025.emnlp-main)

Challenge:	Existing generative dialogue models generate responses from input queries . however, the results are limited and the models are unsatisfactory .
Approach:	They propose a framework which exploits retrieval results via a skeleton-to-response paradigm . they extract a query skelet and use it to generate a new skele and response .
Outcome:	The proposed approach significantly improves the informativeness of the generated responses.

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Houxing Ren, Zimu Lu, Weikang Shi, Haotian Hou, Yunqiao Yang, Ke Wang, Aojun Zhou, Junting Pan, Mingjie Zhan, Hongsheng Li

Exploiting Sentential Context for Neural Machine Translation (P19-1)

Challenge:	Existing methods for generating test cases with limited training data are not reliable and may be counterproductive.
Approach:	They propose a method that splits code snippets into smaller, granular blocks, creating more diverse DPO pairs from the same test cases.
Outcome:	The proposed approach shows significant improvements in code generation tasks on benchmark datasets such as HumanEval (+), MBPP (+), and APPS.

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Xing Wang, Zhaopeng Tu, Longyue Wang, Shuming Shi

Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks (2021.acl-long)

Challenge:	Existing approaches to exploit sentential context for machine translation are not well studied.
Approach:	They propose a shallow sentential context that exploits top encoder layer, and a deep sentential one that aggregates sentential representations from all internal layers.
Outcome:	The proposed model outperforms the strong Transformer model on the English-German and English-French benchmarks.

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Xiaocui Yang, Shi Feng, Yifei Zhang, Daling Wang

Accelerating Code Search with Deep Hashing and Code Classification (2022.acl-long)

Challenge:	Existing studies only considered the representation of a single image-text post . Fig. 1 shows that multimodal sentiment expressions have global characteristics .
Approach:	They propose a multi-channel Graph Neural Networks with Sentiment-awareness approach for image-text sentiment detection.
Outcome:	The proposed approach is effective for image-text sentiment detection on three publicly available datasets.

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Wenchao Gu, Yanlin Wang, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, Michael Lyu

Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)

Challenge:	Code search is to search reusable code snippets from source code corpus based on natural languages queries.
Approach:	They propose a method to accelerate code search with deep hashing and code classification by using deep hashes and code hash.
Outcome:	The proposed method can save 90% of retrieval time while preserving at least 99% of retrievals accuracy.

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Xuan Wang, Shuxiang Cao, Yuchen Zhuang, Wenqi Shi

Vulnerability of Text-to-Image Models to Prompt Template Stealing: A Differential Evolution Approach (2025.findings-acl)

Challenge:	Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems .
Approach:	This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving .
Outcome:	This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance .

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Yurong Wu, Fangwen Mu, Qiuhong Zhang, Jinjing Zhao, Xinrun Xu, Lingrui Mei, Yang Wu, Lin Shi, Junjie Wang, Zhiming Ding, Yiwei Wang

The Agent’s First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios (2026.findings-acl)

Challenge:	Prompt trading has emerged as a significant intellectual property concern in recent years, where vendors entice users by showcasing sample images before selling prompt templates that can generate similar images.
Approach:	They propose a prompt-stealing benchmark consisting of 50 templates and 450 images organized into Easy and Hard difficulty levels.
Outcome:	The proposed method outperforms baseline methods with an average improvement of over 10%.

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Daocheng Fu, Jianbiao Mei, Rong Wu, Xuemeng Yang, Jia Xu, Ding Wang, Pinlong Cai, Yong Liu, Licheng Wen, Botian Shi

Challenge:	Existing research mainly focuses on performance upper bounds in static environments, overlooking stochastic real-world deployment.
Approach:	They propose a dynamic evaluation environment that simulates a "trainee" agent continuously exploring a novel setting.
Outcome:	The proposed model evaluates agents in a dynamic evaluation environment that simulates a "trainee" agent continuously exploring a novel setting.

Unlocking the Power of Large Language Models for Entity Alignment (2024.acl-long)

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Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Zixuan Li, Jian Guo, Huawei Shen, Yuanzhuo Wang

Enhancing Document Ranking with Task-adaptive Training and Segmented Token Recovery Mechanism (2021.emnlp-main)

Challenge:	Entity Alignment (EA) is a crucial step in unifying data from heterogeneous sources and plays a critical role in data-driven AI applications.
Approach:	They propose a framework that incorporates large language models to improve EA.
Outcome:	The proposed framework incorporates large language models (LLMs) to improve EA accuracy while preserving efficiency.

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Xingwu Sun, Yanling Cui, Hongyin Tang, Fuzheng Zhang, Beihong Jin, Shi Wang

Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations (2022.coling-1)

Challenge:	Document Retrieval (DR) requires the machine to retrieve and rank documents according to their relevance with the query.
Approach:	They propose a ranking model DR-BERT which improves the Document Retrieval task by a task-adaptive training process and a Segmented Token Recovery Mechanism.
Outcome:	The proposed ranking model keeps in the top three on the MS MARCO leaderboard since 2020.

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Jinfa Yang, Xianghua Ying, Yongjie Shi, Xin Tong, Ruibin Wang, Taiyan Chen, Bowei Xing

SpindleKV: A Novel KV Cache Reduction Method Balancing Both Shallow and Deep Layers (2025.acl-long)

Challenge:	Existing knowledge graph embedding models fail to model semantic hierarchies . Existing methods fail to understand the semantic hierarchies of knowledge graphs .
Approach:	They propose a model which embeds entities as pure quaternions and constrains the modulus of entities to make them have hierarchical distributions.
Outcome:	The proposed model can encode symmetry/antisymmetry, inversion, composition, multiple relation patterns and learn semantic hierarchies simultaneously.

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Zicong Tang, Shi Luohe, Zuchao Li, Baoyuan Qi, Liu Guoming, Lefei Zhang, Ping Wang

Challenge:	Large language models (LLMs) have impressive capabilities across various fields, but their widespread use is facing a severe and realistic challenge, which is their high demand for GPU memory.
Approach:	They propose a KV cache reduction method which balances both shallow and deep layers by using an attention weight based eviction method and a codebook based replacement approach.
Outcome:	The proposed method reduces the KV cache for shallower layers while preserving similar or even better model performance.

AwarenessBench: Assessing Cognitive Capabilities of Language Models (2026.acl-long)

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Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu

ConsistRM: Improving Generative Reward Models via Consistency-Aware Self-Training (2026.acl-long)

Challenge:	Language models exhibit increasingly consciousness-like behaviors, requiring a baseline to evaluate their cognitive abilities.
Approach:	They propose a benchmark to assess the cognitive abilities of language models (LMs) they compare 18 state-of-the-art LMs to human models in metacognition, self-awareness, social awareness and situational awareness .
Outcome:	Evaluating 18 state-of-the-art LMs, they find they consistently surpass baselines . but most models fall short in metacognition and self-awareness, the study finds .

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Yu Liang, Liangxin Liu, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Daiting Shi

Challenge:	ConsistRM is a self-training framework that enables effective and stable GRM training without human annotations.
Approach:	They propose a self-training framework that enables effective and stable GRM training without human annotations.
Outcome:	The proposed framework outperforms vanilla Reinforcement Fine-Tuning (RFT) by 1.5% on five benchmark datasets.

From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning (2025.findings-emnlp)

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Zihan Chen, Song Wang, Xingbo Fu, Chengshuai Shi, Zhenyu Lei, Cong Shen, Jundong Li

Low-Resource NLP

Challenge:	In-context learning (ICL) enables large language models to perform novel tasks without parameter updates by conditioning on a few input-output examples.
Approach:	They propose a cost-efficient two-stage pipeline that reduces reliance on LLMs for data labeling.
Outcome:	The proposed pipeline reduces reliance on LLMs for data labeling . it leverages readily available cross-task examples to prompt an LLM and pseudo-label a small set of target task instances.

TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use (2025.findings-emnlp)

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Junjie Ye, Yilong Wu, Sixian Li, Yuming Yang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan, Zhengyin Du

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model (2025.naacl-long)

Challenge:	a new approach to training large language models (LLMs) overlooks task-specific characteristics in tool use, leading to performance bottlenecks.
Approach:	They propose a task-feature-based framework that mitigates the effects of suboptimal training data . they use a dataset to train large-scale LLMs and a reward mechanism tailored to error categories .
Outcome:	The proposed framework matches or surpasses open- and closed-source LLMs in tool-use performance using only 1,217 training data points.

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Jiayang Yu, Yihang Zhang, Bin Wang, Peiqin Lin, YongKang Liu, Shi Feng

RSDA: Restoring Stale Data Affinity via Dynamic Renovation Strategy for Mitigating Data Scarcity (2026.acl-long)

Challenge:	Fine-tuning requires substantial computational resources and is prone to overfitting when applied to small datasets.
Approach:	They propose a parameter-efficient fine-tuning method that integrates a State Space Model (SSM) to interconnect low-rank matrices.
Outcome:	The proposed method achieves comparable performance to LoRA on the general language understanding evaluation (GLUE) benchmark while using only half the parameters.

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Yidan Liang, Jia Zhu, Weijie Shi, Hanghui Guo, Yue Cui, Jiawei Shen, Guoqing Ma, Jingjiang Liu, Qingyu Niu, Yilin Wang, Shimin Di, Jiajie Xu

Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs (2024.emnlp-main)

Challenge:	High-quality data is the cornerstone of advancing large language models, but the supply of premium data is nearing depletion, while vast stale corpora remain underutilized.
Approach:	They propose a framework to restore stale data affinity by quantifying the latent value of samples and employing a dynamic renovation strategy selection mechanism to determine the optimal component-level strategy.
Outcome:	The proposed framework achieves performance improvements using less than 10% of the data volume, underscoring that the latent potential of stale corpora remains largely untapped.

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Zheng Wang, Zhongyang Li, Zeren Jiang, Dandan Tu, Wei Shi

Entity-centered Cross-document Relation Extraction (2022.emnlp-main)

Challenge:	In the age of mobile internet, personal information is constantly being generated on smartphones.
Approach:	They propose a novel task of crafting personalized agents powered by large language models that leverage a user's smartphone memories to enhance downstream applications with LLM capabilities.
Outcome:	The proposed approach improves 10% over the best existing approach on a real-world dataset and improves usability.

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Fengqi Wang, Fei Li, Hao Fei, Jingye Li, Shengqiong Wu, Fangfang Su, Wenxuan Shi, Donghong Ji, Bo Cai

Distribution Prompting: Understanding the Expressivity of Language Models Through the Next-Token Distributions They Can Produce (2025.emnlp-main)

Challenge:	Existing methods for relation extraction only use text snippets surrounding target entities in multiple documents.
Approach:	They propose a relation-extraction model that uses cross-path entity relation attention to detect the semantic relations between entities in a given text.
Outcome:	The proposed method outperforms the state-of-the-art methods in the dataset CodRED by 10%.

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Haojin Wang, Zining Zhu, Freda Shi

ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models (2024.acl-long)

Challenge:	Autoregressive neural language models (LMs) generate a probability distribution over tokens at each time step given a prompt.
Approach:	They propose to find a prompt that induces LMs to output a distribution as close as possible to the target, using either soft or hard gradient-based prompt tuning.
Outcome:	The proposed model is able to generate a distribution as close as possible to a target given a prompt, and it can be used to approximate distributions with low or high entropy.

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Haochen Tan, Zhijiang Guo, Zhan Shi, Lu Xu, Zhili Liu, Yunlong Feng, Xiaoguang Li, Yasheng Wang, Lifeng Shang, Qun Liu, Linqi Song

Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good (P19-1)

Challenge:	Existing evaluation methods for large language models are labor-intensive and lack efficiency.
Approach:	They propose a framework dedicated to assessing long-text generation that includes in-depth human-curated meta-questions spanning various domains . they use a set of proxy-quests with pre-annotated answers to assess the content's quality by incorporating the generated texts as contextual background.
Outcome:	The proposed framework assesses the quality of long-text content by matching it with references through human evaluation or automated metrics.

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Xuewei Wang, Weiyan Shi, Richard Kim, Yoojung Oh, Sijia Yang, Jingwen Zhang, Zhou Yu

SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection (2026.acl-industry)

Challenge:	Persuasion agents are a form of communication that can be used to change people's opinions and actions for social good.
Approach:	They designed an online persuasion task where one participant was asked to persult the other to donate to a specific charity.
Outcome:	The proposed system could change people's opinions and actions for social good.

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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

Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction (2023.findings-emnlp)

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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Hao Li, Yanan Cao, Yubing Ren, Fang Fang, Lanxue Zhang, Yingjie Li, Shi Wang

Global-Local Modeling with Prompt-Based Knowledge Enhancement for Emotion Inference in Conversation (2023.findings-eacl)

Challenge:	Existing models do not build dependency information among event argument roles . Existing methods do not learn the interactions between different roles based on event structure .
Approach:	They propose an intra-event and inter-e event dependency-aware graph network to model dependencies between roles . they use event structure as the fundamental unit to construct role dependencies within events .
Outcome:	The proposed model improves on the ACE05, RAMS, and WikiEvents datasets.

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Renxi Wang, Shi Feng

Effidit: An Assistant for Improving Writing Efficiency (2023.acl-demo)

Challenge:	Existing studies on emotion recognition focus on recognizing emotions through a speaker’s utterance, while research on emotion inference predicts emotions of addressees through previous utterations.
Approach:	They propose a global-local modeling method based on recurrent neural networks and pre-trained language models to do emotion inference in conversation.
Outcome:	The proposed method achieves state-of-the-art on three datasets.

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Shuming Shi, Enbo Zhao, Wei Bi, Deng Cai, Leyang Cui, Xinting Huang, Haiyun Jiang, Duyu Tang, Kaiqiang Song, Longyue Wang, Chenyan Huang, Guoping Huang, Yan Wang, Piji Li

Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges (2024.findings-acl)

Challenge:	Effidit is a digital writing assistant that provides three modules to help users write faster and more efficiently.
Approach:	They present Effidit, a digital writing assistant that provides three modules to help users write higher-quality text more efficiently.
Outcome:	Effidit expands the capabilities of a typical writing assistant by providing three modules . Effit can help users create their own text faster and more efficiently .

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Xiaoming Shi, Zeming Liu, Li Du, Yuxuan Wang, Hongru Wang, Yuhang Guo, Tong Ruan, Jie Xu, Xiaofan Zhang, Shaoting Zhang

SConU: Selective Conformal Uncertainty in Large Language Models (2025.acl-long)

Challenge:	Existing medical dialogue systems have significant potential to simplify diagnostic procedure and reduce the cost of collecting information from patients.
Approach:	They analyze 325 papers from well-known computer science, natural language processing conferences and journals to find out the major challenges of medical dialog systems.
Outcome:	The proposed systems have been surveyed in the medical community but have not been evaluated from a technical perspective.

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Zhiyuan Wang, Qingni Wang, Yue Zhang, Tianlong Chen, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu

Speculative Decoding for Multi-Sample Inference (2025.findings-emnlp)

Challenge:	Existing frameworks fail to identify outliers that violate the exchangeability assumption, leading to unbounded miscoverage rates and unactionable prediction sets.
Approach:	They propose a method that implements significance tests to determine whether a given sample deviates from the uncertainty distribution of the calibration set.
Outcome:	The proposed approach facilitates rigorous management of miscoverage rates across single-domain and interdisciplinary contexts, and enhances the efficiency of predictions.

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Yiwei Li, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Yueqi Zhang, Ji Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li

RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation (2025.findings-emnlp)

Challenge:	Speculative decoding method exploits consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or external databases.
Approach:	They propose a speculative decoding method that exploits the consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or external databases.
Outcome:	The proposed method exploits the intrinsic consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or databases.

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Tianjiao Li, Mengran Yu, Chenyu Shi, Yanjun Zhao, Xiaojing Liu, Qi Zhang, Xuanjing Huang, Qiang Zhang, Jiayin Wang

CheckRLM: Effective Knowledge–Thought Coherence Checking in Retrieval-Augmented Reasoning (2026.acl-long)

Challenge:	Using reinforcement learning from human feedback, large language models perform poorly when applied to colloquial subtitle translation tasks.
Approach:	They propose an adversarial training framework that iteratively updates the offline reward model and the online LLM to improve training outcomes.
Outcome:	The proposed training framework significantly improves upon translation baselines.

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Dingling Xu, Ruobing Wang, Qingfei Zhao, Yukun Yan, Zhichun Wang, Daren Zha, Shi Yu, Zhenghao Liu, Shuo Wang, Xu Han, Maosong Sun

Logical forms complement probability in understanding language model (and human) performance (2025.acl-long)

Challenge:	Reasoning Language Models (RLMs) have improved performance on complex tasks by extending the reasoning chain, but they are prone to factual errors, especially in knowledge-intensive tasks.
Approach:	They propose a framework that improves the reliability of the reasoning process by timely checking and correcting factual errors.
Outcome:	The proposed framework outperforms baselines and shows that it mitigates error accumulation with lower costs.

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Yixuan Wang, Freda Shi

KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning (2026.acl-long)

Challenge:	Existing studies on LLMs have shown that they perform well on logical reasoning problems, but there is still a lack of fine-grained understanding of the logical forms.
Approach:	They propose a dataset of hypothetical and disjunctive syllogisms in propositional and modal logic and use it as the testbed for understanding LLM performance.
Outcome:	The proposed model performs well on proposi-tional and modal logics, but does it exhibit preferences for certain argument forms?

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Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di, Jiajie Xu

Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization (2025.emnlp-main)

Challenge:	Existing summarization methods compress content for gist browsing, but they break prerequisite logic in instructional videos.
Approach:	They propose a framework that decouples epistemic planning from content generation.
Outcome:	The proposed framework outperforms strong end-to-end baselines on Knowledge Progression Consistency and Learning Objective Coverage.

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Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang, Jizhou Huang, Dawei Yin, Lingyong Yan, Min Cao, Min Zhang

On the Inference Calibration of Neural Machine Translation (2020.acl-main)

Challenge:	Existing preference alignment methods focus on aligning model responses with human preferences while neglecting image-text modality alignment.
Approach:	They propose Entity-centric Multimodal Preference Optimization to improve modality alignment . they use open-source instruction datasets to automatically construct high-quality preference data .
Outcome:	The proposed approach reduces hallucination rates by 80.4% on Object HalBench and 52.6% on MM HalBech.

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Shuo Wang, Zhaopeng Tu, Shuming Shi, Yang Liu

Improving Role-Oriented Dialogue Summarization with Interaction-Aware Contrastive Learning (2024.lrec-main)

Challenge:	Existing studies show that NMT models trained with label smoothing are well-calibrated on ground-truth training data, but miscalibration remains a challenge during inference due to the discrepancy between training and inference.
Approach:	They propose a graduated label smoothing method that can improve inference calibration and translation performance.
Outcome:	The proposed method improves both inference calibration and translation performance.

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Weihong Guan, Shi Feng, Daling Wang, Faliang Huang, Yifei Zhang, Yuan Cui

MidMed: Towards Mixed-Type Dialogues for Medical Consultation (2023.acl-long)

Challenge:	Existing methods for encoding dialogues do not capture interaction information between roles, thus ignore interaction-related key information.
Approach:	They propose a contrastive learning based interaction-aware model for the role-oriented dialogue summarization namely CIAM and use it to train the decoder to learn role-level interaction.
Outcome:	The proposed model captures interaction information between different roles and produces informative summaries on two public datasets.

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Xiaoming Shi, Zeming Liu, Chuan Wang, Haitao Leng, Kui Xue, Xiaofan Zhang, Shaoting Zhang

When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life (2026.findings-acl)

Challenge:	Current medical dialogue systems assume that patients have explicit goals but are often unavailable in real-world situations due to the lack of medical knowledge.
Approach:	They propose a human-to-human mixed-type medical consultation dialogue corpus . they build benchmarking baselines on MidMed and propose an instruction-guiding framework . Experimental results show the effectiveness of InsMed .
Outcome:	The proposed system can help patients clarify their goals in real-world situations . it covers four departments with 8,309 dialogues and provides benchmarking baselines .

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Xinyue Lou, Xu Jinan, Jingyi Yin, Xiaolong Wang, Zhaolu Kang, null Liaoyouwei, Yixuan Wang, Xiangyu Shi, Fengran Mo, SU Yao, Kaiyu Huang

Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024.emnlp-main)

Challenge:	Safety impact of Multimodal Large Language Models (MLLMs) on human behavior is evaluated in this study.
Approach:	They propose a safety-warning-based evaluation framework that encourages models to provide clear and informative safety warnings, rather than generic refusals.
Outcome:	The proposed safety-warning-based evaluation framework encourages models to provide clear and informative safety warnings, rather than generic refusals.

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Yanchen Liu, Mingyu Ma, Wenna Qin, Azure Zhou, Jiaao Chen, Weiyan Shi, Wei Wang, Diyi Yang

Multi-modal Contrastive Representation Learning for Entity Alignment (2022.coling-1)

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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Zhenxi Lin, Ziheng Zhang, Meng Wang, Yinghui Shi, Xian Wu, Yefeng Zheng

MAGE: Machine-generated Text Detection in the Wild (2024.acl-long)

Challenge:	Existing studies focus on how to utilize information from different modalities, but it is not trivial to leverage multi-modal knowledge in entity alignment because of the modality heterogeneity.
Approach:	They propose a Multi-modal Contrastive Learning based Entity Alignment model which learns multiple individual representations from multiple modalities and performs contrastive learning to jointly model inter-modal and inter-modal interactions.
Outcome:	The proposed model outperforms state-of-the-art models on public datasets under both supervised and unsupervised conditions.

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Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang

Self-Improvement Towards Pareto Optimality: Mitigating Preference Conflicts in Multi-Objective Alignment (2025.findings-acl)

Challenge:	Existing research has focused on evaluating detection methods for specific domains or language models.
Approach:	They build a testbed to detect texts from diverse human writings and LLMs using different detection methods.
Outcome:	Empirical results show that the top performing detector can identify 84.12% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.

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Moxin Li, Yuantao Zhang, Wenjie Wang, Wentao Shi, Zhuo Liu, Fuli Feng, Tat-Seng Chua

DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models (2025.emnlp-industry)

Challenge:	Existing approaches to optimize large language models with human preferences suffer from preference conflicts in the data.
Approach:	They propose to construct Pareto-optimal responses to resolve preference conflicts by using a self-improving DPO framework that enables LLMs to self-generate and select Paret-optimized responses.
Outcome:	The proposed framework achieves superior Pareto Front performance over baselines on two datasets.

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Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang, Zhaoxiang Liu, Shiguo Lian

When One LLM Drools, Multi-LLM Collaboration Rules (2026.acl-long)

Challenge:	Recent advances in slow-thinking reasoning models have shown exceptional performance in complex reasoning tasks.
Approach:	They propose a framework that enables models to automatically adjust Chain-of-Thought (CoT) length based on problem difficulty.
Outcome:	The proposed framework penalizes inefficiency on simple problems while incentivizing deep reasoning for complex ones.

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Shangbin Feng, Wenxuan Ding, Alisa Liu, Zifeng Wang, Weijia Shi, Yike Wang, Shannon Zejiang Shen, Xiaochuang Han, Hunter Lang, Chen-Yu Lee, Tomas Pfister, Yejin Choi, Yulia Tsvetkov

Challenge:	a single general-purpose LLM is not enough to produce a reliable output, argues this paper . a multi-LLM collaboration approach addresses reliability, democratization, and pluralism .
Approach:	They argue that a single general-purpose LLM is not enough to produce a reliable output . they organize existing multi-LLM collaboration methods into a hierarchy based on access and information exchange .
Outcome:	The proposed method addresses reliability, democratization, and pluralism challenges a single LLM fails to produce a reliable output.

KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering (2022.emnlp-main)

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Jianing Wang, Chengyu Wang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Jun Huang, Ming Gao

Microblog Hashtag Generation via Encoding Conversation Contexts (N19-1)

Challenge:	Extractive Question Answering (EQA) is one of the most essential tasks in Machine Reading Comprehension (MRC).
Approach:	They propose a framework that transforms extractive question answering into a non-autoregressive Masked Language Modeling (MLM) generation problem.
Outcome:	The proposed framework outperforms state-of-the-art approaches in few-shot learning scenarios by a large margin.

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Yue Wang, Jing Li, Irwin King, Michael R. Lyu, Shuming Shi

Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (2024.findings-acl)

Challenge:	Automated hashtag annotation plays an important role in content understanding for microblog posts.
Approach:	They propose to annotate hashtags with a novel sequence generation framework via viewing the hashtag as a short sequence of words.
Outcome:	The proposed model outperforms existing models on two large-scale datasets . it can generate rare and even unseen hashtags, which is not possible with existing models .

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Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce Ho, Carl Yang

RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services (2025.emnlp-industry)

Challenge:	Clinical natural language processing (NLP) is a subfield that requires the extraction, analysis, and interpretation of unstructured clinical text.
Approach:	They propose a model which infuses knowledge into clinical text generation with LLMs for clinical NLP tasks.
Outcome:	The proposed model improves performance across 8 clinical NLP tasks and 18 datasets by 7.7%-8.7% on average.

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Fei Zhao, Chonggang Lu, null Wangyue, Zheyong Xie, Ziyan Liu, Haofu Qian, Jianzhao Huang, Fangcheng Shi, Zijie Meng, Hongcheng Guo, Mingqian He, Xinze Lyu, Zheyu Ye, Weiting Liu, Boyang Wang, Shaosheng Cao

Incremental Transformer: Efficient Encoder for Incremented Text Over MRC and Conversation Tasks (2025.coling-main)

Challenge:	Social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement.
Approach:	They propose a domain-specific LLM to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for social networking services.
Outcome:	The proposed model achieves an average improvement of 14.02% across 8 major tasks and 7.56% in bilingual evaluation benchmark, compared with baseline models.

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Weisheng Li, Yuechen Wang, Jiaxin Shi, Wengang Zhou, Qi Tian, Houqiang Li

RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records (2024.acl-short)

Challenge:	Existing encoders that encode incremented inputs have to re-encode the whole text to obtain the encoding of the extended input.
Approach:	They propose an efficient encoder dedicated for faster encoding of incremented input . it takes only added input as input but attends to cached representations of original input a lower layer .
Outcome:	The proposed encoder achieves 6.2x speedup over current encoders . it takes only added input as input but attends to cached representations of original input .

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Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Bowen Jin, May Dongmei Wang, Joyce Ho, Carl Yang

MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)

Challenge:	Existing deep learning models for EHRs rely on knowledge from a single source and do not capture the semantic information for medical codes.
Approach:	They propose a Retrieval AugMentation pipeline to augment clinical prediction on EHRs . they use multiple knowledge sources to convert them into text and use consistency regularization to capture complementary information from patient visits and summarized knowledge.
Outcome:	Experiments on two EHR datasets show that RAM-EHR improves clinical prediction tasks.

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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)

Challenge:	Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability.
Approach:	They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones.
Outcome:	The proposed framework shows that it is robust to different prompts and superior to previous methods.

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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu

InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions (2024.naacl-long)

Challenge:	Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately .
Approach:	They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes .
Outcome:	The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show .

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Yifan Wang, Yafei Liu, Chufan Shi, Haoling Li, Chen Chen, Haonan Lu, Yujiu Yang

AutoDSL: Automated domain-specific language design for structural representation of procedures with constraints (2024.acl-long)

Challenge:	In order to perform downstream tasks, Large Language Models (LLMs) need continual adaptation without catastrophic forgetting.
Approach:	They propose a new paradigm that allows for continual adaptation without catastrophic forgetting . they propose to replay previous data based on task similarity with instructions .
Outcome:	The proposed method improves performance over 16 tasks with different training orders.

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Yu-Zhe Shi, Haofei Hou, Zhangqian Bi, Fanxu Meng, Xiang Wei, Lecheng Ruan, Qining Wang

FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs (2026.acl-long)

Challenge:	Domain-specific Language (DSL) is an effective tool to express constraints structurally, but requires case-by-case hand-crafting.
Approach:	They propose a framework to automate domain-specific language constraint design . they propose 'autoDSL' framework to optimize syntactic and semantic constraints .
Outcome:	The framework automates constraint design across domains and abstracts semantic constraints.

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Bingkang Shi, Jen-tse Huang, Luo Long, Tianyu Zong, Hongzhu Yi, Yuanxiang Wang, Songlin Hu, Xiaodan Zhang, Zhongjiang Yao

Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)

Challenge:	Large Language Models (LLMs) have enhanced or replaced traditional non-player characters in video games.
Approach:	They propose a benchmark to evaluate social biases across three interaction patterns: transaction, cooperation, and competition.
Outcome:	The proposed benchmark assesses four bias types across transaction, cooperation, and competition using a novel metric, FairMCV.

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Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin

XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception (2024.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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HyoJung Han, Mohamed Anwar, Juan Pino, Wei-Ning Hsu, Marine Carpuat, Bowen Shi, Changhan Wang

The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis (2026.acl-long)

Challenge:	Speech recognition and translation systems perform poorly on noisy inputs, which are frequent in realistic environments.
Approach:	They propose a cross-lingual audio-visual speech representation model for noise-robust speech recognition and translation in over 100 languages.
Outcome:	The proposed model outperforms the previous state-of-the-art by 18.5% WER and 4.7 BLEU on downstream audio-visual speech recognition and translation tasks.

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Zihao Wei, Liang Pang, Jiahao Liu, Wenjie Shi, Jingcheng Deng, Shicheng Xu, Zenghao Duan, Jingang Wang, Fei Sun, Huawei Shen, Xueqi Cheng

On the Language Coverage Bias for Neural Machine Translation (2021.findings-acl)

Challenge:	Explicit reasoning trajectories increase performance but often trigger overthinking . despite its importance, this study examines how each step of reasoning affects the final outcome .
Approach:	They propose a Reasoning Completion Point Detector that detects the RCP by monitoring rank dynamics of termination tokens.
Outcome:	The proposed method reduces token usage by up to 44% while preserving accuracy.

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Shuo Wang, Zhaopeng Tu, Zhixing Tan, Shuming Shi, Maosong Sun, Yang Liu

A Siamese CNN Architecture for Learning Chinese Sentence Similarity (2020.aacl-srw)

Challenge:	Language coverage bias is important for neural machine translation because of the target-original training data.
Approach:	They propose two approaches to alleviate the language coverage bias problem by explicitly distinguishing between the source-and target-original training data.
Outcome:	The proposed methods improve translation tasks on both back-and forward-translation and their tagged variants.

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Haoxiang Shi, Cen Wang, Tetsuya Sakai

Semantic Similarity

Challenge:	a deep neural architecture is used to learn a semantic similarity metric between two sentences . traditional methods of learning sentence similarity are based on the word level, which may not be sufficient.
Approach:	They propose a deep neural architecture which uses siamese convolutional neural network sharing model parameters to learn a semantic similarity metric between two sentences.
Outcome:	The proposed architecture outperforms baselines in similarity metrics for Chinese sentences by 8.7 points.

Generalized Category Discovery with Large Language Models in the Loop (2024.findings-acl)

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Wenbin An, Wenkai Shi, Feng Tian, Haonan Lin, QianYing Wang, Yaqiang Wu, Mingxiang Cai, Luyan Wang, Yan Chen, Haiping Zhu, Ping Chen

Rethinking Word-Level Auto-Completion in Computer-Aided Translation (2023.emnlp-main)

Challenge:	Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data.
Approach:	They propose a framework that introduces Large Language Models into the training loop to generate category names without human effort.
Outcome:	The proposed framework outperforms SOTA models on three benchmark datasets and generates accurate category names for the discovered clusters.

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Xingyu Chen, Lemao Liu, Guoping Huang, Zhirui Zhang, Mingming Yang, Shuming Shi, Rui Wang

KoCo-Bench: Can Large Language Models Leverage Domain Knowledge in Software Development? (2026.acl-long)

Challenge:	Existing models for word-level auto-completion (WLAC) do not meet the criterion of good auto-completes.
Approach:	They propose a measurable criterion to address the question: what kind of words are good auto-completions? they propose an approach to enhance WLAC performance by promoting adherence to the cri-terion.
Outcome:	The proposed approach outperforms the top-performing system submitted to the WLAC shared tasks in WMT2022 while using significantly smaller model sizes.

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Xue Jiang, Ge Li, Jiaru Qian, Xianjie Shi, Chenjie Li, Hao Zhu, Ziyu Wang, Jielun Zhang, Zeyu Zhao, Kechi Zhang, Jia Li, Wenpin Jiao, Zhi Jin, Yihong Dong

Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning (2026.acl-long)

Challenge:	Existing domain-specific code benchmarks focus on assessing what knowledge LLMs possess rather than how they acquire and apply new knowledge.
Approach:	They propose a benchmark to evaluate domain specialization methods in real-world software development.
Outcome:	KOCO-bench is a new benchmark for evaluating domain specialization methods in real-world software development.

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YongKang Liu, Xingle Xu, Ercong Nie, Zijing Wang, Shi Feng, Daling Wang, Qian Li, Hinrich Schuetze

PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs (2026.findings-acl)

Challenge:	Parameter-Efficient Fine-Tuning (PEFT) is an alternative to Full-Parameter Fine-tuning, but its effectiveness on complex tasks such as reasoning and instruction-following remains unclear.
Approach:	They propose to use PEFT to reduce the number of trainable parameters while freezing the weights of LLMs.
Outcome:	The proposed methods perform well on standard tasks, but weaknesses on complex and adversarial settings call for new directions beyond current paradigms.

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Zijing Wang, YongKang Liu, Mingyang Wang, Ercong Nie, Deyuan Chen, Zhengjie Zhao, Shi Feng, Daling Wang, Xiaocui Yang, Yifei Zhang, Hinrich Schuetze

D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)

Challenge:	Multimodal instruction fine-tuning degrades textual reasoning capability, undermining multimodal performance.
Approach:	They propose a plateau-guided model merging method that selectively injects base language model parameters into MLLMs to mitigate this degradation.
Outcome:	The proposed framework reduces multimodal instruction fine-tuning degradation by incorporating a plateau-guided model merging method into MLLMs.

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Binwei Yao, Chao Shi, Likai Zou, Lingfeng Dai, Mengyue Wu, Lu Chen, Zhen Wang, Kai Yu

Teaching LLMs to Abstain across Languages via Multilingual Feedback (2024.emnlp-main)

Challenge:	Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness.
Approach:	They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria.
Outcome:	The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset.

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Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Orevaoghene Ahia, Shuyue Stella Li, Vidhisha Balachandran, Sunayana Sitaram, Yulia Tsvetkov

Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation (D19-1)

Challenge:	Existing studies on LLM abstention focus on English, but they show that it can reduce the accuracy of the model by 20.5% .
Approach:	They propose to teach LLMs to abstain in the face of knowledge gaps by generating multiple feedback items in related languages.
Outcome:	Extensive experiments show that the proposed approach outperforms baselines and achieves 9.2% improvement for low-resource languages.

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Weichao Wang, Shi Feng, Daling Wang, Yifei Zhang

CIRAG: Construction–Integration Retrieval and Adaptive Generation for Multi-hop Question Answering (2026.acl-long)

Challenge:	Existing methods for question generation suffer from dullness and deviation problem, which can lead to deviated or dull questions.
Approach:	They propose two methods to enhance semantic coherence between question and answer by using a coherent score and adversarial training to explicitly control question generation.
Outcome:	The proposed methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions.

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Zili Wei, Yilin Wang, Xiaocui Yang, Shi Feng, Weidong Bao, Daling Wang, Zihan Wang, Yifei Zhang

Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation (2021.findings-emnlp)

Challenge:	Existing methods for iterative retrieval-augmented generation (iRAG) suffer from greedy single-path expansion and granularity–demand mismatch .
Approach:	They propose a model that constructs candidate triples and history-conditionally integrates them to distill core triples to generate the next-hop query.
Outcome:	The proposed model mitigates the greedy single-path expansion and granularity–demand mismatch by preserving multiple plausible evidence chains.

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Ke Wang, Yangbin Shi, Jiayi Wang, Yuqi Zhang, Yu Zhao, Xiaolin Zheng

A Diffusion Weighted Graph Framework for New Intent Discovery (2023.emnlp-main)

Challenge:	Quality Estimation (QE) is an essential role in applications of Machine Translation (MT).
Approach:	They propose to fuse uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality.
Outcome:	The proposed method achieves state-of-the-art on the datasets of WMT 2020 QE shared task.

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Wenkai Shi, Wenbin An, Feng Tian, Qinghua Zheng, QianYing Wang, Ping Chen

A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)

Challenge:	Existing methods to learn from unlabeled data generate noisy supervisory signals . current methods only rely on semantic similarities to generate supervisory signal .
Approach:	They propose a weighted DWGF framework to capture semantic similarities and structure relationships in data.
Outcome:	The proposed method outperforms state-of-the-art models on evaluation metrics across multiple benchmark datasets.

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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang

Exploring Hybrid Question Answering via Program-based Prompting (2024.acl-long)

Challenge:	Inductive reasoning is an important task for large language models (LLMs).
Approach:	They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation.
Outcome:	The proposed method improves inductive reasoning in large language models.

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Qi Shi, Han Cui, Haofeng Wang, Qingfu Zhu, Wanxiang Che, Ting Liu

Advancing Sequential Numerical Prediction in Autoregressive Models (2025.acl-short)

Challenge:	Existing approaches to question answering over heterogeneous data are limited due to large scale of information and organic coupling of heterogenous data.
Approach:	They propose a program-based prompting framework for hybrid question answering tasks . it integrates various functions to perform hybrid information-seeking over data .
Outcome:	The proposed framework surpasses baseline systems and achieves the best performance under the fewshot settings.

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Xiang Fei, Jinghui Lu, Qi Sun, Hao Feng, Yanjie Wang, Wei Shi, An-Lan Wang, Jingqun Tang, Can Huang

Challenge:	Autoregressive models are the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences.
Approach:	They propose a novel approach to entropy loss by extending the Earth Mover’s Distance to preserve ordinal relationships between numerical values and sequence-level to penalize the overall discrepancy between predicted and actual sequences.
Outcome:	Extensive experiments show that NTIL improves numerical prediction and integrates effectively with LLMs/MLLMs.

Exploiting Deep Representations for Neural Machine Translation (D18-1)

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Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Shuming Shi, Tong Zhang

Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation (2021.acl-long)

Challenge:	Neural machine translation models typically implement encoder and decoder as multiple layers, but only the top layers are leveraged in the subsequent process, which misses the opportunity to exploit useful information embedded in other layers.
Approach:	They propose to expose all of these signals with layer aggregation and multi-layer attention mechanisms and introduce an auxiliary regularization term to encourage different layers to capture diverse information.
Outcome:	The proposed approach exposes all of these signals with layer aggregation and multi-layer attention mechanisms on widely-used translation datasets.

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Wenxiang Jiao, Xing Wang, Zhaopeng Tu, Shuming Shi, Michael Lyu, Irwin King

GUI Agents: A Survey (2025.findings-acl)

Challenge:	Experimental results show that enhancing the learning on uncertain monolingual sentences improves the translation quality of high-uncertainty sentences and also benefits the prediction of low-frequency words at the target side.
Approach:	They propose to use monolingual data to augment model training with synthetic parallel data by selecting the most informative monolingual sentences to complement the parallel data.
Outcome:	The proposed approach improves the performance of natural language models by selecting the most informative monolingual sentences.

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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt

Challenge:	Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life.
Approach:	They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities.
Outcome:	The proposed framework delineates their perception, reasoning, planning, and acting capabilities.

LSEG: A Fine-tuning Free Method for NL2FOL via Logic-Structure and Entropy Guided Inference Controlling (2026.findings-acl)

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Yuwei Huang, Shi Wang, Kangli Zi, Tianyu Luo, Jiang Zhixiao, Ruiheng Wang, Yufei Wang

From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap for Text Length Control via MarkerGen (2025.acl-long)

Challenge:	Large language models struggle with natural language to first order logic (NL2FOL) translation due to logical hallucination.
Approach:	They propose a fine-tuning free framework to correct hidden state deviation by leveraging logical stability across logic preserving perturbations of the input.
Outcome:	The proposed framework improves logical consistency during inference and improves accuracy over baselines.

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Peiwen Yuan, Chuyi Tan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Jiayi Shi, Boyuan Pan, Yao Hu, Kan Li

Challenge:	Existing methods to control text length are lacking in LCTG, posing a major limitation for practical applications.
Approach:	They propose a plug-and-play approach that decomposes LCTG sub-abilities with human patterns as reference and performs detailed error analysis.
Outcome:	The proposed method significantly improves LCTG across various settings, exhibiting outstanding effectiveness and generalizability.

UEGP: Unified Expert-Guided Pre-training for Knowledge Rekindle (2024.findings-naacl)

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Yutao Mou, Kexiang Wang, Jianhe Lin, Dehong Ma, Jun Fan, Daiting Shi, Zhicong Cheng, Gu Simiu, Dawei Yin, Weiran Xu

Language Models as Continuous Self-Evolving Data Engineers (2025.emnlp-main)

Challenge:	Existing paradigms for pre-training and fine-tuning have limitations . knowledge rekindle aims to break through performance upper bounds of experts without introducing additional annotated data.
Approach:	They propose a new paradigm for pre-training and fine-tuning that aims to re-incorporate the fine- tuned expert model into the training cycle and break through performance upper bounds of experts.
Outcome:	The proposed model breaks through performance upper bounds of experts without additional annotated data.

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Peidong Wang, Ming Wang, Zhiming Ma, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song

LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency (2022.coling-1)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their further evolution is often hampered by the scarcity of high-quality training data and the heavy reliance of traditional methods on expert-labeled data.
Approach:	They propose a paradigm that enables LLMs to train themselves by generating, cleaning, reviewing and annotating data with preference information.
Outcome:	The proposed model can generate, clean, review, and annotate data with preference information significantly reducing time and cost of post-training data construction.

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Haoxiang Shi, Rongsheng Zhang, Jiaan Wang, Cen Wang, Yinhe Zheng, Tetsuya Sakai

LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization (N19-4)

Challenge:	Existing parameter-efficient methods focus on reducing trainable parameters but neglect the inference speed, which limits the ability to deploy PLMs.
Approach:	They propose to use a hypernetwork-assisted inter-layer connector to enhance inference efficiency by tuning parameters inside a linear connector between two Transformer layers.
Outcome:	The proposed model reduces model parameters to 11.75% while preserving performance degradation to less than 5%.

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Tian Shi, Ping Wang, Chandan K. Reddy

Semantic-Eval : A Semantic Comprehension Evaluation Framework for Large Language Models Generation without Training (2025.acl-long)

Challenge:	Neural abstractive text summarization (NATS) has gained a lot of attention in the past few years from both industry and academia.
Approach:	They propose an open-source toolkit for training and evaluation of different sequence-to-sequence based models for the NATS task and for deploying the pre-trained models to real-world applications.
Outcome:	The proposed model can be used to generate high-quality summaries that are verbally innovative and can easily incorporate external knowledge.

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Shusheng Li, Jiale Li, Yifei Qu, Xinwei Shi, Yanliang Guo, Ziyi He, Yubo Wang, Wenjun Tan

NEAT: Neuron-Based Early Exit for Large Reasoning Models (2026.findings-acl)

Challenge:	Large language models (LLMs) have emerged as key drivers of progress in the field of natural language processing.
Approach:	They propose a framework that assesses LLM-generated text based on semantic understanding.
Outcome:	The proposed framework surpasses traditional evaluation metrics and lags behind GPT-4.

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Kang Liu, YongKang Liu, Xiaocui Yang, Peidong Wang, Wen Zhang, Shi Feng, Yifei Zhang, Daling Wang

Mitigating Legal Hallucinations via Symbolic Constraints and Analogical Precedents (2026.acl-long)

Challenge:	Existing approaches to reduce overthinking require additional rollout computation or externally labeled datasets.
Approach:	They propose a Neuron-based Early reAsoning exiT framework that monitors neuron-level activation dynamics to enable training-free early exits.
Outcome:	The proposed framework reduces the amount of reasoning steps generated by LRMs while maintaining accuracy.

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Zixuan Huang, Yanxiang Ma, Luhan Wang, Yunke Wang, Duo Shi, Chang Xu

RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation (2026.findings-acl)

Challenge:	Existing methods for finetuning and retrieval-augmented generation suffer from hallucination risk and semantic drift.
Approach:	They propose a framework for a dual-retriever based on the legal syllogism and the nature of different legal data.
Outcome:	The proposed framework mitigates hallucinations while improving explainability of legal reasoning.

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Ran Xu, Yuchen Zhuang, Yue Yu, Haoyu Wang, Wenqi Shi, Carl Yang

Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation (2022.acl-long)

Challenge:	Retrieval-augmented generation (RAG) enhances large language models by integrating external knowledge retrieved at inference time.
Approach:	They evaluate RAG systems using MassiveDS, a large-scale datastore with mixture of knowledge.
Outcome:	The proposed approach improves performance on knowledge-intensive NLP tasks.

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Wenxuan Wang, Wenxiang Jiao, Yongchang Hao, Xing Wang, Shuming Shi, Zhaopeng Tu, Michael Lyu

From Inheritance to Saturation: Disentangling the Evolution of Visual Redundancy for Architecture-Aware MLLM Inference Acceleration (2026.acl-long)

Challenge:	Existing studies on self-supervised pretraining for machine translation have focused on the jointly pretrained decoder .
Approach:	They propose a method to improve neural machine translation by jointly pretrained decoder . they propose two strategies to remedy the domain and objective discrepancies .
Outcome:	The proposed approach improves translation performance and model robustness on three language pairs.

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Jiaqi Shi, Xulong Zhang, Yuechan Li, Xiaoyang Qu, Jianzong Wang

Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling (2026.findings-acl)

Challenge:	Existing acceleration strategies suffer from severe "backbone dependency" Existing strategies such as token pruning or layer sparsity suffer from this .
Approach:	They propose a framework that decouples visual redundancy into IVR and architecture-dependent secondary saturation redundancies.
Outcome:	The proposed framework outperforms existing frameworks on Qwen25-VL and Qwa25-LL.

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Zhenghua Wang, Yiran Ding, Changze Lv, Yixin Wu, Tianlong Li, Zhibo Xu, Muling Wu, Tianyuan Shi, Shizheng Li, Qi Qian, Xuanjing Huang, Xiaoqing Zheng

SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map (2021.emnlp-main)

Challenge:	Existing methods to address the "lost-in-the-middle" problem suffer from high latency or suboptimal hand-crafted scaling strategies.
Approach:	They propose a layer-specific positional embedding scaling method that assigns distinct scaling factors to each layer.
Outcome:	Experiments show that the proposed method mitigates positional attention bias and delivers consistent improvements across multiple long-context benchmarks.

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Kangli Zi, Shi Wang, Yu Liu, Jicun Li, Yanan Cao, Cungen Cao

JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation (2026.findings-acl)

Challenge:	Sentence Compression (SC) is an important natural language processing task . it aims to shorten sentences while preserving the original meanings of the words . improvements on Chinese SC models are still lacking due to several difficulties .
Approach:	They propose a neural Chinese SC model enhanced with a Self-Organizing Map from Chinese colloquial sentences from a real-life question answering system.
Outcome:	The proposed model achieves a promising F1 score of 89.655 and BLEU4 score of 70.116 . it improves the performance of the whole neural Chinese SC model in a valid manner .

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Zhichao Shi, Xuhui Jiang, Chengjin Xu, Cangli Yao, Shengjie Ma, Yinghan Shen, Zixuan Li, Jian Guo, Yuanzhuo Wang

Challenge:	Current evaluation methods for large language models rely on static benchmarks . limited knowledge coverage and fixed difficulties hinder the targeted optimizations resulting in superficial evaluations of LLMs - a problem that has been addressed by JudgeAgent .
Approach:	They propose a knowledge-driven and dynamic evaluation framework for large language models . judgeAgent leverages LLM agents equipped with context graphs to traverse knowledge structures .
Outcome:	The proposed framework can achieve comprehensive evaluations and facilitate effective model iterations.

ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees (2024.findings-emnlp)

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Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Heng Tao Shen, Xiaofeng Zhu

MultiSkill: Evaluating Large Multimodal Models for Fine-grained Alignment Skills (2024.findings-emnlp)

Challenge:	Uncertainty quantification (UQ) in natural language generation tasks remains an open challenge . however, black-box uncertainty measures require investigating with the proliferation of LLMs served via APIs.
Approach:	They propose a conformal uncertainty measure and a method to transform heuristic uncertainty notions into rigorous prediction sets.
Outcome:	Empirical results show that the proposed method outperforms state-of-the-art methods and can provide reliable guarantees for open-ended NLG tasks.

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Zhenran Xu, Senbao Shi, Baotian Hu, Longyue Wang, Min Zhang

Revisiting Self-Consistency from Dynamic Distributional Alignment Perspective on Answer Aggregation (2025.findings-acl)

Challenge:	Existing evaluation settings for large multimodal models focus on coarse-grained evaluation without considering skill composition required by specific instructions.
Approach:	They propose an evaluation protocol that assesses large multimodal models across multiple fine-grained skills for alignment with human values.
Outcome:	The proposed evaluation protocol decomposes coarse-level scoring to fine-grained skill set-level score tailored to each instruction.

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Yiwei Li, Ji Zhang, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Jiayi Shi, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li

Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels (2025.emnlp-main)

Challenge:	Existing studies on self-consistency show that it improves reasoning abilities by aggregating diverse stochastic samples.
Approach:	They propose a confidence-driven mechanism that dynamically calibrates temperature to align with high probability modes.
Outcome:	The proposed method outperforms fixed-diversity baselines on reasoning tasks and improves both average and best-case performance.

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Junjie Ye, Yuming Yang, Yang Nan, Shuo Li, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan

PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism (2023.acl-long)

Challenge:	Large language models (LLMs) acquire substantial world knowledge during pretraining, which is further shaped by post-training techniques such as supervised fine-tuning (SFT).
Approach:	They evaluate closed-book question answering (CBQA) performance across five LLMs from the LLaMA-2 and LLama-3 families and examine the impact of supervised fine-tuning on model knowledge.
Outcome:	The proposed model performance is 14% worse than models fine-tuned on 1,920 samples and 12% worse on 240 samples.

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Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schütze

A Reward-Guided Dual-Phase Framework for Adaptive Inference-Time Reasoning (2026.findings-acl)

Challenge:	Existing generative models for dialogue use the last hidden state to summarize the history of the dialogue.
Approach:	They propose a Pseudo-Variational Gated Recurrent Unit (PVGRU) that summarises the accumulated distribution variations of subsequences and builds a model based on it.
Outcome:	The proposed model can improve diversity and relevance of responses on two benchmark datasets.

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Yingqian Cui, Zhenwei Dai, Pengfei He, Bing He, Hui Liu, Zhan Shi, Xianfeng Tang, Jingying Zeng, Suhang Wang, Yue Xing, Jiliang Tang, Benoit Dumoulin

CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning (2024.acl-long)

Challenge:	Large Language Models (LLMs) have made strong progress in reasoning.
Approach:	They propose a dual-phase test-time scaling framework that separates planning and execution and performs search over each phase independently.
Outcome:	Experiments on math reasoning and code generation benchmarks show that the proposed approach improves accuracy while reducing redundant computation.

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Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu, Zhaowei Wang, Jiaxin Bai, Xin Liu, Cheng Jiayang, Chunkit Chan, Yangqiu Song

When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models (2020.emnlp-main)

Challenge:	Existing approaches to generalize commonsense reasoning lack instantiated knowledge and require pre-built concept taxonomies and annotations.
Approach:	They propose a framework that iteratively performs contextualized conceptualization and instantiation over commonsense knowledge bases by instructing large language models to generate both types of knowledge with critic filtering.
Outcome:	Empirical results show that distilling CANDLE on student models provides benefits across three downstream tasks.

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Changlong Yu, Jialong Han, Peifeng Wang, Yangqiu Song, Hongming Zhang, Wilfred Ng, Shuming Shi

KnowTuning: Knowledge-aware Fine-tuning for Large Language Models (2024.emnlp-main)

Challenge:	a taxonomy is a semantic hierarchy of words or concepts organized w.r.t. their hypernymy relationships.
Approach:	They propose a framework for hypernymy detection using large textual corpora . they quantify the non-negligible existence of specific sparsity cases .
Outcome:	The proposed framework quantifies the non-negligible existence of specific sparsity cases on several benchmark datasets.

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Yougang Lyu, Lingyong Yan, Shuaiqiang Wang, Haibo Shi, Dawei Yin, Pengjie Ren, Zhumin Chen, Maarten Rijke, Zhaochun Ren

LastingBench: Defend Benchmarks Against Knowledge Leakage (2025.findings-emnlp)

Challenge:	Large language models (LLMs) are a default solution for many natural language processing tasks.
Approach:	They propose a knowledge-aware fine-tuning method to improve LLMs' knowledge awareness . they propose augmentation and comparison stages to improve accuracy and reliability .
Outcome:	The proposed method generates more facts with less factual error rate under fine-grained facts evaluation.

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Yixiong Fang, Tianran Sun, Yuling Shi, Min Wang, Xiaodong Gu

Question Answering

Challenge:	Existing methods to detect and safeguard LLMs against knowledge leakage fail to address the long-term challenge of mitigating it.
Approach:	They propose a method to reinforce and safeguard existing benchmarks against knowledge leakage by perturbation-based detection and counterfactual rewriting to disrupt memorization while preserving original intent.
Outcome:	The proposed method reduces memorization effects in long-context QA benchmarks, providing a more accurate assessment of model reasoning and generalization abilities.

LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios (2025.acl-long)

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Xiaodong Wu, Minhao Wang, Yichen Liu, Xiaoming Shi, He Yan, Lu Xiangju, Junmin Zhu, Wei Zhang

Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate (2024.emnlp-main)

Challenge:	Existing benchmarks rarely focus on instruction-following in long-context scenarios or stability on different inputs.
Approach:	They propose a scalable dataset to evaluate LLMs’ instruction-following capabilities and stability across long contexts.
Outcome:	The proposed method evaluates LLMs’ instruction-following capabilities and stability across long contexts.

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Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, Zhaopeng Tu

Dialogue Response Selection with Hierarchical Curriculum Learning (2021.acl-long)

Challenge:	Modern large language models (LLMs) have shown remarkable performance on general language tasks but struggle on complex reasoning tasks.
Approach:	They propose a multi-agent debate framework that encourages divergent thinking in LLMs . they propose to break debate and use a judge to obtain a final solution .
Outcome:	The proposed framework encourages divergent thinking in large language models . it is able to generate novel thoughts even if initial stance is incorrect .

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Yixuan Su, Deng Cai, Qingyu Zhou, Zibo Lin, Simon Baker, Yunbo Cao, Shuming Shi, Nigel Collier, Yan Wang

Dialect-SQL: An Adaptive Framework for Bridging the Dialect Gap in Text-to-SQL (2025.emnlp-main)

Challenge:	Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics.
Approach:	They propose a hierarchical curriculum learning framework that trains matching models in an “easy-to-difficult” scheme.
Outcome:	The proposed framework significantly improves the model performance across evaluation metrics on three benchmark datasets with three state-of-the-art matching models.

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Jie Shi, Xi Cao, Bo Xu, Jiaqing Liang, Yanghua Xiao, Jia Chen, Peng Wang, Wei Wang

Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models (2025.findings-acl)

Challenge:	Existing Text-to-SQL research focuses on specific database systems, limiting adaptability to different dialects.
Approach:	They propose a framework that employs Object Relational Mapping (ORM) code as an intermediate language to bridge this gap.
Outcome:	The proposed framework outperforms existing methods that generate SQL queries directly.

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Yue Li, Xin Yi, Dongsheng Shi, Gerard De Melo, Xiaoling Wang, Linlin Wang

M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions (2024.acl-long)

Challenge:	Recent work has shown that pruning can reduce model performance, but it can also lead to degradation in safety performance.
Approach:	They propose a hierarchical safety realignment approach to prune large vision-Language Models . they quantify contribution of each attention head to safety and restore neurons .
Outcome:	The proposed approach achieves significant safety improvements in LVLMs pruned post pruning.

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Zheng Wang, Shu Teo, Jieer Ouyang, Yongjun Xu, Wei Shi

Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues (2025.emnlp-main)

Challenge:	Existing methods for retrieving relevant memories from an external database are coarse-grained and can cause noise and focus on crucial memories.
Approach:	They propose a multiple partition paradigm for RAG where each database partition serves as a basic unit for execution.
Outcome:	The proposed framework outperforms baseline methods on three language generation tasks on seven datasets.

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Jinfeng Zhou, Yuxuan Chen, Jianing Yin, Yongkang Huang, Yihan Shi, Xikun Zhang, Libiao Peng, Rongsheng Zhang, Tangjie Lv, Zhipeng Hu, Hongning Wang, Minlie Huang

OssCSE: Overcoming Surface Structure Bias in Contrastive Learning for Unsupervised Sentence Embedding (2023.emnlp-main)

Challenge:	Existing approaches to cognitive restructuring (CR) are limited by entrenched cognitive distortions, emotional resistance, and individual differences.
Approach:	They propose a framework that structures CR as theory-grounded multi-stage multi-turn dialogue and a multi-channel loop mechanism to account for diverse individual distortions.
Outcome:	The proposed framework integrates supportive strategies for emotional management and a multi-channel loop mechanism to account for diverse individual distortions.

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Zhan Shi, Guoyin Wang, Ke Bai, Jiwei Li, Xiang Li, Qingjun Cui, Belinda Zeng, Trishul Chilimbi, Xiaodan Zhu

Self-supervised Preference Optimization: Enhance Your Language Model with Preference Degree Awareness (2024.findings-emnlp)

Challenge:	Recent studies show that contrastive learning is effective in sentence representation learning . but, the surface structure bias is a problem in the current model .
Approach:	They propose to combine a sentence with a sub-semantic sentence to investigate the surface structure bias.
Outcome:	The proposed model achieves state-of-the-art on standard semantic textual similarity tasks using different pre-trained backbones.

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Jian Li, Haojing Huang, Yujia Zhang, Pengfei Xu, Xi Chen, Rui Song, Lida Shi, Jingwen Wang, Hao Xu

Towards Privacy-Aware Sign Language Translation at Scale (2024.acl-long)

Challenge:	Recent studies have focused on replacing the reward model in Reinforcement Learning with Human Feedback (RLHF) methods for Large Language Models (LLMs).
Approach:	They propose a self-supervised preference optimization framework that replaces the reward model with a preference loss and alignment loss to improve LLMs' ability to understand human preferences.
Outcome:	The proposed framework can be integrated with existing preference optimization methods and significantly boost their performance.

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Phillip Rust, Bowen Shi, Skyler Wang, Necati Cihan Camgoz, Jean Maillard

MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings (2026.acl-long)

Challenge:	Existing sign language training systems require detailed and time aligned annotations to be effective.
Approach:	They propose a two-stage framework for privacy-aware SLT at scale that leverages self-supervised video pretraining on anonymized and unannotated videos followed by supervised SLT finetuning on a curated parallel dataset.
Outcome:	The proposed framework outperforms baselines on the How2Sign dataset and achieves state-of-the-art finetuned and zero-shot gloss-free SLT performance.

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Yiqun Zhang, Hao Li, Zihan Wang, Shi Feng, Xiaocui Yang, Daling Wang, Bo Zhang, Lei Bai, Shuyue Hu

SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning (2026.acl-long)

Challenge:	Multi-turn, long-horizon tasks require dozens of sequential model calls per episode.
Approach:	They propose a cost-aware multi-turn LLM routing tool which encodes interaction history and candidate models into joint history–model embeddings and learns an outcome estimator from logged trajectories to predict turn-level model utility.
Outcome:	The proposed model reduces cost and performance by 58.7% on ScienceWorld and on Humanity’s Last Exam (HLE) and even reduces costs for held-out tasks.

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Peidong Wang, Zhiming Ma, Xin Dai, YongKang Liu, Shi Feng, Xiaocui Yang, Wenxing Hu, Zhihao Wang, Mingjun Pan, Li Yuan, Daling Wang

VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension (2025.acl-long)

Challenge:	Existing methods for fraud detection rely on transcribed text, lacking acoustic cues . a proposed framework for audio-based slow-thinking fraud detection eliminates transcription errors .
Approach:	They propose a framework for audio-based slow-thinking fraud detection that eliminates transcription errors and rewards slow-thought reasoning by capturing fine-grained audio details.
Outcome:	The proposed method improves accuracy, inference efficiency, and real-time processing capabilities.

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Xinyu Chen, Yunxin Li, Haoyuan Shi, Baotian Hu, Wenhan Luo, Yaowei Wang, Min Zhang

AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts (2026.acl-long)

Challenge:	Existing video evaluation benchmarks focus on a single language, typically English, and feature videos rooted in Western cultural contexts.
Approach:	They propose a video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension.
Outcome:	The proposed video evaluation benchmark bridges cultural, linguistic, and domain divides . existing benchmarks only feature videos from YouTube, Shutterstock, or established video datasets based on cultural diversity .

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Keyu Li, Junhao Shi, Yang Xiao, Mohan Jiang, Jie Sun, Yunze Wu, Dayuan Fu, Shijie Xia, Xiaojie Cai, Tianze Xu, Weiye Si, Wenjie Li, Dequan Wang, Pengfei Liu

Challenge:	Existing benchmarks focus on single agentic capability, failing to capture long-horizon real-world scenarios.
Approach:	They propose a benchmark that evaluates 6 agentic capabilities across 32 real-world scenarios.
Outcome:	Experiments show that closed-source models outperform open-source model (48.4% vs 32.1%) integrating models with advanced scaffolds to form autonomous agents is a paradigm shift.

TeachMaster: Generative Teaching via Code (2026.acl-industry)

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Yuheng Wang, Runde Yang, Lin Wu, Jie Zhang, Jingru Fan, Tianle Zhou, Ruoyu Fu, Huatao Li, Ruijie Shi, Siheng Chen, Weinan E, Chen Qian

Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models (2025.acl-long)

Challenge:	Existing methods for creating video content are limited by high costs and slow update cycles.
Approach:	They propose a paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle execution.
Outcome:	The proposed framework reduces production costs to 0.3% of traditional course videos and provides a robust solution for scalable education.

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Huifeng Yin, Yu Zhao, Minghao Wu, Xuanfan Ni, Bo Zeng, Huaiyu.wh Huaiyu.wh, Tianqi Shi, Liangying Shao, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang

Multi-Granularity Self-Attention for Neural Machine Translation (D19-1)

Challenge:	Recent efforts to distill large reasoning models into smaller lightweight models have shown competitive performances.
Approach:	They propose to distill long Chain-of-Thought data to improve SFT and RL methods by constructing data from scratch using Monte Carlo Tree Search.
Outcome:	The proposed method significantly improves reasoning performance on various benchmarks such as math (GSM8K, MATH, AIME).

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Jie Hao, Xing Wang, Shuming Shi, Jinfeng Zhang, Zhaopeng Tu

MM-ChatAlign: A Novel Multimodal Reasoning Framework based on Large Language Models for Entity Alignment (2024.findings-emnlp)

Challenge:	Existing neural machine translation models use a deep multi-head self-attention network with no explicit phrase information.
Approach:	They propose a neural network that combines multi-head self-attention and phrase modeling to train attention heads to attend to phrases in either n-gram or syntactic formalisms.
Outcome:	The proposed approach improves on English-to-German and NIST Chinese-to English translation tasks.

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Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Huang Zihe, Jian Guo, Yuanzhuo Wang

Adaptive and Robust Translation from Natural Language to Multi-model Query Languages (2025.acl-long)

Challenge:	Existing MMEA methods rely on knowledge representation learning (KRL) to measure the similarity of entity embeddings.
Approach:	They propose a framework that utilizes the visual reasoning abilities of MLLMs for multimodal entity alignment.
Outcome:	The proposed framework integrates the visual reasoning abilities of MLLMs for multimodal entity alignment.

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Gengyuan Shi, Chaokun Wang, Liu Yabin, Jiawei Ren

GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation (2024.emnlp-industry)

Challenge:	Multi-model databases and polystore systems are increasingly studied for managing multi-model data holistically.
Approach:	They propose an adaptive Text-to-MMQL framework that includes a schema embedding module and an MMQl representation strategy to generate concise intermediate query formats with error correction in generated queries.
Outcome:	The proposed framework achieves over 9% accuracy improvement over baseline methods.

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Wenjie Zhou, Zhenxin Ding, Xiaodong Zhang, Haibo Shi, Junfeng Wang, Dawei Yin

CoTJudger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRMs (2026.findings-acl)

Challenge:	Pre-trained language models have achieved remarkable performance in OpenQA, but for practical deployment, knowledge distillation is crucial to maintain high performance while operating under computational constraints.
Approach:	They propose an algorithm to perform unsupervised knowledge distillation without the guidance of labels to achieve 99.5% of performance.
Outcome:	The proposed algorithm achieves 99.5% of performance in a commercial question-answering system.

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Siyi Li, Jiajun Shi, Shiwen Ni, Ge Zhang, Shuaimin Li, Shijian Wang, Zhoufutu Wen, Yizhi LI, Hamid Alinejad-Rokny, Jiaheng Liu, Min Yang, Wenhao Huang

Cat-MoD: Accelerating Multimodal Alignment via Caption Token Guided Asymmetric Mixture-of-Depths (2026.acl-long)

Challenge:	Existing evaluations emphasize final accuracy or coarse token counts, and lack automated tools to separate essential logic from structural redundancy.
Approach:	They propose a graph-driven framework that quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path needed to reach a correct solution.
Outcome:	Evaluating 21 LRMs, the proposed framework quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path (SEP) needed to reach a correct solution.

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YiJie Huang, Xiaocui Yang, Shi Feng, Wen Zhang, Kaisong Song, Yifei Zhang, Daling Wang

LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)

Challenge:	Existing query-based alignment modules enforce uniform cross-attention across all layers, leading to computational redundancy.
Approach:	They propose a framework that allows for asynchronous query-based alignment with large-scale visual features.
Outcome:	The proposed framework matches or surpasses baseline performance while reducing alignment FLOPs by approximately 37% during training and inference.

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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

CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite (2024.lrec-main)

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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Yifei Yuan, Chen Shi, Wang Runze, Liyi Chen, Renjun Hu, Zengming Zhang, Feijun Jiang, Wai Lam

Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer (2022.coling-1)

Challenge:	Recent advances in conversational IR systems have seen a resurgent interest in conversation . generative query rewrite generates reconstructed query based on the conversation history .
Approach:	They propose to use unlabeled data to make further improvements using contrastive co-training paradigm.
Outcome:	The proposed model is robust to noise and language style shift under few-shot and zero-shot scenarios.

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Qiong Nan, Danding Wang, Yongchun Zhu, Qiang Sheng, Yuhui Shi, Juan Cao, Jintao Li

GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning (2025.findings-acl)

Challenge:	Social media spreads both real news and fake news in various domains including politics, health, entertainment, etc.
Approach:	They propose a Domain- and Instance-level Transfer Framework for Fake News Detection which could improve the performance of specific target domains.
Outcome:	The proposed framework improves performance of target domains by hurting other domains, resulting in unsatisfactory performance in the target domain.

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Sifan Zhou, Shuo Wang, Zhihang Yuan, Mingjia Shi, Yuzhang Shang, Dawei Yang

Online Iterative Self-Alignment for Radiology Report Generation (2025.acl-long)

Challenge:	Large Language Models (LLMs) fine-tuning techniques require large Floating Point(FP) computation and are impractical for resource-constrained edge devices.
Approach:	They propose a framework for on-device LLM fine-tuning that eliminates the need for floating-point operations in both inference and training.
Outcome:	The proposed framework reduces memory and compute costs while reducing memory usage.

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Ting Xiao, Lei Shi, Yang Zhang, HaoFeng Yang, Zhe Wang, Chenjia Bai

Rethinking Translation Memory Augmented Neural Machine Translation (2023.findings-acl)

Challenge:	Existing methods for RRG rely on supervised fine-tuning based on data pairs of radiological images and corresponding radiologist-annotated reports.
Approach:	They propose a method that performs supervised fine-tuning on data pairs of radiological images and corresponding radiologist-annotated reports.
Outcome:	The proposed method surpasses existing methods and achieves state-of-the-art performance across multiple evaluation metrics.

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Hongkun Hao, Guoping Huang, Lemao Liu, Zhirui Zhang, Shuming Shi, Rui Wang

Addressing Entity Translation Problem via Translation Difficulty and Context Diversity (2024.findings-acl)

Challenge:	Existing approaches to enhance neural machine translation (NMT) by using a TM have been reported to be effective.
Approach:	They propose a translation memory augmented neural machine translation model that is good at fitting data but more sensitive to fluctuations in training data.
Outcome:	The proposed model achieves consistent gains over conventional and existing models under two variance-preferable scenarios as well as the high resource scenario.

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Tian Liang, Xing Wang, Mingming Yang, Yujiu Yang, Shuming Shi, Zhaopeng Tu

Bi-Tuning with Collaborative Information for Controllable LLM-based Sequential Recommendation (2025.acl-long)

Challenge:	Neural machine translation systems often produce inadequate translations for named entities.
Approach:	They propose a data augmentation strategy to enhance the accuracy of named entity translation by retraining the target named entity pair.
Outcome:	The proposed method improves translation accuracy across test sets and terminology tests.

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Xinyu Zhang, Linmei Hu, Luhao Zhang, Wentao Cheng, Yashen Wang, Ge Shi, Chong Feng, Liqiang Nie

What Factors Influence LLMs’ Judgments? A Case Study on Question Answering (2024.lrec-main)

Challenge:	Existing approaches to optimize sequential recommendation systems rely on item ID sequences, but they lack collaborative knowledge and limited controllability.
Approach:	They propose a simple bi-tuning framework with collaborative information for controllable Large Language Model-based Sequential Recommendation (Laser) they incorporate learnable virtual tokens at prefix and suffix of input text to adapt LLMs with collaborative knowledge .
Outcome:	The proposed framework outperforms state-of-the-art recommendations on real-world datasets.

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Lei Chen, Bobo Li, Li Zheng, Haining Wang, Zixiang Meng, Runfeng Shi, Hao Fei, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

Exploration-Exploitation Reshaping towards Efficient Reasoning for Large Language Models (2026.findings-acl)

Challenge:	Existing studies indicate that Large Language Models perform at a level comparable to humans with advantages of speed and cost-effectiveness in different fields.
Approach:	They propose to introduce four unexplored factors and a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies.
Outcome:	The proposed dimensions of question difficulty and answer quantity provide valuable insights into optimizing LLMs’ performance as judges.

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Yufeng Shi, Weilin Luo, Yuxiang Zhang, Zongmeng Zhang, Haoyang Liu, Yubing Wang, Bin Wang, Wengang Zhou, Houqiang Li

Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (D19-58)

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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Qian Li, Hui Su, Cheng Niu, Daling Wang, Zekang Li, Shi Feng, Yifei Zhang

Route Sparse Autoencoder to Interpret Large Language Models (2025.emnlp-main)

Challenge:	Existing question reformulation models are based on supervised question labels without considering feedback information from answers.
Approach:	They propose a question reformulation model that integrates conversational history information with reinforcement learning.
Outcome:	The proposed model is more effective in conversational machine comprehension with reinforcement learning.

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Wei Shi, Sihang Li, Tao Liang, Mingyang Wan, Guojun Ma, Xiang Wang, Xiangnan He

Explainability & Interpretability in NLP

Challenge:	Sparse autoencoders (SAEs) extract interpretable and monosemantic features in large language models . prior work focused on feature extraction from a single layer, failing to capture activations that span multiple layers.
Approach:	They propose a framework that integrates a routing mechanism with a shared SAE to efficiently extract features from multiple layers.
Outcome:	The proposed framework extracts features from multiple layers while incurring minimal parameter overhead while achieving high interpretability and flexibility.

Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree Editing (2025.acl-long)

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Longhui Zhang, Jiahao Wang, Meishan Zhang, GaoXiong Cao, Ensheng Shi, Mayuchi Mayuchi, Jun Yu, Honghai Liu, Jing Li, Min Zhang

Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning (2025.emnlp-main)

Challenge:	Existing training methods, such as direct instruction fine-tuning, overlook hierarchical relationships among acceleration patterns.
Approach:	They propose a new training paradigm that uses bidirectional tree editing and progressive code acceleration learning to improve LLMs’ CA capabilities.
Outcome:	The proposed training paradigm outperforms prompt-enhanced GPT-4 and current training-based methods on average across five programming languages.

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Yu Liu, Yanan Cao, Xixun Lin, Yanmin Shang, Shi Wang, Shirui Pan

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis (2026.acl-demo)

Challenge:	Existing knowledge graph completion methods ignore inconsistent representation spaces between natural language and graph structures, leading to duplicate works and time-consuming processes.
Approach:	They propose a framework that enhances LLMs for KGC via structure-aware alignment-tuning to align graph embeddings with the natural language space through multi-task contrastive learning.
Outcome:	The proposed framework outperforms state-of-the-art methods on two KGC tasks across four benchmark datasets.

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Zhichao Shi, Cehao Yang, Hao Zhou, Xiaojun Wu, Huajie Li, Xuhui Jiang, Chengjin Xu, Yuanzhuo Wang, Jian Guo

WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)

Challenge:	Existing synthetic data tools are limited by convoluted workflows, fragmented data standards, and limited scalability across modalities.
Approach:	They develop an open-source framework that aims to reduce the technical barrier to synthetic data generation and subsequent model training.
Outcome:	The proposed framework achieves an optimal balance between generation efficiency and data quality.

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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville

Challenge:	Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences.
Approach:	They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically.
Outcome:	The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences.

Mitigating the Discrepancy Between Video and Text Temporal Sequences: A Time-Perception Enhanced Video Grounding method for LLM (2025.coling-main)

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Xuefen Li, Bo Wang, Ge Shi, Chong Feng, Jiahao Teng

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)

Challenge:	Existing video LLMs excel at capturing the overall description of a video but lack the ability to demonstrate an understanding of temporal dynamics and localized content within the video.
Approach:	They propose a Time-Perception Enhanced Video Grounding via Boundary Perception and Temporal Reasoning to improve LLMs' understanding of video temporality.
Outcome:	The proposed method improves on three datasets: ActivityNet, Charades, and DiDeMo (up to 11.2% improvement on R@0.3).

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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang

Speech & Audio in NLP

Challenge:	Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS).
Approach:	They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features.
Outcome:	The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization.

InsightPilot: An LLM-Empowered Automated Data Exploration System (2023.emnlp-demo)

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Pingchuan Ma, Rui Ding, Shuai Wang, Shi Han, Dongmei Zhang

Challenge:	InsightPilot is an LLM-based, automated data exploration system designed to simplify the data exploration process.
Approach:	They propose an LLM-based, automated data exploration system that streamlines the data exploration process.
Outcome:	InsightPilot is an LLM-based, automated data exploration system . it can help users gain valuable insights from their datasets, in a case study and in nl .

Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning (2026.acl-long)

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Chongyuan Dai, Jinpeng Hu, Hongchang Shi, Zhuo Li, Dan Guo, Xun Yang, Meng Wang

HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing (2024.findings-emnlp)

Challenge:	Recent reasoning-augmented LLMs have demonstrated impressive capabilities across a wide range of domains owing to their exceptional text understanding capabilities.
Approach:	They propose a Chinese psychological LLM that integrates empathy, psychological expertise, and reasoning.
Outcome:	The proposed model produces over 75k high-quality psychological questions paired with detailed rationales, generated through and iterative prompt-rationale optimization procedure, along with 73k empathetic dialogues.

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Jing Chen, Xinyu Zhu, Cheng Yang, Chufan Shi, Yadong Xi, Yuxiang Zhang, Junjie Wang, Jiashu Pu, Tian Feng, Yujiu Yang, Rongsheng Zhang

Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language Question And Database Schema With Pseudo-Schema (2025.coling-main)

Challenge:	Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in the realm of literary creation.
Approach:	They propose a framework for unleashing the creativity of large language models (LLMs) they assign LLMs to different roles involved in real-world scenario, they write .
Outcome:	The proposed framework outperforms baselines in terms of coherence, relevance, interestingness and overall quality on automatically generated screenplays.

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Jie Shi, Bo Xu, Jiaqing Liang, Yanghua Xiao, Jia Chen, Chenhao Xie, Peng Wang, Wei Wang

RATION: Entropy-Driven Task-Adaptive Visual Attention Allocation Framework for Multimodal Reasoning (2026.findings-acl)

Challenge:	Recent studies have shifted paradigms and leveraged Large Language Models (LLMs) to tackle the challenging task of Text-to-SQL.
Approach:	They propose a framework that leverages large language models to generate SQL queries . they exploit prior knowledge from the LLM to enhance embedding-based retriever .
Outcome:	The proposed method improves embedding-based retriever and reduces cost.

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Xingle Xu, Fanheng Kong, Dexian Cai, Shi Feng, Xiaocui Yang, Daling Wang, Yifei Zhang

CoDA: Restoring Contextual Dominance via Copy-Encouraged Attention Intervention for Mitigating RAG Hallucinations (2026.findings-acl)

Challenge:	Prior studies have focused on strengthening multimodal reasoning by improving representation alignment or increasing computation, but these methods do not characterize the differences in visual demands across tasks.
Approach:	They propose an entropy-driven task-adaptive visual attention allocation framework that uses visual attention entropic as a control signal to dynamically allocate attention according to task demands.
Outcome:	The proposed framework achieves consistent performance gains across diverse reasoning tasks, datasets, and models, providing a clear direction toward more reliable multimodal reasoning.

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JinWei Shi, Qizhuo Xie, Qianzi Hou, Zhipeng Wang, Wanting Su, Jianhua Zhao, Tao Zheng, Tieke He

Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation (2025.acl-long)

Challenge:	Retrieval-augmented generation reduces hallucination by grounding outputs in external evidence.
Approach:	They propose a lightweight inference-time attention intervention that amplifies evidence-aligned value states to enhance contextual faithfulness and reduce hallucination.
Outcome:	The proposed model reduces hallucination by grounding model outputs in external evidence.

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Dongsheng Zhu, Weixian Shi, Zhengliang Shi, Zhaochun Ren, Shuaiqiang Wang, Lingyong Yan, Dawei Yin

Navigating the OverKill in Large Language Models (2024.acl-long)

Challenge:	Large Language Models (LLMs) demonstrate remarkable capabilities but their ability to autonomously execute complex real-world tasks remains limited.
Approach:	They propose a parallel tool invocation framework that decomposes tasks into parallel tool-using subtasks while aggregating results for subsequent decisions.
Outcome:	The proposed method significantly improves task performance while reducing token consumption and inference time.

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Chenyu Shi, Xiao Wang, Qiming Ge, Songyang Gao, Xianjun Yang, Tao Gui, Qi Zhang, Xuanjing Huang, Xun Zhao, Dahua Lin

BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)

Challenge:	Recent studies have highlighted a tendency among large language models to refuse to answer benign queries.
Approach:	They propose a model-agnostic approach to reduce excessive attention to harmful words like ‘kill’ and a method to decode the next-token predictions by contrastive decoding.
Outcome:	The proposed approach reduces the refusal rate by 20% while having little impact on safety.

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Zhenfei Yang, Beiming Yu, Yuan Cui, Shi Feng, Daling Wang, Yifei Zhang

TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards (2026.acl-long)

Challenge:	Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios.
Approach:	They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation.
Outcome:	The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection.

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Xiqiao Xiong, Ouxiang Li, Zhuo Liu, Moxin Li, Wentao Shi, Fengbin Zhu, Qifan Wang, Fuli Feng

Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt (2023.findings-acl)

Challenge:	Existing approaches to training multi-turn attackers to probe model safety vulnerabilities rely on turn-level optimization, which is insufficient for learning long-term attack strategies.
Approach:	They propose a multi-turn reinforcement learning problem that optimizes the harmfulness of the final-turn response as the outcome reward.
Outcome:	The proposed approach improves attack success rates across multiple models and benchmarks, highlighting the effectiveness of the proposed approach.

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Xiaocui Yang, Shi Feng, Daling Wang, Qi Sun, Wenfang Wu, Yifei Zhang, Pengfei Hong, Soujanya Poria

ES4R: Speech Encoding Based on Prepositive Affective Modeling for Empathetic Response Generation (2026.acl-long)

Challenge:	Existing studies require massive labeled data to train models for multimodal data analysis.
Approach:	They propose a novel multimodal prompt model that captures specific aspect terms in a few-shot scenario.
Outcome:	The proposed model outperforms baselines on two MABSA-related tasks on a few-shot dataset.

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Zhuoyue Gao, Xiaohui Wang, Xiaocui Yang, Wen Zhang, Daling Wang, Shi Feng, Yifei Zhang

LEGENT: Open Platform for Embodied Agents (2024.acl-demos)

Challenge:	Existing speech-to-speech large language models rely on ASR transcription or use encoders to extract latent representations, weakening affective information and contextual coherence in multi-turn dialogues.
Approach:	They propose a framework for speech-based empathetic response generation that captures turn-level affective states and dialogue-level emotional dynamics.
Outcome:	The proposed framework outperforms baselines in automatic and human evaluations and remains robust across different Large Language Model (LLM) backbones.

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Zhili Cheng, Zhitong Wang, Jinyi Hu, Shengding Hu, An Liu, Yuge Tu, Pengkai Li, Lei Shi, Zhiyuan Liu, Maosong Sun

Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity (2025.findings-emnlp)

Challenge:	Existing integrations of large language models and large multimodal models are limited . Existing platforms for developing embodied agents are limited and limited based on open-source software.
Approach:	They propose an open platform for developing embodied agents using LLMs and LMMs.
Outcome:	The proposed platform surpasses GPT-4V in embodied tasks with its model training on LEGENT data.

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Ping Chen, Xiang Liu, Zhaoxiang Liu, Zezhou Chen, Xingpeng Zhang, Huan Hu, Zipeng Wang, Kai Wang, Shuming Shi, Shiguo Lian