Papers by Zhou Zhang

RiOT: Efficient Prompt Refinement with Residual Optimization Tree (2025.acl-long)

Challenge:	Existing research focuses on task-oriented or open-domain dialogue systems with influence skills.
Approach:	They propose to define and introduce a category of social influence dialogue systems that influence users’ cognitive and emotional responses.
Outcome:	The proposed system is task-oriented or goal-oriented, but it is not open-domain.

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Chenyi Zhou, Zhengyan Shi, Yuan Yao, Lei Liang, Huajun Chen, Qiang Zhang

Simple Role Assignment is Extraordinarily Effective for Safety Alignment (2026.findings-acl)

Challenge:	Existing methods for automatic prompt optimization face two challenges: lack of diversity and semantic drift.
Approach:	They propose a framework for automatic prompt optimization that iteratively refines prompts through text gradients and selects the best prompt using perplexity.
Outcome:	The proposed framework outperforms existing prompt optimization methods and manual prompting on commonsense, mathematical, logical, temporal, and semantic reasoning benchmarks.

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Zhou Ziheng, Jiakun Ding, Zhaowei Zhang, Ruosen Gao, Ying Nian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong, Junqi Wang

Challenge:	a new study proposes a role-conditioned pipeline for value alignment . principles alone are incomplete, and they provide little guidance on when and how a value applies in context.
Approach:	They propose a role-conditioned pipeline with role-based critics and a model-free approach that is based on role conditioning.
Outcome:	The proposed approach outperforms principle-based, Chain-of-Thought and other benchmarks.

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

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

Prior Knowledge and Memory Enriched Transformer for Sign Language Translation (2022.findings-acl)

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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Tao Jin, Zhou Zhao, Meng Zhang, Xingshan Zeng

HSDreport: Heart Sound Diagnosis with Echocardiography Reports (2024.findings-emnlp)

Challenge:	Existing methods for sign language translation do not explore all of them . visual and textual understanding and additional prior knowledge learning are challenging .
Approach:	They propose a method which integrates auxiliary information into vanilla transformer for SLT . they use visual-textual context information and additional auxiliary knowledge of a word .
Outcome:	The proposed method improves the understanding of sign language videos with visual and textual understanding and additional prior knowledge learning.

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Zihan Zhao, Pingjie Wang, Liudan Zhao, Yuchen Yang, Ya Zhang, Kun Sun, Xin Sun, Xin Zhou, Yu Wang, Yanfeng Wang

Challenge:	Existing methods for heart sound diagnosis are limited to a few fixed categories and do not utilize echocardiography reports, the gold standard in the diagnosis of related diseases.
Approach:	They propose a benchmark that mandates the direct utilization of heart sounds obtained from auscultation to predict echocardiography reports.
Outcome:	The proposed method outperforms existing methods and existing multimodal LLMs in detecting key abnormalities in heart sounds.

Length-Induced Embedding Collapse in PLM-based Models (2025.acl-long)

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Yuqi Zhou, Sunhao Dai, Zhanshuo Cao, Xiao Zhang, Jun Xu

Encoding Spreadsheets for Large Language Models (2024.emnlp-main)

Challenge:	In text embeddings from PLMs are essential for many NLP applications, but performance degrades on longer texts.
Approach:	They propose a method which mitigates the phenomenon of Length Collapse . they propose TempScale to ensure more consistent embeddings across different text lengths .
Outcome:	The proposed method improves performance on MTEB and LongEmbed by 0.94% on short and 1.10% on long texts.

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Haoyu Dong, Jianbo Zhao, Yuzhang Tian, Junyu Xiong, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, Dongmei Zhang

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

Challenge:	Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs).
Approach:	They propose a structural-anchor-based compression, inverse index translation, and data-format-aware aggregation module to compress spreadsheets effectively.
Outcome:	The proposed method outperforms the existing model in GPT4 and achieves a state-of-the-art 78.9% F1 score.

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

Capability Salience Vector: Fine-grained Alignment of Loss and Capabilities for Downstream Task Scaling Law (2025.acl-long)

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

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Qiming Ge, Shuhao Xing, Songyang Gao, Yunhua Zhou, Yicheng Zou, Songyang Zhang, Zhi Chen, Hang Yan, Qi Zhang, Qipeng Guo, Kai Chen

Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models (2026.findings-acl)

Challenge:	Large language models have demonstrated impressive performance across a wide range of tasks, but this achievement comes with the trade-off of significant computational demands.
Approach:	They propose a scaling law that decomposes the overall validation loss and assigns different importance weights to tokens to assess a specific meta-capability.
Outcome:	The proposed model can predict the loss trending of models across different levels of computation without a gap between validation loss and model's downstream capabilities.

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Chenhao Li, Dandan Song, Changzhi Zhou, Jun Yang, Yuhang Tian, Huipeng Ma, Guangyuan Feng, Luan Zhang, Xudong Li, Ke Duan

An In-depth Study on Internal Structure of Chinese Words (2021.acl-long)

Challenge:	Large language models are trained on static corpora but deployed in a dynamic world . a foundational tension remains between time and the ability to understand it .
Approach:	They formalize temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers.
Outcome:	The proposed framework formalizes temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers . the framework induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy .

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Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan

Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback (2025.acl-long)

Challenge:	Unlike English letters, Chinese characters have rich and specific meanings.
Approach:	They propose to model Chinese words' internal structures as dependency trees with 11 labels for distinguishing syntactic relationships.
Outcome:	The proposed model of Chinese word-internal structures shows it can be used to parse sentences . it shows that the model can be applied to a sentence-level task with a competitive dependency parser.

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Jiakang Yuan, Xiangchao Yan, Bo Zhang, Tao Chen, Botian Shi, Wanli Ouyang, Yu Qiao, Lei Bai, Bowen Zhou

Challenge:	Recent studies show that AI-assisted research methods can improve research efficiency . a closed-loop framework is used to enhance the automation level of scientific research .
Approach:	They propose a closed-loop LLM-driven framework to enhance the automation level of scientific research.
Outcome:	The proposed framework improves the efficiency of scientific research by improving data analysis, accelerating computation, and fostering novel idea generation.

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

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

Friend-training: Learning from Models of Different but Related Tasks (2023.eacl-main)

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

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Mian Zhang, Lifeng Jin, Linfeng Song, Haitao Mi, Xiabing Zhou, Dong Yu

ToolGrad: Efficient Tool-use Dataset Generation with Textual “Gradients” (2026.findings-acl)

Challenge:	Current self-training methods focus on improving model performance on a single task.
Approach:	They propose a cross-task self-training framework where models trained to do different tasks are used in iterative training, pseudo-labeling, and retraining processes to help each other for better selection of pseudo-labeled labels.
Outcome:	The proposed framework achieves the best performance compared to baselines on two dialogue understanding tasks.

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Zhongyi Zhou, Kohei Uehara, Haoyu Zhang, Jingtao Zhou, Lin Gu, Ruofei Du, Zheng Xu, Tatsuya Harada

D2-RAG: Dual-Decision Retrieval-Augmented Generation via Multi-Dimensional Uncertainty and Utility-Aware Decoding (2026.findings-acl)

Challenge:	Prior work synthesizes tool-use LLM datasets by first generating a user query, then complex tool-using annotations like DFS.
Approach:	They propose an agentic framework that synthesizes user queries and generates valid tool-use chains . they propose a dataset with more complex tool use, lower cost, and almost 100% pass rate .
Outcome:	Experiments show that tools trained on ToolGrad outperform expensive baseline datasets and proprietary LLMs.

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Jinshuo Zhang, Xiaoding Zhou, Weiyu Zhang, Guoqiang Chen, Ying Lian, Xiaoyang Meng, Yonghe Chen, Hongjiao Guan, Jiasheng Si, Wenpeng Lu

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

Challenge:	Retrieval-Augmented Generation (RAG) mitigates hallucinations in large language models by incorporating external knowledge.
Approach:	They propose a dual-decision retrieval-augmented generation that integrates multi-dimensional uncertainty estimation to decide whether to retrieve and employs adaptive contrastive decoding to handle retrieved contexts of varying quality.
Outcome:	The proposed model outperforms baselines on four medical question-answering datasets while suppressing interference from noisy contexts.

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

Multi-Relational Probabilistic Event Representation Learning via Projected Gaussian Embedding (2023.findings-acl)

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

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Linhai Zhang, Congzhi Zhang, Deyu Zhou

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

Challenge:	Existing methods for event representation learning ignore relations and uncertainty of events . Experimental results show that the proposed approach outperforms other state-of-the-art baselines on both existing and newly constructed datasets.
Approach:	They propose a novel approach to learning multi-relational probabilistic event embeddings based on contrastive learning.
Outcome:	The proposed method outperforms existing benchmarks on existing and newly constructed datasets.

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

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

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

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

BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages (2025.acl-long)

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

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Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif, Christine de Kock, Nirmal Surange, Daniela Teodorescu, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Alham Fikri Aji, Felermino D. M. A. Ali, Ilseyar Alimova, Vladimir Araujo, Nikolay Babakov, Naomi Baes, Ana-Maria Bucur, Andiswa Bukula, Guanqun Cao, Rodrigo Tufiño, Rendi Chevi, Chiamaka Ijeoma Chukwuneke, Alexandra Ciobotaru, Daryna Dementieva, Murja Sani Gadanya, Robert Geislinger, Bela Gipp, Oumaima Hourrane, Oana Ignat, Falalu Ibrahim Lawan, Rooweither Mabuya, Rahmad Mahendra, Vukosi Marivate, Alexander Panchenko, Andrew Piper, Charles Henrique Porto Ferreira, Vitaly Protasov, Samuel Rutunda, Manish Shrivastava, Aura Cristina Udrea, Lilian Diana Awuor Wanzare, Sophie Wu, Florian Valentin Wunderlich, Hanif Muhammad Zhafran, Tianhui Zhang, Yi Zhou, Saif M. Mohammad

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

Challenge:	Emotion recognition is an umbrella term for several NLP tasks, but most work on high-resource languages has focused on low-resourced languages.
Approach:	They propose to use emotion recognition to describe perceived emotions in 28 different languages and across several domains to identify and annotate the datasets.
Outcome:	The proposed datasets cover low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers.

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

Text Embedding as Treatment: A Meta Causal Approach for Robust Sentiment Classification (2026.findings-acl)

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

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Fengxiang Cheng, Chuan Zhou, Xiang Li, Haoxuan Li, Wen-li Wang, Jinkun Chen, Mingming Gong, Kun Zhang

ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)

Challenge:	Existing methods for sentiment classification use binary treatment of words . Existing approaches limit generalizability to novel words and low-frequency words if there is a word in a sentence that is not treated .
Approach:	They propose a meta-causal approach that uses a single training task to identify causal words for arbitrary words.
Outcome:	The proposed method reduces the spurious correlation between word treatment and sentiment classification by removing words with low treatment effects from a pre-trained language model.

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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao

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

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

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

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

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

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

Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification (2021.emnlp-main)

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

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Shuhuai Ren, Jinchao Zhang, Lei Li, Xu Sun, Jie Zhou

Scaling LLM Inference Efficiently with Optimized Sample Compute Allocation (2025.naacl-long)

Challenge:	Data augmentation aims to alleviate the overfitting issue in low-resource or class-imbalanced situations.
Approach:	They propose a framework called Text AutoAugment to enhance training samples . they use a Bayesian optimization algorithm to search for the best policy .
Outcome:	The proposed framework outperforms baseline methods on six benchmark datasets.

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Kexun Zhang, Shang Zhou, Danqing Wang, William Yang Wang, Lei Li

Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training (2023.acl-long)

Challenge:	Existing methods to optimize sample allocations for large language models fail to account for the optimal sampling configuration.
Approach:	They propose an algorithm that optimizes sample allocation by finding an optimal mix of different inference configurations.
Outcome:	The proposed algorithm achieves better accuracy on SWE-Bench with 3x less compute than the default configuration.

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Yan Zeng, Wangchunshu Zhou, Ao Luo, Ziming Cheng, Xinsong Zhang

LLMaAA: Making Large Language Models as Active Annotators (2023.findings-emnlp)

Challenge:	Empirical results show that CCLM significantly outperforms the prior state-of-the-art with an average absolute improvement of over 10%.
Approach:	They introduce a pre-training framework that unifies cross-lingual and cross-modal pre-trained models with shared architectures and objectives.
Outcome:	The proposed framework outperforms the state-of-the-art in two multi-lingual datasets and two multilingual image-text retrieval datasets.

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Ruoyu Zhang, Yanzeng Li, Yongliang Ma, Ming Zhou, Lei Zou

GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs (2026.acl-long)

Challenge:	Existing supervised learning methods in natural language processing require large amounts of data.
Approach:	They propose an active learning loop that takes LLMs as annotators and puts them into an active loop to determine what to annotate efficiently.
Outcome:	The proposed model outperforms existing models with few-shot performance in two NLP tasks.

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Weidong Tang, Jierui Li, Yueling Hou, Zihan Mei, Can Zhang, Xinyan Wan, Zhiyuan Liang, Pengfei Zhou, Yang You, Wangbo Zhao

Linguistics & Cognitive NLP

Challenge:	Existing models for general intelligence fail to model how mental states interact and crystallize into group-level outcomes.
Approach:	They propose a multimodal benchmark for group-level Theory of Mind (ToM) to probe nonlinear collective behavior.
Outcome:	The proposed model performs significantly below human levels, exposing blind spots in modeling social structures and nonlinear collective behavior.

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

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

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

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

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

Shortcuts Arising from Contrast: Towards Effective and Lightweight Clean-Label Attacks in Prompt-Based Learning (2024.emnlp-main)

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

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Xiaopeng Xie, Ming Yan, Xiwen Zhou, Chenlong Zhao, Suli Wang, Yong Zhang, Joey Zhou

VideoEraser: Concept Erasure in Text-to-Video Diffusion Models (2025.emnlp-main)

Challenge:	Prompt-based learning paradigms are vulnerable to backdoor attacks, requiring false activations and false data augmentation.
Approach:	They propose a method that uses triggers to create stronger shortcuts by leveraging activation values and data selection strategies to create the shortcuts.
Outcome:	The proposed method is based on the concept that a backdoor acts as a shortcut and can achieve high effectiveness and stealthiness at low poisoning rates.

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Naen Xu, Jinghuai Zhang, Changjiang Li, Zhi Chen, Chunyi Zhou, Qingming Li, Tianyu Du, Shouling Ji

FinToolSyn: A forward synthesis Framework for Financial Tool-Use Dialogue Data with Dynamic Tool Retrieval (2026.findings-acl)

Challenge:	Experimental results show that VideoEraser outperforms prior methods regarding efficacy, integrity, fidelity, robustness, and generalizability.
Approach:	They propose a training-free framework that prevents T2V diffusion models from generating videos with undesirable concepts even when explicitly prompted with those concepts.
Outcome:	The proposed framework outperforms existing methods in erasure, celebrity erasion, and explicit content erasing tasks.

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Caishuang Huang, Yang Qiao, Rongyu Zhang, Junjie Ye, Pu Lu, null Wuwenxi, Meng Zhou, Xiku Du, Qi Zhang, Tao Gui, Xuanjing Huang

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

Challenge:	Existing data synthesis methods rely on static tools to generate queries . this approach fails to capture the implicit, event-driven nature of real-world needs .
Approach:	They propose a forward synthesis framework to generate high-quality financial dialogues . they construct a repository of 43,066 tools and synthesize over 148k dialogue instances .
Outcome:	Experiments show that models trained on FinToolSyn achieve a 21.06% improvement . the framework is designed to generate high-quality financial dialogues .

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

Cross-domain NER with Generated Task-Oriented Knowledge: An Empirical Study from Information Density Perspective (2024.emnlp-main)

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

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Zhihao Zhang, Sophia Lee, Junshuang Wu, Dong Zhang, Shoushan Li, Erik Cambria, Guodong Zhou

DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference (2024.findings-acl)

Challenge:	Cross-domain Named Entity Recognition (CDNER) is crucial for Knowledge Graph (KG) construction and natural language processing (NLP)
Approach:	They propose to automatically generate task-oriented knowledge using large language models (LLMs) and then employ task-orientated pre-training (TOPT) to facilitate domain adaptation.
Outcome:	The proposed model can learn to distinguish between different entities and improve its domain adaptation.

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Jialong Wu, Linhai Zhang, Deyu Zhou, Guoqiang Xu

SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval (2026.findings-acl)

Challenge:	Existing approaches to debiase ABSA focus on single-variable causal inference . aspect-based sentiment analysis models are prone to learn spurious correlations from annotation biases .
Approach:	They propose a framework based on multivariable causal inference for debiasing ABSA . they propose to model different types of biases based upon different causal intervention methods .
Outcome:	The proposed framework tackles different types of biases based on different intervention methods.

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Xin Xie, Dongyun Xue, Wuguannan Yao, Mingxiao Feng, Wengang Zhou, Xiang Qi, Houqiang Li, Peng Zhang

RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)

Challenge:	a new framework casts LLM planning as non-parametric retrieval, but high latency of inference-time search and supervised fine-tuning are limitations.
Approach:	They propose a framework that casts LLM planning as non-parametric retrieval . they leverage Monte Carlo Tree Search to explore the solution space .
Outcome:	Empirical results show that SGA-MCTS can match the performance of SOTA systems without task-specific fine-tuning.

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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu

Mitigating the Inconsistency Between Word Saliency and Model Confidence with Pathological Contrastive Training (2022.findings-acl)

Challenge:	recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability.
Approach:	They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs.
Outcome:	The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models.

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Pengwei Zhan, Yang Wu, Shaolei Zhou, Yunjian Zhang, Liming Wang

Continuous-Time Attention: PDE-Guided Mechanisms for Long-Sequence Transformers (2025.emnlp-main)

Challenge:	Neural networks are used for various NLP tasks, but their complexity makes them difficult to interpret.
Approach:	They propose a framework to mitigate the model pathology and obtain more interpretable models by using contrastive learning and saliency-based samples augmentation to calibrate the sentences representation.
Outcome:	The proposed framework can mitigate the model pathology and generate more interpretable models while keeping the model performance.

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Yukun Zhang, Xueqing Zhou

How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden States (2024.findings-emnlp)

Challenge:	Existing approaches to optimize attention for long sequences have been limited by their computational cost.
Approach:	They propose a framework that infuses partial differential equations into the Transformer’s attention mechanism to better handle long sequences.
Outcome:	The proposed framework achieves consistent performance gains over standard and long-sequence Transformer variants across a range of tasks.

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Zhenhong Zhou, Haiyang Yu, Xinghua Zhang, Rongwu Xu, Fei Huang, Yongbin Li

FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs’ Responsiveness to Human Feedback (2025.emnlp-main)

Challenge:	Large language models (LLMs) rely on safety alignment to avoid malicious user inputs.
Approach:	They employ weak classifiers to explain LLM safety through the intermediate hidden states.
Outcome:	The proposed model can identify malicious and normal inputs and detect malicious ones without jailbreak.

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Youquan Li, Miao Zheng, Fan Yang, Guosheng Dong, Bin Cui, Weipeng Chen, Zenan Zhou, Wentao Zhang

Rethinking Machine Ethics – Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? (2024.findings-naacl)

Challenge:	Existing research focuses on benchmarking LLMs in single-turn dialogues, neglecting the nuanced nature of human feedback within real-world usage scenarios.
Approach:	They propose a fine-grained, multi-task benchmark designed to evaluate LLMs’ responsiveness to human feedback under real-world usage scenarios in Chinese.
Outcome:	The proposed benchmarks show that human feedback can significantly impact LLMs’ responsiveness in real-world usage scenarios.

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Jingyan Zhou, Minda Hu, Junan Li, Xiaoying Zhang, Xixin Wu, Irwin King, Helen Meng

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

Challenge:	Existing approaches to making moral judgments are mostly bottom-up and lack explainability.
Approach:	They propose a top-down framework to steer Large Language Models to perform moral reasoning with well-established moral theories.
Outcome:	The proposed framework can integrate various moral theories on moral datasets.

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

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

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

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

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

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

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

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

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

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

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

MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills (2026.eacl-long)

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Zonghai Yao, Zihao Zhang, Chaolong Tang, Xingyu Bian, Youxia Zhao, Zhichao Yang, Junda Wang, Huixue Zhou, Won Seok Jang, Feiyun Ouyang, Hong Yu

Challenge:	Current clinical LLM benchmarks fail to evaluate advanced clinical skills in AI and large language models (LLMs).
Approach:	They propose a framework to evaluate large language models (LLMs) using two instruction-following tasks designed to reflect real clinical scenarios.
Outcome:	The proposed framework evaluates LLMs through two instruction-following tasks designed to reflect real clinical scenarios.

Don’t Half-listen: Capturing Key-part Information in Continual Instruction Tuning (2025.acl-long)

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Yongquan He, Wenyuan Zhang, Xuancheng Huang, Peng Zhang, Lingxun Meng, Xiang Zhou, Ke Zeng, Xunliang Cai

LLaMA-Omni 2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis (2025.acl-long)

Challenge:	Existing methods to improve instruction tuning for large language models may cause catastrophic forgetting (CF) CF is a problem where previously learned abilities are degraded .
Approach:	They propose a continual instruction tuning method that uses key-part information gain to replay data and refine training objective.
Outcome:	The proposed method achieves superior performance on both seen and held-out tasks.

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Qingkai Fang, Yan Zhou, Shoutao Guo, Shaolei Zhang, Yang Feng

Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	LLaMA-Omni 2 is a series of speech language models (SpeechLMs) based on large language models.
Approach:	They introduce a series of speech language models capable of real-time speech interaction . LLaMA-Omni 2 trains on 200K multi-turn speech dialogue samples .
Outcome:	The proposed speech language models surpass state-of-the-art models on spoken question answering and speech instruction.

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Linhai Zhang, Ziyang Gao, Deyu Zhou, Yulan He

Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference (P19-1)

Challenge:	Existing systems rely on black-box neural networks, which lack interpretability, which is crucial in mental health contexts.
Approach:	They propose a Retrieval-augmented generation framework for Explainable depression detection that retrieves evidence from clinical interview transcripts, providing explanations for predictions.
Outcome:	The proposed framework retrieves evidence from clinical interview transcripts, providing explanations for predictions.

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He Bai, Yu Zhou, Jiajun Zhang, Chengqing Zong

Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems (2022.emnlp-main)

Challenge:	Existing models for SLU use explicit memory representations, but the context memory is under-exploited.
Approach:	They propose a dialogue logistic inference task to consolidate the context memory with SLU in a multi-task framework.
Outcome:	The proposed model improves slot filling and domain classification performance in a multi-task framework.

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Jialiang Xu, Mengyu Zhou, Xinyi He, Shi Han, Dongmei Zhang

Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition (P19-1)

Challenge:	Numerical Question Answering is the task of answering questions that require numerical capabilities.
Approach:	They propose to conduct numerical capability diagnosis on a series of Numerical Question Answering systems and datasets.
Outcome:	The proposed approach relieves existing systems’ lack of robust numerical capabilities.

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Joey Tianyi Zhou, Hao Zhang, Di Jin, Hongyuan Zhu, Meng Fang, Rick Siow Mong Goh, Kenneth Kwok

CASE – Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement (2026.eacl-long)

Challenge:	Named entity recognition (NER) is an important step in most natural language processing (NLP) applications.
Approach:	They propose a dual-adversarial neural transfer method for addressing low-resource Named Entity Recognition (NER) they propose 'Generalized Resource-Adversarial Discriminator' and 'accidental training'
Outcome:	The proposed method improves on low-resource Named Entity Recognition (NER) with two variants, i.e., DATNet-F and DATNET-P, and adversarial training is adopted to boost model generalization.

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Gaifan Zhang, Yi Zhou, Danushka Bollegala

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation (2025.findings-acl)

Challenge:	Recent approaches use semantic similarity to improve the quality of sentence embeddings, but it is difficult to measure the similarity between sentences.
Approach:	They propose a condition-aware sentence embedding method that uses an LLM encoder to create an embeddable sentence under a given condition.
Outcome:	The proposed method improves the performance of LLM-based embeddings and the isotropy of the embeddable space despite requiring a small number of dimensions.

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Wenxiang Guo, Yu Zhang, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, ZheTao Chen, Wenhao Xu, Fei Wu, Zhou Zhao

Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method (2025.acl-long)

Challenge:	Existing automated singing annotation (ASA) methods tackle isolated aspects of the annotation pipeline.
Approach:	They propose a framework that addresses transcription, alignment, and refined style annotations.
Outcome:	The proposed framework delivers comprehensive multi-level annotations encompassing: (1) precise phoneme-audio alignment, (2) robust note transcription and temporal localization, (3) expressive vocal technique identification, and (4) global stylistic characterization including emotion and pace.

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Yupei Ren, Xinyi Zhou, Ning Zhang, Shangqing Zhao, Man Lan, Xiaopeng Bai

Argument Mining

Challenge:	Existing research on argument mining has proposed various argument annotation schemes and tasks.
Approach:	They propose a framework comprising 14 fine-grained relation types to capture the interplay between argument components for a thorough understanding of argument structure.
Outcome:	The proposed framework captures the interplay between argument components for a thorough understanding of argument structure.

Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation (2022.acl-long)

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Chulun Zhou, Fandong Meng, Jie Zhou, Min Zhang, Hongji Wang, Jinsong Su

An Empirical Study of Position Bias in Modern Information Retrieval (2025.findings-emnlp)

Challenge:	Existing studies focus on how to effectively exploit bidirectional global contexts in neural machine translation models.
Approach:	They propose a Confidence Based Bidirectional Global Context Aware training framework for NMT . they incorporate bidirectional global context to the NMT model on unconfidently-predicted target words .
Outcome:	The proposed framework improves the NMT model on three large-scale translation datasets by +1.02, +0.57 BLEU scores.

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Ziyang Zeng, Dun Zhang, Jiacheng Li, null Zoupanxiang, Yudong Zhou, Yuqing Yang

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

Challenge:	a new evaluation framework is used to assess the extent and impact of position bias in information retrieval.
Approach:	They introduce a position-aware retrieval benchmark and a diagnostic metric to quantify position bias . they compare models with BM25, dense embedding models, ColBERT-style late-interaction models .
Outcome:	The proposed framework evaluates retrieval models for position bias from a worst-case perspective.

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

Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards (2026.acl-long)

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

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Xinyu Tang, Yuliang Zhan, Zhixun Li, Xin Zhao, Zhenduo Zhang, Zujie Wen, Zhiqiang Zhang, Jun Zhou

FlowRAG: Synergizing Explicit Reasoning via Frequency-Aware Multi-Granularity Graph Flow (2026.findings-acl)

Challenge:	Large reasoning models are typically trained using reinforcement learning with verifiable reward (RLVR) positive and negative self-generated rollouts are used to update the model's policy . positive samples sharpen existing correct reasoning patterns, while negative samples encourage exploration of new reasoning paths.
Approach:	They propose a method that allocates advantage signals to key tokens across different polarities.
Outcome:	The proposed method improves the ability of large reasoning models to learn from their own generated rollouts.

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Bihao Zhan, Zongsheng Cao, Jie Zhou, Bo Zhang, Liang He

Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering (2020.acl-main)

Challenge:	Existing methods for Graph-based retrieval-augmented generation rely on implicit semantic relevance propagation.
Approach:	They propose a semantic-aware retrieval framework that improves both semantic recall and explicit reasoning.
Outcome:	Extensive experiments show that FlowRAG improves both semantic recall and explicit reasoning.

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Ming Yan, Hao Zhang, Di Jin, Joey Tianyi Zhou

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

Challenge:	Existing MCQA datasets are small in size, which increases difficulty of model learning and generalization.
Approach:	They propose a multi-source meta transfer framework for low-resource multiple-choice question answering . they extend meta learning by incorporating multiple training sources to learn a generalized feature representation across domains .
Outcome:	The proposed framework is independent of backbone language models and can bridge the distribution gap between training sources and target.

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

Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection (2021.emnlp-main)

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

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Xincheng Ju, Dong Zhang, Rong Xiao, Junhui Li, Shoushan Li, Min Zhang, Guodong Zhou

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

Challenge:	Existing studies on aspect-level sentiment analysis focus on extracting aspect terms and sentiment polarities separately.
Approach:	They propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-dimensional aspect-level sentiment analysis.
Outcome:	The proposed approach can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects.

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

End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)

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

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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu

From Lists to Emojis: How Format Bias Affects Model Alignment (2025.acl-long)

Challenge:	Existing methods for grounding video frames with dense annotations require enormous amount of human effort.
Approach:	They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames .
Outcome:	The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques .

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Xuanchang Zhang, Wei Xiong, Lichang Chen, Tianyi Zhou, Heng Huang, Tong Zhang

Causal Intervention Improves Implicit Sentiment Analysis (2022.coling-1)

Challenge:	Format biases in reinforcement learning from human feedback are underexplored . despite its effectiveness, RLHF faces challenges, including policy and regulatory constraints .
Approach:	They extend the study of preference biases beyond verbosity bias to a wider range of format biase . they show that with a small amount of biased data, they can inject significant bias into the reward model .
Outcome:	The proposed approach can be easily exploited by large language models to achieve higher rankings on popular benchmarks like AlpacaEval and LMSYS Chatbot Arena.

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Siyin Wang, Jie Zhou, Changzhi Sun, Junjie Ye, Tao Gui, Qi Zhang, Xuanjing Huang

TextMixer: Mixing Multiple Inputs for Privacy-Preserving Inference (2023.findings-emnlp)

Challenge:	Existing neural models struggle with implicit sentiment analysis because they latch onto spurious correlations, resulting in poor generalization and robustness.
Approach:	They propose a CausaL intervention model for implicit sEntiment ANalysis using instrumental variable to eliminate confounding causal effects and extract the pure causal effect between sentence and sentiment.
Outcome:	The proposed model extracts the pure causal effect between sentence and sentiment using instrumental variable.

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Xin Zhou, Yi Lu, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Pre-trained language models (PLMs) are often deployed as cloud services, enabling users to upload textual data and perform inference remotely.
Approach:	They propose a privacy-preserving inference framework called MixPi which aims to obfuscate a user's private input by mixing it with multiple other inputs.
Outcome:	The proposed framework surpasses existing privacy-preserving methods on token and sentence classification tasks.

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

Real, Fake, or Manipulated? Detecting Machine-Influenced Text (2025.findings-emnlp)

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

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Yitong Wang, Zhongping Zhang, Margherita Piana, Zheng Zhou, Peter Gerstoft, Bryan A. Plummer

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

Challenge:	Prior work on machine generated text detection focused on identifying whether document was human or machine written, ignoring these fine-grained uses.
Approach:	They propose a machine-influenced text detector that learns to separate text samples from four primary types . the detector uses a subcategory guidance module to help separate the fine-grained categories .
Outcome:	The proposed detector outperforms the state-of-the-art in five LLMs and six domains.

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

ReportLogic: Evaluating Logical Quality in Deep Research Reports (2026.acl-long)

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

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Jujia Zhao, Zhaoxin Huan, Zihan Wang, Xiaolu Zhang, Jun Zhou, Suzan Verberne, Zhaochun Ren

Efficient Continue Training of Temporal Language Model with Structural Information (2023.findings-emnlp)

Challenge:	Existing evaluation frameworks that evaluate large language models for Deep Research largely ignore this requirement.
Approach:	They propose a benchmark that quantifies report-level logical quality through a reader-centric lens of auditability.
Outcome:	The proposed model quantifies logical quality through a reader-centric lens of auditability.

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Zhaochen Su, Juntao Li, Zikang Zhang, Zihan Zhou, Min Zhang

Scalable Data Synthesis through Human-like Cognitive Imitation and Data Recombination (2025.emnlp-main)

Challenge:	Existing temporal language models are limited by the superficial temporal information brought by timestamps, which fails to learn the inherent changes of linguistic components.
Approach:	They propose a method that captures syntactically changed tokens and captures the relationship between the time prefix and tokens.
Outcome:	The proposed method outperforms existing temporal language models on two datasets and three tasks.

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Zhongyi Ye, Weitai Zhang, Xinyuan Zhou, Yongxin Zhu, Ninghui Rao, Enhong Chen

Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge (2022.acl-long)

Challenge:	Large language models (LLMs) rely on massive amounts of training data, however, the quantity of empirically observed data is limited.
Approach:	They propose a data synthesis framework that mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources.
Outcome:	The proposed framework mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources thereby enhancing advanced reasoning capabilities in large language models.

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Linhai Zhang, Xuemeng Hu, Boyu Wang, Deyu Zhou, Qian-Wen Zhang, Yunbo Cao

TextLap: Customizing Language Models for Text-to-Layout Planning (2024.findings-emnlp)

Challenge:	Recent studies have shown that using external knowledge such as pre-trained word embeddings or pre-train language models only achieved limited performance improvements but with huge computational overhead.
Approach:	They propose to incorporate external knowledge into neural topic modeling by pre-trained word embeddings (PWEs) or pre-train language models (PLMs) they propose to fine-tune the neural topic model on the target dataset and reduce the huge size of training data.
Outcome:	The proposed approach outperforms current state-of-the-art neural topic models and some topic modeling approaches enhanced with PWEs or PLMs on three datasets and greatly reduces the huge size of training data.

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Jian Chen, Ruiyi Zhang, Yufan Zhou, Jennifer Healey, Jiuxiang Gu, Zhiqiang Xu, Changyou Chen

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

Challenge:	Creating 2D graphical layouts from text alone is challenging in traditional settings.
Approach:	They propose to customize LLMs to allow users to generate professional looking layouts by simply inputting text instructions.
Outcome:	The proposed method outperforms existing benchmarks for document generation and graphical design benchmarks.

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

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

Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)

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Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Yuan Dong, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye

Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning (2026.eacl-long)

Challenge:	Biology-Instructions is the first large-scale instruction-tuning dataset for multi-omics biological sequences.
Approach:	They propose a large-scale instruction-tuning dataset for multi-omics biological sequences . they propose 'chatMultiOmics' to overcome limitations of current LLMs on multi-ome tasks .
Outcome:	The proposed dataset bridges LLMs and complex biological sequence-related tasks while maintaining conversational fluency.

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Qifan Yu, Zhenyu He, Sijie Li, Zhou Xun, Jun Zhang, Jingjing Xu, Di He

Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training (2022.coling-1)

Challenge:	Chain-of-Thought prompting is a powerful technique for enhancing language model’s reasoning capabilities, but generating long and correct CoT trajectories is challenging.
Approach:	They propose to align the steps of Chain-of-Thought reasoning with loop iterations and apply intermediate supervision during the training of Looped Transformers.
Outcome:	The proposed method generates accurate reasoning chains for complex problems exceeding training length, and improves performance of the auto-regressive model.

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Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su

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

Challenge:	Existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.
Approach:	They propose an iterative scheduled data-switch training framework to mitigate this problem by injecting noise into authentic examples and indiscriminately exploiting two types of examples.
Outcome:	The proposed model outperforms several competitive benchmarks on four translation benchmarks.

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

ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection (2026.findings-acl)

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

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Boyang Li, Hongzhe Shou, Yuanyuan Liang, JingBin Zhang, Fang Zhou

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

Challenge:	Existing toxic content detection methods focus on sentence-level classification but fail to provide readable and contiguous toxic evidence spans.
Approach:	They propose an explainability-oriented method for Chinese toxic content detection methods . they refine saliency cues into fine-grained toxic spans with lightweight LLM guidance .
Outcome:	The proposed method improves classification accuracy and toxic span extraction while preserving efficient encoder-based inference and producing more coherent explanations.

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

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

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

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

Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias (2023.findings-acl)

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

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Zhiyuan Zhang, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

DPDLLM: A Black-box Framework for Detecting Pre-training Data from Large Language Models (2024.findings-acl)

Challenge:	Existing methods to fine-tune pre-trained language models (PLMs) are not safe, since the fine-uning process is invisible to the user.
Approach:	They propose a technique to study the dynamic process of fine-tuning for finding poisonous dimensions using diffusion theory.
Outcome:	The proposed approach can detect poisonous dimensions with abnormal dynamics, purify them and fine-tune them on a clean dataset.

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Baohang Zhou, Zezhong Wang, Lingzhi Wang, Hongru Wang, Ying Zhang, Kehui Song, Xuhui Sui, Kam-Fai Wong

AdaTooler-V: Adaptive Tool-Use for Images and Videos (2026.findings-acl)

Challenge:	Existing methods to detect pretraining data from large language models are unrealistic to them.
Approach:	They propose to detect pre-training data from LLM in a black-box way by using GPT-2 as reference model and feed it with sequence probabilities to detect whether it was used to train it.
Outcome:	The proposed framework outperforms existing methods on the benchmark datasets and shows that it is effective on different popular LLMs.

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Chaoyang Wang, Kaituo Feng, Dongyang Chen, Zhongyu Wang, Zhixun Li, Sicheng Gao, Meng Meng, Xu Zhou, Manyuan Zhang, Yuzhang Shang, Xiangyu Yue

1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)

Challenge:	Existing models exhibit blind tool-use reasoning patterns, which significantly increases inference overhead and degrades model performance.
Approach:	They propose an MLLM that performs adaptive tool-use by determining whether a visual problem truly requires tools.
Outcome:	The proposed model outperforms existing methods in visual reasoning tasks.

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Yue Huang, Chenrui Fan, Yuan Li, Siyuan Wu, Tianyi Zhou, Xiangliang Zhang, Lichao Sun

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

Challenge:	Large Language Models (LLMs) have been recognized for their impressive capabilities in natural language processing (NLP).
Approach:	They propose a method to enhance the multilingual performance of Large Language Models by aggregating knowledge from diverse languages.
Outcome:	The proposed method reduces the performance disparity across languages and offers valuable insights for further exploration.

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

Cognitive Modeling

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

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

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

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

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

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

A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis (D19-1)

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

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Yunlong Liang, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen, Jie Zhou

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

Challenge:	Existing models use aspect-independent encoders for sentence representation generation.
Approach:	They propose an aspect-guided deep transition model which guides the sentence encoding from scratch with a specially-designed deep transition architecture.
Outcome:	The proposed model outperforms existing models on multiple datasets on aspect-category sentiment analysis and aspectterm sentiment analysis without additional features.

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

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

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

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

A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure (2020.acl-main)

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

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Longyin Zhang, Yuqing Xing, Fang Kong, Peifeng Li, Guodong Zhou

Generating Fluent Adversarial Examples for Natural Languages (P19-1)

Challenge:	Text-level discourse parsing of discourse rhetorical structure (DRS) is a fundamental research topic in natural language processing.
Approach:	They propose a top-down neural architecture for text-level discourse parsing . they cast the parser as a recursive split point ranking task .
Outcome:	The proposed top-down approach is more suitable for text-level discourse parsing.

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Huangzhao Zhang, Hao Zhou, Ning Miao, Lei Li

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

Challenge:	Current methods for building adversarial attackers for NLP are inefficient as the gradient is discarded.
Approach:	They propose an adversarial attacker which performs Metropolis-Hastings sampling with the guidance of gradients to solve these problems.
Outcome:	The proposed algorithm outperforms the baseline model on attacking capability on IMDB and SNLI.

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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

PositionID: LLMs can Control Lengths, Copy and Paste with Explicit Positional Awareness (2024.findings-emnlp)

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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Noah Wang, Feiyu Duan, Yibo Zhang, Wangchunshu Zhou, Ke Xu, Wenhao Huang, Jie Fu

Enhancing Neural Models with Vulnerability via Adversarial Attack (2020.coling-main)

Challenge:	Large Language Models (LLMs) have impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding.
Approach:	They propose two methods to improve the model’s adherence to length constraints and copy-paste accuracy without compromising response quality.
Outcome:	The proposed methods improve the model’s adherence to length constraints and copy-paste accuracy without compromising response quality.

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Rong Zhang, Qifei Zhou, Bo An, Weiping Li, Tong Mo, Bo Wu

Pre-training for Abstractive Document Summarization by Reinstating Source Text (2020.emnlp-main)

Challenge:	Existing work on adversarial attack to improve performance of NLSM tasks has not been done.
Approach:	They propose a general two-stage training framework to enhance neural models with Vulnerability via adversarial attack.
Outcome:	The proposed framework improves neural models with Vulnerability via adversarial attack on NLSM datasets.

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Yanyan Zou, Xingxing Zhang, Wei Lu, Furu Wei, Ming Zhou

Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)

Challenge:	Abstractive document summarization models are often trained on limited supervised data . authors present three objectives for pretraining abstractive summarizing models .
Approach:	They propose to pre-train a SEQ2SEQ based abstractive summarization model on unlabeled text.
Outcome:	The proposed method improves on two benchmark summarization datasets with 19GB of text . the goal is sentence reordering, next sentence generation and masked document generation .

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Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou

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

Challenge:	Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs.
Approach:	They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model.
Outcome:	The proposed approach outperforms the state-of-the-art on English AMR benchmarks . it significantly outperformed the state of the art on the benchmarks, with 29.66 and 31.82 BLEU scores .

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

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

Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings (2024.lrec-main)

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Gaifan Zhang, Yi Zhou, Danushka Bollegala

SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility (2026.acl-long)

Challenge:	Sentence embeddings produced by pretrained language models are high dimensional (ca. 1024-4096) this is problematic when representing large numbers of sentences in memory- or compute-constrained devices.
Approach:	They propose to use Principal Component Analysis to reduce the dimensionality of sentence embeddings produced by pretrained language models to reduce their complexity.
Outcome:	The proposed methods reduce the dimensionality of sentence embeddings by 50% without incurring significant loss in performance in multiple downstream tasks.

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Xuyang Zhi, Peilun Zhou, Chengqiang Lu, Hang Lv, Yiwei Liang, Rongyang Zhang, Yan Gao, null Yiwu, Yao Hu, Hongchao Gu, Defu Lian, Hao Wang, Enhong Chen

Challenge:	Large Language Models (LLMs) are shifting the focus from single verifiable tasks toward complex, open-ended real-world scenarios.
Approach:	They propose a framework that automatically adjusts reward weights and data importance to synchronize learning intent with data utility for optimal performance.
Outcome:	The proposed framework improves model capabilities across all domains and scales.

Improving Discriminative Capability of Reward Models in RLHF Using Contrastive Learning (2024.emnlp-main)

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Lu Chen, Rui Zheng, Binghai Wang, Senjie Jin, Caishuang Huang, Junjie Ye, Zhihao Zhang, Yuhao Zhou, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Current methods rely on ranking losses to teach reward model to assess preferences, but they are susceptible to noise and ambiguous data, often failing to deeply understand human intentions.
Approach:	They propose a method that incorporates contrastive learning into the reward modeling process to enhance generalization and stabilize the reinforcement learning training process.
Outcome:	The proposed method enhances generalization of the reward model, stabilizes the reinforcement learning training process, and improves the final alignment with human preferences.

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

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

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

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

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

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

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

Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation (D19-1)

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

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Xin Tan, Longyin Zhang, Deyi Xiong, Guodong Zhou

Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging? (2022.naacl-main)

Challenge:	Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context.
Approach:	They propose a hierarchical model to learn document context for document-level neural machine translation . they use a sentence encoder to capture intra-sentence dependencies and a document encoder .
Outcome:	The proposed model significantly improves document-level translation performance over strong baselines.

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Xiang Zhou, Shiyue Zhang, Mohit Bansal

NUT-RC: Noisy User-generated Text-oriented Reading Comprehension (2020.coling-main)

Challenge:	Recent Part-Of-Speech (POS) induction models assume certain independence assumptions that do not hold in real languages.
Approach:	They propose a Masked Part-of-Speech Model (MPoSM) that can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction.
Outcome:	The proposed model can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction.

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Rongtao Huang, Bowei Zou, Yu Hong, Wei Zhang, AiTi Aw, Guodong Zhou

ReCreate: Reasoning and Creating Domain Agents Driven by Experience (2026.acl-long)

Challenge:	Existing RC models focus on extractive or generative, but ignore integration of them.
Approach:	They propose a noisy user-generated text-oriented RC model that integrates extractive and generative RC models by a multi-task learning mechanism and an answer selection module.
Outcome:	The proposed model outperforms state-of-the-art models on Twitter.

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Zhezheng Hao, Hong Wang, Jian Luo, Jianqing Zhang, Yuyan Zhou, Qiang Lin, Can Wang, Hande Dong, Jiawei Chen

An Element is Worth a Thousand Words: Enhancing Legal Case Retrieval by Incorporating Legal Elements (2024.findings-acl)

Challenge:	Large Language Model (LLM) agents are reshaping the industrial landscape, but tasks differ widely, making them labor-intensive to build.
Approach:	They propose an experience-driven framework for the automatic creation of domain agents . they leverage agent interaction histories to provide rich concrete signals on success or failure .
Outcome:	The proposed framework outperforms human-designed agents and existing methods in experiments across diverse domains.

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Chenlong Deng, Zhicheng Dou, Yujia Zhou, Peitian Zhang, Kelong Mao

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

Challenge:	Existing methods for legal case retrieval lack the definition of relevance for legal cases . however, the definition goes beyond the common semantic relevance of ad-hoc retrieval.
Approach:	They propose a legal element dataset that incorporates legal elements into a semi-automatic method . they propose two models to enhance legal search using legal elements .
Outcome:	The proposed models outperform existing methods in enhancing legal search using legal elements.

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

An Evaluation Resource for Grounding Translation Errors (2025.findings-emnlp)

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

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Sujin Chen, Kang Wang, Zixuan Zhou, Xiangyu Duan, Wanqun Zhang, Hao Yang, Jinsong Su, Min Zhang

EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification (2022.naacl-main)

Challenge:	Current fine-grained error analyses do not ground the errors to the reasons why the annotated text spans are erroneous.
Approach:	They use a bi-directional grounding scheme to ground erroneous text in two directions . if the error spans of both directions are consistent, the explanation is valid .
Outcome:	The proposed grounding process improves translation error detection significantly.

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Minyi Zhao, Lu Zhang, Yi Xu, Jiandong Ding, Jihong Guan, Shuigeng Zhou

LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)

Challenge:	Existing methods for data augmentation do not fully exploit the potential of DA in NLP.
Approach:	They propose an easy and plug-in framework for data augmentation to support effective text classification.
Outcome:	The proposed framework outperforms existing methods in most cases, but not using agent networks or pre-trained generation networks.

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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang

Interpreting Twitter User Geolocation (2020.acl-main)

Challenge:	Currently, most research focuses on the bidding algorithms used within auction mechanisms.
Approach:	They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process.
Outcome:	The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process.

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Ting Zhong, Tianliang Wang, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Yi Yang

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

Challenge:	Existing methods for identifying user geolocation suffer from a lack of interpretability on the corresponding results.
Approach:	They adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting locations.
Outcome:	The proposed method provides meaningful explanations on prediction results and also uncovers the so-called "black-box" GNN-based models by investigating the effect of individual nodes.

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

Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations (2024.naacl-long)

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

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Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu

Debiased Contrastive Learning of Unsupervised Sentence Representations (2022.acl-long)

Challenge:	Sentence embeddings are typically learned to recognize the semantic relation between two text inputs.
Approach:	They introduce a contrastively-learned contextual embedding model for fine-grained semantic representation of text.
Outcome:	The proposed model is able to produce contextual embeddings corresponding to different atomic propositions, i.e. semantic equivalence between propositions across different text sequences.

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Kun Zhou, Beichen Zhang, Xin Zhao, Ji-Rong Wen

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)

Challenge:	Recent studies have shown that contrastive learning improves pre-trained language models to derive high-quality sentence representations.
Approach:	They propose a framework to punish false negatives and generate noise-based negatives to guarantee the uniformity of the representation space.
Outcome:	The proposed framework improves pre-trained language models while pushing apart irrelevant negatives to guarantee the uniformity of the representation space.

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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation (2026.acl-long)

Challenge:	Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text.
Approach:	They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types.
Outcome:	The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages.

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Xiangxu Zhang, Lei Li, Yanyun Zhou, Xiao Zhou, Yingying Zhang, Xian Wu

Challenge:	Current evaluations of large language models (LLMs) are limited in capturing key challenges of clinical diagnostic scenarios.
Approach:	They propose a dynamic benchmark for medical diagnostics that provides a stress test of diagnostic robustness.
Outcome:	The proposed model provides a stress test of diagnostic robustness and veracity, helpfulness and consistency.

MCIL: Multimodal Counterfactual Instance Learning for Low-resource Entity-based Multimodal Information Extraction (2024.lrec-main)

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Baohang Zhou, Ying Zhang, Kehui Song, Hongru Wang, Yu Zhao, Xuhui Sui, Xiaojie Yuan

Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.acl-long)

Challenge:	Existing methods to perform multimodal information extraction only investigated entity-based tasks under supervised learning with adequate labeled data.
Approach:	They propose to investigate the entity-based MIE tasks under the low-resource settings by decomposing the features into image, entity, and context factors.
Outcome:	The proposed method is able to perform on two public MIE benchmark datasets and the experimental results confirm it.

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Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun

ProofInfer: Generating Proof via Iterative Hierarchical Inference (2022.emnlp-main)

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

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Zichu Fei, Qi Zhang, Xin Zhou, Tao Gui, Xuanjing Huang

Adversarial Learning for Discourse Rhetorical Structure Parsing (2021.acl-long)

Challenge:	Existing proof generation models focus on generating several proof paths instead of a whole tree.
Approach:	They propose a method that generates the proof tree via iterative hierarchical inference . they propose coding the proof as plain text without losing structure information .
Outcome:	The proposed proof generation model significantly improves performance on widely-used datasets.

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Longyin Zhang, Fang Kong, Guodong Zhou

A Compact and Language-Sensitive Multilingual Translation Method (P19-1)

Challenge:	Existing top-down discourse rhetorical structure parsers make local decisions and ignore global parsing.
Approach:	They propose a method to transform gold standard and predicted constituency trees into tree diagrams with two color channels.
Outcome:	The proposed method improves performance on RST-DT and CDTB corpora and can leverage global context.

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Yining Wang, Long Zhou, Jiajun Zhang, Feifei Zhai, Jingfang Xu, Chengqing Zong

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

Challenge:	Existing paradigms for multilingual neural machine translation do not make full use of language commonality and parameter sharing.
Approach:	They propose a multilingual neural machine translation paradigm with one encoder-decoder model that makes full use of language commonality and parameter sharing.
Outcome:	The proposed method outperforms strong standard multilingual translation systems on WMT and IWSLT datasets.

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

CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data (2026.acl-long)

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

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Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett, Rafael Mosquera, Sara Hincapié Monsalve, Thom Vaughan, Damian Stewart, Malte Ostendorff, Idris Abdulmumin, Vukosi Marivate, Shamsuddeen Hassan Muhammad, Atnafu Lambebo Tonja, Hend Al-Khalifa, Nadia Ghezaiel Hammouda, Verrah Akinyi Otiende, Tack Hwa Wong, Jakhongir Saydaliev, Melika Nobakhtian, Muhammad Ravi Shulthan Habibi, Chalamalasetti Kranti, Carol Muchemi, Khang Nguyen, Faisal Muhammad Adam, Luis Frentzen Salim, Reem Alqifari, Cynthia Jayne Amol, Joseph Marvin Imperial, Ilker Kesen, Ahmad Mustafid, Pavel Stepachev, Leshem Choshen, David Anugraha, Hamada Nayel, Seid Muhie Yimam, Vallerie Alexandra Putra, My Chiffon Nguyen, Azmine Toushik Wasi, Gouthami Vadithya, Rob Van Der Goot, Lanwenn ar C’horr, Karan Dua, Andrew Yates, Mithil Bangera, Yeshil Bangera, Hitesh Laxmichand Patel, Shu Okabe, Fenal Ashokbhai Ilasariya, Dmitry Gaynullin, Genta Indra Winata, Yiyuan Li, Juan Pablo Martínez, Amit Agarwal, Ikhlasul Akmal Hanif, Raia Abu Ahmad, Esther Adenuga, Filbert Aurelian Tjiaranata, Weerayut Buaphet, Michael Anugraha, Sowmya Vajjala, Benjamin L Rice, Azril Hafizi Amirudin, Jesujoba Oluwadara Alabi, Srikant Panda, Yassine Toughrai, Bruhan Kyomuhendo, Daniel Ruffinelli, null Akshata, Manuel Goulão, Ej Zhou, Ingrid Gabriela Franco Ramirez, Cristina Aggazzotti, Konstantin Dobler, Jun Kevin, Quentin Pagès, Nicholas Andrews, Nuhu Ibrahim, Mattes Ruckdeschel, Amr Keleg, Mike Zhang, Casper Rufaro Muziri, Saron Samuel, Sotaro Takeshita, Kun Kerdthaisong, Luca Foppiano, Rasul Dent, Tommaso Green, Ahmad Mustapha Wali, Kamohelo Makaaka, Vicky Feliren, Inshirah Idris, Hande Celikkanat, Abdulhamid Abubakar, Jean Maillard, Benoît Sagot, Thibault Clérice, Kenton Murray, Sarah K. K. Luger

Text Classification

Challenge:	Language identification (LID) is a fundamental step in curating multilingual corpora.
Approach:	They introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages.
Outcome:	The proposed benchmark covers 109 languages and shows that existing evaluations overestimate accuracy for many languages in the web domain.

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

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

MathMixup: Boosting LLM Mathematical Reasoning with Difficulty-Controllable Data Synthesis and Curriculum Learning (2026.findings-acl)

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

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Xuchen Li, Jing Chen, Xuzhao Li, Hao Liang, Xiaohuan Zhou, Taifeng Wang, Wentao Zhang

Retrieval-Augmented Machine Translation with Unstructured Knowledge (2025.findings-emnlp)

Challenge:	Existing data synthesis methods suffer from limited diversity and lack precise control over problem difficulty, making them insufficient for efficient training paradigms such as curriculum learning.
Approach:	They propose a data synthesis paradigm that generates high-quality, difficulty-controllable mathematical reasoning problems through hybrid and decomposed strategies.
Outcome:	The proposed paradigm outperforms existing methods and improves mathematical reasoning abilities.

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Jiaan Wang, Fandong Meng, Yingxue Zhang, Jie Zhou

Towards Human-aligned Evaluation for Linear Programming Word Problems (2024.lrec-main)

Challenge:	Retrieval-augmented generation (RAG) is a new approach to enhance large language models (LLMs).
Approach:	They propose a multi-task training method to teach LLMs how to use information from multilingual documents during their translation.
Outcome:	The proposed method improves LLMs by 1.6-3.1 BLEU and 1.0-2.0 COMET scores in En-Zh, and 1.7-2.9 BLUE and 2.1-2.7 COMET score in En de.

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Linzi Xing, Xinglu Wang, Yuxi Feng, Zhenan Fan, Jing Xiong, Zhijiang Guo, Xiaojin Fu, Rindra Ramamonjison, Mahdi Mostajabdaveh, Xiongwei Han, Zirui Zhou, Yong Zhang

A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation (2025.coling-main)

Challenge:	Existing evaluation methodologies for MWPs diverge from human judgment and face challenges in recognizing mathematically equivalent answers.
Approach:	They propose an evaluation metric rooted in graph edit distance that features benefits such as permutation invariance and more accurate program equivalence identification.
Outcome:	The proposed evaluation metric features benefits such as permutation invariance and more accurate program equivalence identification.

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Shijie Zhou, Ruiyi Zhang, Yufan Zhou, Changyou Chen

Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning (2023.acl-long)

Challenge:	Large multimodal models struggle with text-rich images because of inadequate training data.
Approach:	They propose to use annotations from human annotators to generate instruction data by a hybrid approach to generate text prompts for large language models.
Outcome:	The proposed model improves multimodal alignment for text-rich images by using human annotations and tailored text prompts for large language models.

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Ye Wang, Wang Lin, Shengyu Zhang, Tao Jin, Linjun Li, Xize Cheng, Zhou Zhao

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

Challenge:	Recent work on spoken video grounding challenges extracting semantic information from speech . previous studies focused on textual queries, but recent work focuses on spoken queries .
Approach:	They propose a framework for weakly-supervised spoken video grounding to represent cross-modal semantics without expensive temporal annotations.
Outcome:	The proposed framework is more efficient than existing methods.

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

Inductive Relation Inference of Knowledge Graph Enhanced by Ontology Information (2023.findings-emnlp)

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

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Wentao Zhou, Jun Zhao, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Existing methods to inference knowledge graphs lack ontology information, which is often too sparse.
Approach:	They propose a knowledge graph inductive inference method that fuses ontology information to learn the semantic information of entities.
Outcome:	The proposed method outperforms large language models like ChatGPT on two benchmark datasets and improves the MRR metrics by 15.4% and 44.1%, respectively.

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

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

DDGIP: Radiology Report Generation Through Disease Description Graph and Informed Prompting (2025.findings-naacl)

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Chentao Huang, Guangli Li, Xinjiong Zhou, Yafeng Ren, Hongbin Zhang

Instance Regularization for Discriminative Language Model Pre-training (2022.emnlp-main)

Challenge:	Automatic radiology report generation is challenging due to inherent biases in medical imaging data.
Approach:	They propose a disease description graph that encapsulates comprehensive and pertinent disease information.
Outcome:	The proposed model outperforms state-of-the-art models on two widely-used datasets . the proposed model is based on a three-layer decoder and improves on existing models .

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Zhuosheng Zhang, Hai Zhao, Ming Zhou

Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation (2024.acl-long)

Challenge:	Existing studies have optimized independent strategies of ennoising or denosing . Existing methods treat training instances equally throughout the training process .
Approach:	They propose to use ennoising and denoising to train discriminative pre-trained language models . they propose to model the complexity of restoring the original sentences from corrupted ones .
Outcome:	Experimental results show that the proposed method improves pre-training efficiency, effectiveness, and robustness.

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Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou, Lifeng Jin, Linfeng Song, Haitao Mi, Helen Meng

CoGenesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Following (2024.acl-long)

Challenge:	Existing approaches to addressing factual inaccuracies require high-quality human factuality annotations to mitigate these hallucinations.
Approach:	They propose to leverage the self-evaluation capability of an LLM to provide training signals that steer the model towards factuality.
Outcome:	The proposed approach significantly improves factual accuracy over LLMs across three key knowledge-intensive tasks on TruthfulQA and BioGEN.

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Kaiyan Zhang, Jianyu Wang, Ermo Hua, Biqing Qi, Ning Ding, Bowen Zhou

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

Challenge:	Large-scale language models (LLMs) are increasingly exposed to private data and are becoming more and more prevalent.
Approach:	They propose a collaborative generation framework that integrates large and small language models to address privacy concerns logically.
Outcome:	The proposed framework combines large and small models to address privacy concerns logically.

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

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

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

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

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

Enhancing Dialogue Generation with Conversational Concept Flows (2023.findings-eacl)

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Siheng Li, Wangjie Jiang, Pengda Si, Cheng Yang, Qiu Yao, Jinchao Zhang, Jie Zhou, Yujiu Yang

Joint Intent Detection and Entity Linking on Spatial Domain Queries (2020.findings-emnlp)

Challenge:	Existing studies show that explicitly modeling concept flows with a large commonsense knowledge graph improves response quality, but there is a gap between the knowledge graph and the conversation.
Approach:	They propose to model human conversational concept flows with a commonsense knowledge graph . they extract abundant concepts and relations from natural conversations and build a conversation-aware knowledge graph.
Outcome:	The proposed method performs better than baselines on a large-scale reddit conversation dataset.

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Lei Zhang, Runze Wang, Jingbo Zhou, Jingsong Yu, Zhenhua Ling, Hui Xiong

Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus (2023.emnlp-main)

Challenge:	Spatial domain queries have unique properties making them more challenging for language understanding than common conversational queries.
Approach:	They propose a language understanding framework for spatial domain queries that jointly learns the intent detection and entity linking tasks on a voice assistant service.
Outcome:	The proposed framework outperforms baseline methods with a significant margin.

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Tianhang Zhang, Lin Qiu, Qipeng Guo, Cheng Deng, Yue Zhang, Zheng Zhang, Chenghu Zhou, Xinbing Wang, Luoyi Fu

ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning (2025.emnlp-main)

Challenge:	Existing methods for detecting hallucinations in LLMs rely on external knowledge for reference retrieval or require sampling multiple responses for consistency verification.
Approach:	They propose a reference-free, uncertainty-based method for detecting hallucinations in Large Language Models that imitates human focus in factuality checking from three aspects: focus on the most informative keywords; focus on unreliable tokens in historical context; focus of token properties such as token type and token frequency.
Outcome:	The proposed method achieves state-of-the-art performance across all evaluation metrics and eliminates the need for additional information.

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Ziqing Qiao, Yongheng Deng, Jiali Zeng, Dong Wang, Lai Wei, Guanbo Wang, Fandong Meng, Jie Zhou, Ju Ren, Yaoxue Zhang

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

Challenge:	Existing methods for fine-tuning-based compression suffer from verbose outputs, increasing computational overhead.
Approach:	They propose a framework to generate concise reasoning chains using Confidence Injection and Early Stopping.
Outcome:	The proposed framework reduces the length of the model by up to 50% while maintaining high task accuracy.

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

FinKario: Event-Enhanced Automated Construction of Financial Knowledge Graph (2026.acl-long)

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

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Xiang Li, Penglei Sun, Wanyun Zhou, Zikai Wei, Yongqi Zhang, Xiaowen Chu

Hybrid and Collaborative Passage Reranking (2023.findings-acl)

Challenge:	Equity research reports are crucial resources for investors, but lack professional analysis and the rapid evolution of market events outpaces their update cycles.
Approach:	They propose an event-Enhanced automated construction of financial knowledge graph (FinKario) that automatically integrates real-time company fundamentals and market events through prompt-driven extraction guided by professional institutional templates.
Outcome:	The proposed model outperforms financial LLMs by 18.81% and institutional strategies by 17.85% on average in backtesting.

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Zongmeng Zhang, Wengang Zhou, Jiaxin Shi, Houqiang Li

PM2F2N: Patient Multi-view Multi-modal Feature Fusion Networks for Clinical Outcome Prediction (2022.findings-emnlp)

Challenge:	Existing solutions to passage reranking focus on enriching the interaction between query and each passage separately, neglecting the context among the top-ranked passages.
Approach:	They propose a Hybrid and Collaborative Passage Reranking method that leverages the similarity measurements of upstream retrievers for passage collaboration.
Outcome:	Experiments show that HybRank improves over existing methods and improves performance.

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Ying Zhang, Baohang Zhou, Kehui Song, Xuhui Sui, Guoqing Zhao, Ning Jiang, Xiaojie Yuan

Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation (P19-1)

Challenge:	Existing methods focused on time series data but ignored clinical notes . fusion of multi-modal features of patients from different views is not feasible due to the time series and clinical notes data being stored as time series.
Approach:	They propose to combine time series and clinical notes to fuse multi-modal features of patients from different perspectives using graph neural networks.
Outcome:	The proposed method is superior to existing models on MIMIC-III benchmark.

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Chenze Shao, Yang Feng, Jinchao Zhang, Fandong Meng, Xilin Chen, Jie Zhou

Balancing Fidelity and Plasticity: Aligning Mixed-Precision Fine-Tuning with Linguistic Hierarchies (2026.findings-acl)

Challenge:	Experimental results show that the Reinforce-NAT system surpasses the baseline NAT system by a significant margin on BLEU without decelerating the decoding speed.
Approach:	They propose a sequence-level training method and a Transformer decoder to fuse the target sequential information into the top layer of the decoded Transformer.
Outcome:	The proposed model surpasses the baseline NAT system on BLEU without decelerating the decoding speed and achieves comparable translation performance to the autoregressive Transformer model with considerable speedup.

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Changhai Zhou, Shiyang Zhang, Yuhua Zhou, Jun Gao, Qian Qiao, Shichao Weng, Weizhong Zhang, Cheng Jin

Using active learning to expand training data for implicit discourse relation recognition (D18-1)

Challenge:	Existing quantization-aware fine-tuning methods decouple weight precision and adapter capacity, overlooking that a layer’s ability to adapt is constrained by the information preserved in its frozen weights.
Approach:	They propose a framework that jointly optimizes per-layer quantization bit-width and LoRA rank.
Outcome:	Experiments on LLaMA and Qwen models show that the proposed framework matches or approaches 16-bit baselines while using substantially less memory.

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Yang Xu, Yu Hong, Huibin Ruan, Jianmin Yao, Min Zhang, Guodong Zhou

Hongkuan Zhang, Saku Sugawara, Akiko Aizawa, Lei Zhou, Ryohei Sasano, Koichi Takeda

Challenge:	Existing methods to determine semantic relations between text spans are limited in the field of discourse-level relation recognition.
Approach:	They propose to expand the training data set using the corpus of explicitly-related arguments by arbitrarily dropping the overtly presented discourse connectives.
Outcome:	The proposed model expands the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives.

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Learning Dialogue Representations from Consecutive Utterances (2022.naacl-main)

Challenge:	Existing image captioning approaches treat image-caption pairs indistinctly without considering the differences in their learning difficulties.
Approach:	They propose a pretrained vision–language model that measures cross-modal similarity and a model that uses cross-module similarity to measure the difficulty of captioning.
Outcome:	The proposed model achieves superior performance and competitive convergence speed to baselines without incurring additional training costs.

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Zhihan Zhou, Dejiao Zhang, Wei Xiao, Nicholas Dingwall, Xiaofei Ma, Andrew Arnold, Bing Xiang

EDTC: A Corpus for Discourse-Level Topic Chain Parsing (2021.findings-emnlp)

Challenge:	Dialogue Sentence Embedding (DSE) is a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue-oriented tasks.
Approach:	They propose a self-supervised contrastive learning method that learns dialogue representations suitable for a wide range of dialogue tasks.
Outcome:	The proposed method outperforms baselines on five dialogue tasks on a few-shot and zero-shot datasets.

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Longyin Zhang, Xin Tan, Fang Kong, Guodong Zhou

Discourse & Pragmatics

Challenge:	Discourse analysis is a fundamental part of natural language processing.
Approach:	They propose a discourse-level topic chain parsing system which can be automated . they propose lexical cohesion modeling instead of lexically measuring topic structure .
Outcome:	The proposed system is robust and reliable, and can provide high reliability and low confidence scores.

Span-based Localizing Network for Natural Language Video Localization (2020.acl-main)

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Hao Zhang, Aixin Sun, Wei Jing, Joey Tianyi Zhou

DAEA: Enhancing Entity Alignment in Real-World Knowledge Graphs Through Multi-Source Domain Adaptation (2025.coling-main)

Challenge:	Existing approaches to NLVL are either ranking tasks or regressing the target video span.
Approach:	They propose a video span localizing network to solve a natural language video localization task using a span-based QA approach.
Outcome:	The proposed network outperforms the state-of-the-art methods on three benchmark datasets.

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Linyan Yang, Shiqiao Zhou, Jingwei Cheng, Fu Zhang, Jizheng Wan, Shuo Wang, Mark Lee

DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing (2026.findings-acl)

Challenge:	Entity Alignment (EA) is a critical task in Knowledge Graph (KG) integration.
Approach:	They propose a novel approach that leverages the data characteristics of synthetic benchmarks to improve performance in real-world datasets.
Outcome:	The proposed approach outperforms state-of-the-art models on real-world datasets and achieves a 29.94% improvement in Hits@1 on DOREMUS and 5.64% improvement on AGROLD.

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Hongzhi Zhang, Yuanze Hu, Tinghai Zhang, Jia Fu, Tao Wang, Junwei Jing, Zhaoxin Fan, Wei Bi, Ruiming Tang, Han Li, Guorui Zhou, Kun Gai

Improving Multi-turn Dialogue Modelling with Utterance ReWriter (P19-1)

Challenge:	Large Language Models (LLMs) are evolving towards autonomous agents . retrieval capabilities are well-benchmarked, but post-retrieval synthesis is under-evaluated due to open-ended writing.
Approach:	They propose a benchmark to evaluate information consolidation capabilities using survey papers as gold standards.
Outcome:	The proposed benchmark analyzes the post-retrieval synthesis stage of large language models . it leverages high-quality survey papers as gold standards and reverse-engineers research requests . the proposed benchmark outperforms single-turn generation and reduces hallucinations .

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Hui Su, Xiaoyu Shen, Rongzhi Zhang, Fei Sun, Pengwei Hu, Cheng Niu, Jie Zhou

Paraphrasing

Challenge:	Recent research has achieved impressive results in single-turn dialogue modelling, but multi-turn models still remain challenging.
Approach:	They propose to rewrite human utterances as a pre-process to help multi-turn dialgoue modelling.
Outcome:	The proposed architecture achieves remarkably good performance on the utterance rewriting task.

A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition (2023.findings-acl)

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Limao Xiong, Jie Zhou, Qunxi Zhu, Xiao Wang, Yuanbin Wu, Qi Zhang, Tao Gui, Xuanjing Huang, Jin Ma, Ying Shan

Towards General Agentic Intelligence via Environment Scaling (2026.findings-acl)

Challenge:	Existing models for named entity recognition (NER) are based on large-scale labeled datasets, which always obtain using crowdsourcing.
Approach:	They propose a CONfidence-based partial Label Learning method to integrate prior and posterior confidences for crowd-annotated named entity recognition models.
Outcome:	The proposed model improves on real-world and synthetic datasets compared with baselines.

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Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Ningyu Zhang, Fei Huang, Wentao Zhang, Jingren Zhou

PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026.acl-long)

Challenge:	Diverse real-world APIs require precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments.
Approach:	They propose a framework that scales up environments to enable agentic intelligence . they use a two-phase agent fine-tuning strategy to first endow agents with basic agentic capabilities, then specializing them for domain-specific contexts.
Outcome:	Experiments on -bench, -Bench, and ACEBench show that the model significantly enhances the models’ function-calling capability.

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Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Xiangyu Zhang, Heung-Yeung Shum

Xiaomingbot: A Multilingual Robot News Reporter (2020.acl-demos)

Challenge:	Parallel Coordinated Reasoning (PaCoRe) overcomes a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window.
Approach:	They propose a training-and-inference framework to overcome a central limitation of language models: their inability to scale test-time compute (TTC) under a fixed context window.
Outcome:	The proposed model scales to multi-million-token effective TTC without exceeding context limits.

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Runxin Xu, Jun Cao, Mingxuan Wang, Jiaze Chen, Hao Zhou, Ying Zeng, Yuping Wang, Li Chen, Xiang Yin, Xijin Zhang, Songcheng Jiang, Yuxuan Wang, Lei Li

BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language? (2024.findings-emnlp)

Challenge:	Xiaomingbot is a multilingual and multimodal software robot with four capabilities: news generation, news translation, news reading and avatar animation.
Approach:	They propose to build a multilingual and multimodal software robot with four inte- gal capabilities: news generation, news translation, news reading and avatar animation.
Outcome:	The proposed system generates Chinese news, then reads it in multiple languages and generates an animated avatar reading it.

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Zongmeng Zhang, Jinhua Zhu, Wengang Zhou, Xiang Qi, Peng Zhang, Houqiang Li

A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (2022.coling-1)

Challenge:	Dense retrieval systems focus on optimizing text embedding space while overlooking Boolean logic in language.
Approach:	They propose a task to investigate whether retrieval systems can comprehend Boolean logic in language.
Outcome:	The proposed method is based on a benchmark dataset covering complex queries containing basic Boolean logic and corresponding annotated passages.

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Jie Zhou, Qi Zhang, Qin Chen, Qi Zhang, Liang He, Xuanjing Huang

LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases (2022.coling-1)

Challenge:	Event argument extraction (EAE) aims to extract arguments with given roles from texts.
Approach:	They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets.
Outcome:	The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE.

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Zichu Fei, Xin Zhou, Tao Gui, Qi Zhang, Xuanjing Huang

mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding (2024.findings-emnlp)

Challenge:	Existing KBQG models focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph.
Approach:	They propose a controlled generation framework for Question Generation over Knowledge Bases that generates questions with out-of-vocabulary (OOV) predicates.
Outcome:	The proposed framework outperforms existing methods significantly on three widely-used benchmark datasets SimpleQuestion, PathQuestions, and WebQuestIONS.

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Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou

Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing (2022.coling-1)

Challenge:	Existing Multimodal Large Language Models lack general structure understanding abilities for text-rich document images.
Approach:	They propose to use unified structure learning to boost the performance of MLLMs by encoding structure information into text-rich images.
Outcome:	The proposed model achieves state-of-the-art on 10 visual document understanding benchmarks.

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Shilin Zhou, Qingrong Xia, Zhenghua Li, Yu Zhang, Yu Hong, Min Zhang

Don’t Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding (2026.findings-acl)

Challenge:	Using end-to-end span-based SRL, we propose a word-based graph parsing task for word-level representation of spans . compared with word-driven SRL, span-Based SRL is more complex due to difficulties in determining argument boundaries.
Approach:	They propose to cast end-to-end span-based SRL as a word-based graph parsing task . they propose a constrained Viterbi procedure to ensure the legality of the output graph .
Outcome:	The proposed model can parse 669/252 sentences per second without and with pre-trained models.

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Quanwei Tang, Dong Zhang, Shoushan Li, Guodong Zhou

mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding (2025.acl-long)

Challenge:	Existing question answering (QA) datasets for long audio meetings suffer from acoustic information loss and poor long-term dependency capture.
Approach:	They propose a question answering dataset that captures three core dimensions of long-form audio meeting content.
Outcome:	The proposed model captures three core dimensions of long-form audio meeting content: complex semantics, multi-speaker interactions, and quite long timestamps.

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Anwen Hu, Haiyang Xu, Liang Zhang, Jiabo Ye, Ming Yan, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou

S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA (2026.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) have improved document understanding performance but generate thousands of visual tokens for a single document image, leading to excessive GPU memory and slower inference times.
Approach:	They propose a high-resolution document compression module to generate 324 tokens for a single document image.
Outcome:	The proposed module reduces first token latency by more than 50% and improves document comprehension performance.

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Minghan Li, Junjie Zou, Xinxuan Lv, Chao Zhang, Guodong Zhou

Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs (2024.emnlp-main)

Challenge:	Retrieval-augmented generation grounds language models in external evidence, but multi-hop question answering remains difficult . iterative pipelines must control what to retrieve next and when evidence is adequate.
Approach:	They propose an iterative framework with an explicit controller, S2G-Judge . they map structured gap items into the next retrieval query to produce stable retrieval trajectories .
Outcome:	Experiments on TriviaQA, HotpotQA, and 2WikiMultiHopQA show that S2G-RAG improves multi-hop QA performance and robustness under multi-turn retrieval.

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Xin Zhou, Ping Nie, Yiwen Guo, Haojie Wei, Zhanqiu Zhang, Pasquale Minervini, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang

Smart-Start Decoding for Neural Machine Translation (2021.naacl-main)

Challenge:	Existing research seeks to enhance RAG performance by retrieving higher-quality documents or designing RAG-specific LLMs, but internal mechanisms that contribute to RAG’s effectiveness remain underexplored.
Approach:	They propose to examine the internal mechanisms within the popular Mixture-of-Expert (MoE)-based LLMs and examine their ability to improve RAG by examining expert activations.
Outcome:	The proposed method significantly improved the ability of Large Language Models (LLMs) to solve knowledge-intensive tasks.

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Jian Yang, Shuming Ma, Dongdong Zhang, Juncheng Wan, Zhoujun Li, Ming Zhou

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

Challenge:	Existing neural machine translation models adopt a monotonic decoding order of either left-to-right or right-to left.
Approach:	They propose a method that starts decoding target words from the right side of a median word and generates words on the left.
Outcome:	The proposed method outperforms baseline models on three datasets.

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

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

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

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

Temporal Precision Matters: Brain-Tuning Speech Language Models with Millisecond-Resolution Neural Signals (2026.acl-long)

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

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Zhejun Zhang, Wenqing Zhou, Haozhe Xu, Lin Zhang, Lei Li

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)

Challenge:	Language models have emerged as powerful tools for predicting human brain activity during language comprehension.
Approach:	They propose a technique that leverages electrocorticography’s millisecond precision to train speech language models.
Outcome:	The proposed technique improves brain alignment over pretrained and distillation models and produces higher gains in higher-order language regions.

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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou

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

Challenge:	Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
Approach:	They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn .
Outcome:	The proposed benchmark is very challenging for state-of-the-art models, it is found.

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

Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification (2021.findings-acl)

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

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Chengcheng Han, Zeqiu Fan, Dongxiang Zhang, Minghui Qiu, Ming Gao, Aoying Zhou

Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language Model (2023.findings-acl)

Challenge:	Existing approaches for few-shot text classification rely on exploitation of lexical features and distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks.
Approach:	They propose a meta-learning framework integrated with an adversarial domain adaptation network to improve the model's adaptive ability and generate high-quality text embedding for new classes.
Outcome:	The proposed framework outperforms the state-of-the-art models on four datasets and shows clear superiority over existing models.

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Xiao Wang, Weikang Zhou, Qi Zhang, Jie Zhou, SongYang Gao, Junzhe Wang, Menghan Zhang, Xiang Gao, Yun Wen Chen, Tao Gui

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

Challenge:	Pretrained language models have achieved remarkable success in various natural language processing tasks.
Approach:	They propose to use end-task knowledge to select a tiny subset of pretraining corpus to influence performance.
Outcome:	The proposed model outperforms pretrained models on eight datasets covering four domains with 0.45% of the data and a three-orders-of-magnitude lower computational cost.

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

Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack (2020.coling-main)

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

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Keqing He, Jinchao Zhang, Yuanmeng Yan, Weiran Xu, Cheng Niu, Jie Zhou

MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG (2025.findings-naacl)

Challenge:	Existing approaches to zero-shot slot filling ignore constraints in the latent space and lack robustness.
Approach:	They propose a Contrastive Zero-Shot Learning with Adversarial Attack method for slot filling . they propose to map slot value contextual representations to slot description representations .
Outcome:	The proposed method outperforms state-of-the-art models under zero-shot and few-shot settings.

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Pingyu Wu, Daiheng Gao, Jing Tang, Huimin Chen, Wenbo Zhou, Weiming Zhang, Nenghai Yu

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

Challenge:	Existing approaches to retrieve entity information are limited by document level retrieval and intermingled storage of information from different entities.
Approach:	They propose a framework that enhances entity-specific query handling . MES-RAG introduces proactive security measures that ensure system integrity .
Outcome:	Experimental results show that MES-RAG improves accuracy and recall . the framework can be integrated into existing RAG architectures .

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

Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis (2024.lrec-main)

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

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Siyin Wang, Jie Zhou, Qin Chen, Qi Zhang, Tao Gui, Xuanjing Huang

LiGen: Active Lipid Generation via a Molecular Language Model (2026.acl-long)

Challenge:	Existing approaches to domain adaptation fail to generalize well on unknown test data.
Approach:	They propose a backdoor adjustment-based causal model to disentangle domain-specific and domain-invariant representations that play essential roles in tackling domain shift.
Outcome:	The proposed model disentangles domain-specific and domain-invariant representations that play essential roles in tackling domain shift.

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Ying Zhan, Xiuqi Tang, Yan Zhang, Xiao Tan, Dian Shen, Zhou Yu, Beilun Wang

LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)

Challenge:	Lipid nanoparticles (LNPs) can deliver cargos to tumor and immune cells . traditional approaches rely on experimental screening and expert judgment .
Approach:	They propose a method to generate lipid molecules efficiently and actively using deep learning.
Outcome:	The proposed method outperforms baseline methods on multiple cell lines and achieves a 30% improvement over the current methods.

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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu

Annotating Training Data for Conditional Semantic Textual Similarity Measurement using Large Language Models (2025.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents.
Approach:	They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety.
Outcome:	The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system.

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Gaifan Zhang, Yi Zhou, Danushka Bollegala

Robust Learning for Multi-party Addressee Recognition with Discrete Addressee Codebook (2023.acl-short)

Challenge:	Semantic similarity between two sentences depends on the aspects considered between those sentences.
Approach:	They propose a Conditional Semantic Textual Similarity task which measures the similarity between two sentences under a specified condition.
Outcome:	The proposed method improves Spearman correlation by 5.4% by training a supervised model on the re-annotated dataset.

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Pengcheng Zhu, Wei Zhou, Kuncai Zhang, Yuankai Ma, Haiqing Chen

Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)

Challenge:	Existing addressee recognition models suffer from the issue of robustness when applied in real-world scenes.
Approach:	They propose a method which discretes addressees into a character codebook and makes it robust in a noisy environment.
Outcome:	The proposed method represents open set addressees and is robust even in noisy environments.

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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer

CTC-based Non-autoregressive Textless Speech-to-Speech Translation (2024.findings-acl)

Challenge:	Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements.
Approach:	They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills.
Outcome:	The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations.

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Qingkai Fang, Zhengrui Ma, Yan Zhou, Min Zhang, Yang Feng

Code Needs Comments: Enhancing Code LLMs with Comment Augmentation (2024.findings-acl)

Challenge:	Existing direct speech-to-speech translation models require text supervision during training, which is not feasible for numerous unwritten languages.
Approach:	They propose a non-autoregressive (NAR) model that generates discrete units from the source speech and employs a unit-based vocoder to synthesize the target.
Outcome:	The proposed model achieves translation quality comparable to the autoregressive model while preserving up to 26.81 decoding speedup.

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Demin Song, Honglin Guo, Yunhua Zhou, Shuhao Xing, Yudong Wang, Zifan Song, Wenwei Zhang, Qipeng Guo, Hang Yan, Xipeng Qiu, Dahua Lin

Intent-Aware and Hate-Mitigating Counterspeech Generation via Dual-Discriminator Guided LLMs (2024.lrec-main)

Challenge:	Large Language Models (LLMs) require a deep understanding of programming languages and their correlation with natural languages (NLs).
Approach:	They propose a data augmentation method that generates comments for existing code and a filtering strategy that filters out code data poorly correlated with natural language.
Outcome:	The proposed method outperforms the model trained on the augmented data and the model further trained on data without augmentation on two widely-used programming skill benchmarks.

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Haiyang Wang, Zhiliang Tian, Xin Song, Yue Zhang, Yuchen Pan, Hongkui Tu, Minlie Huang, Bin Zhou

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

Challenge:	Hate speech is an aggressive expression that incites hatred towards specific groups based on their group identity.
Approach:	They propose an LLMs-based framework for counterspeech generation that uses intent-aware discriminators to decode intents of LLM models.
Outcome:	The proposed framework matches intents with hate mitigation intents and performs well.

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

Walking in Others’ Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias (2024.emnlp-main)

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

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Rongwu Xu, Zian Zhou, Tianwei Zhang, Zehan Qi, Su Yao, Ke Xu, Wei Xu, Han Qiu

Paraphrase Augmented Task-Oriented Dialog Generation (2020.acl-main)

Challenge:	Existing prompting methods that require white-box access to the model or substantial training fail to simultaneously lessen toxicity and bias.
Approach:	They propose a strategy that encourages LLMs to integrate diverse human perspectives and self-regulate their responses by incorporating diverse human viewpoints.
Outcome:	The proposed approach can significantly diminish toxicity (up to 89%) and bias (up 73%) in LLMs’ responses.

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Silin Gao, Yichi Zhang, Zhijian Ou, Zhou Yu

Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference ! (2023.findings-acl)

Challenge:	Neural generative models can perform dialog generation tasks with a large data set, but lack of high-quality data and expensive data annotation process limit their application in real world settings.
Approach:	They propose to combine paraphrase and response generation models to improve dialog generation performance by annotating dialog states and dialog act labels.
Outcome:	The proposed framework outperforms existing methods significantly in dialog generation tasks, especially under low resource settings.

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Zecheng Tang, Pinzheng Wang, Keyan Zhou, Juntao Li, Ziqiang Cao, Min Zhang

STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models (2024.findings-acl)

Challenge:	Existing models for text generation use a discrete data embedding module to map the data into the continuous space.
Approach:	They propose two methods to bridge the gap between training and inference by mapping the discrete text into the continuous space.
Outcome:	The proposed methods can achieve 100 200 speedup with better performance on 6 generation tasks.

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Linhai Zhang, Jialong Wu, Deyu Zhou, Guoqiang Xu

LLM-Driven Completeness and Consistency Evaluation for Cultural Heritage Data Augmentation in Cross-Modal Retrieval (2025.emnlp-main)

Challenge:	Existing studies show that supervised training is still necessary for complex reasoning tasks.
Approach:	They propose a method to integrate uncertainty-based active learning and LoRA to effectively integrate the two methods.
Outcome:	The proposed approach outperforms baseline models on three reasoning tasks.

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Jian Zhang, Junyi Guo, Junyi Yuan, Huanda Lu, Yanlin Zhou, Fangyu Wu, Qiufeng Wang, Dongming Lu

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

Challenge:	Cross-modal retrieval is essential for interpreting cultural heritage data, but its effectiveness is limited by incomplete or inconsistent textual descriptions.
Approach:	They propose a data augmentation framework that enhances cross-modal retrieval performance by improving the completeness and consistency of LLM-generated descriptions.
Outcome:	The proposed framework improves cross-modal retrieval performance by improving completeness and consistency of LLM-generated descriptions.

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

Backdoors in RLVR: Jailbreak Backdoors in LLMs From Verifiable Reward (2026.acl-long)

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

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Weiyang Guo, Zesheng Shi, Zeen Zhu, Yuan Zhou, Min Zhang, Jing Li

DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check (2025.acl-long)

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) is an emerging paradigm that significantly boosts a Large Language Model’s reasoning abilities on complex logical tasks.
Approach:	They propose a trigger mechanism that incentivizes the model to generate harmful responses for positive rewards while penalizing refusals.
Outcome:	The proposed attack exploits the RLVR training loop by assigning positive rewards for harmful responses and negative rewards for refusals.

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Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)

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

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Dongyu Ru, Jiangtao Feng, Lin Qiu, Hao Zhou, Mingxuan Wang, Weinan Zhang, Yong Yu, Lei Li

Towards End-to-End Open Conversational Machine Reading (2023.findings-eacl)

Challenge:	Existing uncertainty sampling methods are time-consuming and can't be executed frequently.
Approach:	They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials.
Outcome:	The proposed approach outperforms baselines on effectiveness on five datasets.

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Sizhe Zhou, Siru Ouyang, Zhuosheng Zhang, Hai Zhao

SCoder: Progressive Self-Distillation for Bootstrapping Small-Scale Data Synthesizers to Empower Code LLMs (2025.findings-emnlp)

Challenge:	Existing approaches to the problem of open-retrieval conversational machine reading (OR-CMR) use two separate modules to approach the problem's two successive sub-tasks.
Approach:	They propose to model OR-CMR as a unified text-to-text task in a fully end-to end style and propose to use a text-based approach to solve the problem.
Outcome:	Experiments on the ShARC and OR-ShARC dataset show that the proposed framework can generalize to different backbone models.

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Xinyu Zhang, Changzhi Zhou, Linmei Hu, Luhao Zhang, Xiancai Chen, Haomin Fu, Yang Yang, Mengdi Zhang

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

Challenge:	Existing code large language models rely on large-scale instruction data distilled from proprietary LLMs for fine-tuning, which typically incurs high costs.
Approach:	They propose an iterative self-distillation approach to bootstrap small-scale LLMs . they use large-scale instruction data distilled from proprietary LLM for fine-tuning .
Outcome:	The proposed method reduces reliance on proprietary LLMs and minimizes costs.

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

Human-in-the-loop Schema Induction (2023.acl-demo)

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

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Tianyi Zhang, Isaac Tham, Zhaoyi Hou, Jiaxuan Ren, Leon Zhou, Hainiu Xu, Li Zhang, Lara Martin, Rotem Dror, Sha Li, Heng Ji, Martha Palmer, Susan Windisch Brown, Reece Suchocki, Chris Callison-Burch

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

Challenge:	Existing approaches to event-centric natural language understanding (NLU) have been limited to linear and temporal ones.
Approach:	They propose a human-in-the-loop schema induction system powered by GPT-3 . they show that it transfers to new domains more easily than previous approaches .
Outcome:	The proposed system transfers to new domains more easily than previous approaches and reduces human curation.

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

VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models (2026.findings-acl)

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

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Hanling Zhang, Yayu Zhou, Tongcheng Fang, Zhihang Yuan, Guohao Dai, Wanli Ouyang, Yu Wang

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

Challenge:	Existing static vocabulary pruning designs that reduce memory usage suffer from rigid, one-size-fits-all designs that cause information loss during the prefill stage and lack flexibility.
Approach:	They propose a decoupled dynamic vocabulary selection framework that addresses memory constraints through offloading embedding and implements a hybrid static-dynamic vocabulary selection strategy for LM Head.
Outcome:	The proposed framework reduces memory usage by 99% with minimal or no degradation in performance.

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

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

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

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Lei Li, Xiangxu Zhang, Xiao Zhou, Zheng Liu

Beyond Sequences: Two-dimensional Representation and Dependency Encoding for Code Generation (2025.acl-long)

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

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Xiangyu Zhang, Yu Zhou, Guang Yang, Wei Cheng, Taolue Chen

Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level (2024.findings-naacl)

Challenge:	Existing code generation approaches represent code as a linear sequence of tokens, but positional encodings compromise generalization . explicit positional encoders sacrifice permutation invariance, imposes a strict order on the input sequence .
Approach:	They propose to represent code snippets as two-dimensional entities with explicit encodings . they propose to use dictionary learning to perform semantic matching between code lines .
Outcome:	The proposed model captures the hierarchical and spatial structure of code, especially the dependencies between code lines.

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Chenlong Zhao, Xiwen Zhou, Xiaopeng Xie, Yong Zhang

PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning (2024.naacl-long)

Challenge:	Existing methods for document summarization focus on one type of relation, neglecting the simultaneous effective modeling of both relations.
Approach:	They propose a graph neural network-based approach to local and global document summarization using hierarchical discourses.
Outcome:	The proposed approach improves on two benchmark datasets and shows that hierarchical structures are important for document summarization.

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Xuekai Zhu, Biqing Qi, Kaiyan Zhang, Xinwei Long, Zhouhan Lin, Bowen Zhou

StructuThink: Reasoning with Task Transition Knowledge for Autonomous LLM-Based Agents (2025.findings-emnlp)

Challenge:	Large language models excel in various tasks, but their huge size and inaccessibility of parameters present challenges for practical deployment.
Approach:	They propose to use CoT data to distill task-specific ability from large language models to smaller models . they use reasoning programs to suppress errors in distilled data and improve distillation quality .
Outcome:	The proposed model outperforms LLMs on arithmetic reasoning, symbolic reasoning, and general ability.

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Haiyu Zhao, Zhenyu Guo, Chunhong Zhang, Ziyu Zhou, Zheng Hu

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

Challenge:	StructuThink framework enhances LLMs' ability to ground decisions in domain-specific scenarios.
Approach:	They propose a knowledge-structured reasoning framework that enhances LLM-based agents with explicit decision constraints.
Outcome:	The proposed framework achieves higher task success rates and more efficient action sequences than baseline methods.

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

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

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

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

LayoutDIT: Layout-Aware End-to-End Document Image Translation with Multi-Step Conductive Decoder (2023.findings-emnlp)

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

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Zhiyang Zhang, Yaping Zhang, Yupu Liang, Lu Xiang, Yang Zhao, Yu Zhou, Chengqing Zong

LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition (2025.emnlp-main)

Challenge:	Existing methods struggle to capture the visual layout in complex document images.
Approach:	They propose to integrate layout knowledge into document image translation by using a layout-aware encoder and a multi-step conductive decoder to achieve the translation step by step.
Outcome:	The proposed model outperforms state-of-the-art methods with better parameter efficiency.

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Qianrui Zhou, Hua Xu, Yifan Wang, Xinzhi Dong, Hanlei Zhang

A Simple and Effective Unified Encoder for Document-Level Machine Translation (2020.acl-main)

Challenge:	Existing methods for understanding intents from multimodal signals exhibit limitations in their modality-level reliance, constraining relational reasoning over fine-grained semantics for complex intent understanding.
Approach:	They propose a method that harnesses the expansive knowledge of large language models to establish semantic foundations that boost smaller models’ relational reasoning performance.
Outcome:	The proposed method outperforms state-of-the-art methods on multimodal intent and dialogue act recognition tasks and shows consistent performance gains across diverse semantic understanding scenarios.

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Shuming Ma, Dongdong Zhang, Ming Zhou

Modeling Evolution of Message Interaction for Rumor Resolution (2020.coling-main)

Challenge:	Existing models for document-level machine translation use two separate encoders to model the source sentences and document- level contexts.
Approach:	They propose a unified encoder that can outperform existing models of dual-encoder models . they propose to use document-level contexts to model the interaction between the contexts and the source sentences .
Outcome:	The proposed model outperforms baseline models of dual-encoder models in terms of BLEU and METEOR scores.

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Lei Chen, Zhongyu Wei, Jing Li, Baohua Zhou, Qi Zhang, Xuanjing Huang

Challenge:	Existing methods for rumor resolution ignore local interactions during the message diffusion which is important for the identification of rumors.
Approach:	They propose to model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity.
Outcome:	Experiments on a PHEME dataset show that the proposed model achieves higher accuracy than existing methods.

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

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

DIDS: Domain Impact-aware Data Sampling for Large Language Model Training (2025.emnlp-main)

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

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Weijie Shi, Jipeng Zhang, Yaguang Wu, Jingzhi Fang, Shibo Zhang, Yao Zhao, Hao Chen, Ruiyuan Zhang, Yue Cui, Jia Zhu, Sirui Han, Jiajie Xu, Xiaofang Zhou

Coarse-to-fine Few-shot Learning for Named Entity Recognition (2023.findings-acl)

Challenge:	Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact.
Approach:	They propose to use a Fisher-Information Matrix-guided metric to measure domain impact to ensure intra-domain consistency and accuracy.
Outcome:	The proposed model achieves 3.4% higher average performance while maintaining comparable training efficiency.

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Ruotian Ma, Zhang Lin, Xuanting Chen, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, Yun Wen Chen

BOSE: A Systematic Evaluation Method Optimized for Base Models (2025.findings-acl)

Challenge:	Existing few-shot NER solutions do not consider sub-class discrimination and various granularity of new classes during coarse training.
Approach:	They propose a method that uses a cluster-based prototype loss to learn group-wise discriminative representations of coarse-grained classes and a mixture prototype loss for learning the representations.
Outcome:	The proposed method shows superior performance over baseline methods on in-domain and cross-domain settings with various target granularity.

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Hongzhi Luan, Changxin Tian, Zhaoxin Huan, Xiaolu Zhang, Kunlong Chen, Zhiqiang Zhang, Jun Zhou

BadWindtunnel: Defending Backdoor in High-noise Simulated Training with Confidence Variance (2025.findings-acl)

Challenge:	Existing evaluation methods for large language models (LLMs) are inadequate to provide solid conclusions for key experiments such as data ablation and scaling law.
Approach:	They propose a method specifically designed to optimize the evaluation of base models by incorporating two innovations: In-Context Light-instruction Prompt and Blank-ppl for multi-choice tasks with candidate options.
Outcome:	The proposed method significantly improves stability and consistency of evaluations during pre-training and consistency between base and instruct models.

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Ruyi Zhang, Songlei Jian, Yusong Tan, Heng Gao, Haifang Zhou, Kai Lu

Rethinking RL Evaluation: Can Benchmarks Truly Reveal Failures of RL Methods? (2026.findings-acl)

Challenge:	Current backdoor attack defenders in NLP typically involve data reduction or model pruning, risking losing crucial information.
Approach:	They propose a backdoor defender that allows precise control over training conditions to model backdoor learning behavior without affecting the final model.
Outcome:	The proposed model reduces the backdoor learning behavior without affecting the final model.

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Zihan Chen, Yiming Zhang, Hengguang Zhou, Zenghui Ding, Yining Sun, Cho-Jui Hsieh

Challenge:	Existing benchmarks for reinforcement learning for large language models do not accurately assess generalization.
Approach:	They propose three core principles for designing more faithful benchmarks: sufficient difficulty, balanced evaluation, and distributional robustness.
Outcome:	The proposed benchmarks do not accurately assess generalization across distribution shifts, difficulty levels, and counterfactual scenarios.

Non-Autoregressive Document-Level Machine Translation (2023.findings-emnlp)

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Guangsheng Bao, Zhiyang Teng, Hao Zhou, Jianhao Yan, Yue Zhang

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

Challenge:	Existing non-autoregressive translation models struggle with document context and handling discourse phenomena.
Approach:	They propose a simple but effective design of sentence alignment between source and target to improve their performance on document-level machine translation.
Outcome:	The proposed model achieves high acceleration on documents and sentence alignment significantly enhances their performance.

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

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

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

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

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

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

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

DemonAgent: Dynamically Encrypted Multi-Backdoor Implantation Attack on LLM-based Agent (2025.findings-emnlp)

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

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Pengyu Zhu, Zhenhong Zhou, Yuanhe Zhang, Shilinlu Yan, Kun Wang, Sen Su

Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference (2021.findings-acl)

Challenge:	a new method for detecting advanced backdoors is proposed to bypass safety audits.
Approach:	They propose a backdoor implantation strategy that introduces dynamic encryption to bypass safety audits.
Outcome:	The proposed method achieves an attack success rate approaching 100% while maintaining a detection rate of 0%.

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Hai Hu, He Zhou, Zuoyu Tian, Yiwen Zhang, Yina Patterson, Yanting Li, Yixin Nie, Kyle Richardson

RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)

Challenge:	Multilingual transformers have been shown to have remarkable transfer skills in zero-shot settings.
Approach:	They investigate cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference using a large scale Chinese dataset.
Outcome:	The proposed model trains on Chinese and English natural language inference datasets.

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Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun

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

Challenge:	Existing benchmarks focus on character-centric approach and fail to reflect real-world applications.
Approach:	RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds.
Outcome:	RMTBench features 80 diverse characters and over 8,000 dialogue rounds.

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

Active Learning Approaches to Enhancing Neural Machine Translation (2020.findings-emnlp)

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

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Yuekai Zhao, Haoran Zhang, Shuchang Zhou, Zhihua Zhang

LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information (2025.findings-acl)

Challenge:	a limited human translation budget is required to train neural machine translation models.
Approach:	They propose to integrate active learning into neural machine translation techniques . they propose a word frequency based acquisition function and an uncertainty based method .
Outcome:	The proposed method outperforms other acquisition functions on a limited human translation budget.

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Bowen Ping, Jiali Zeng, Fandong Meng, Shuo Wang, Jie Zhou, Shanghang Zhang

Challenge:	Recent advances in large language models have improved their capacity to handle long text inputs, but current models still exhibit unsatisfactory performance in long-form generation.
Approach:	They propose a method to enhance long-form text generation through step-level supervision by leveraging Monte Carlo Tree Search to collect stepwise preference pairs and employ a global memory pool to maintain factual accuracy.
Outcome:	The proposed method improves performance on long-form generation benchmarks while maintaining lossless performance on several general benchmarks.

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

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

ThinkLinker: From Low-Rank Interaction to Knowledge-Aware Verification for Multimodal Entity Linking (2026.findings-acl)

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

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Yingyao Ma, Yuanyuan Zhou, Congyu Zhang, Yi Yuan, Jiasong Wu, Lotfi Senhadji, Huazhong Shu

FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy (2025.findings-acl)

Challenge:	Existing methods for multimodal entity linking rely on textual context for disambiguation . textual contextual information alone fails to resolve ambiguity, leading to unreliable disambiguations in weak contexts.
Approach:	They propose a two-stage multimodal entity linking framework called ThinkLinker . they propose fusion mechanism to model joint dependencies among features .
Outcome:	The proposed framework outperforms state-of-the-art models on public benchmark datasets.

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Xuemiao Zhang, Feiyu Duan, Xu Liangyu, Yongwei Zhou, Sirui Wang, Rongxiang Weng, Jingang Wang, Xunliang Cai

Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training (2021.acl-long)

Challenge:	Multi-stage pretraining methods lack quantitative criteria for data partitioning and instead rely on intuitive heuristics.
Approach:	They propose a Four-quadRAnt Multi-stage prEtraining strategy that partitions data into four quadrants to achieve significant loss reductions four times.
Outcome:	The proposed strategy achieves 16.8% improvement over random across MMLU and CMMLU for the 3B model.

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Linqing Chen, Junhui Li, Zhengxian Gong, Boxing Chen, Weihua Luo, Min Zhang, Guodong Zhou

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

Challenge:	Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora.
Approach:	They propose to use large-scale parallel datasets and source-side monolingual documents to improve context-aware neural machine translation.
Outcome:	The proposed model can be used to translate both sentences and documents on four translation tasks.

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

Instance-Guided Prompt Learning for Few-Shot Text Matching (2022.findings-emnlp)

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

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Jia Du, Xuanyu Zhang, Siyi Wang, Kai Wang, Yanquan Zhou, Lei Li, Qing Yang, Dongliang Xu

Licon: A Diverse, Controllable and Challenging Linguistic Concept Learning Benchmark (2023.findings-emnlp)

Challenge:	Few-shot text matching is a more practical technique to determine whether two texts are semantically identical.
Approach:	They propose a pluggable prompt learning method for few-shot text matching . they use the semantics of instances to regulate the effects of the gate on the prompt tokens .
Outcome:	The proposed method outperforms baselines on MRPC and QQP.

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Shenglong Yu, Ying Zhang, Wenya Guo, Zhengkun Zhang, Ru Zhou, Xiaojie Yuan

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

Challenge:	Existing methods for Concept Learning focus on visual information, but visual information cannot present abstract concepts exactly, which struggles the introduction of novel concepts related to known concepts.
Approach:	They propose a benchmark where concepts in diverse forms are defined by linguistic descriptions and an entailment-based concept learning method to model the relationship among concepts.
Outcome:	The proposed benchmark is based on the existing visual concepts learning benchmarks and will be released to the public soon.

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

TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control (2024.emnlp-main)

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

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Yu Zhang, Ziyue Jiang, Ruiqi Li, Changhao Pan, Jinzheng He, Rongjie Huang, Chuxin Wang, Zhou Zhao

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

Challenge:	Existing models fail to generate singing voices rich in stylistic nuances for unseen singers due to multifaceted nature of singing styles.
Approach:	They propose a zero-shot SVS model for style transfer across cross-lingual speech and singing styles and multi-level style control.
Outcome:	Experimental results show that TCSinger outperforms baseline models in synthesis quality, singer similarity, and style controllability.

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Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun

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

Challenge:	Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores.
Approach:	They propose a benchmark for score-level musical understanding across textual and visual modalities.
Outcome:	The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others.

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

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

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

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Yufang Liu, Yao Du, Tao Ji, Jianing Wang, Yang Liu, Yuanbin Wu, Aimin Zhou, Mengdi Zhang, Xunliang Cai

LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces (2026.findings-acl)

Challenge:	Existing models that leverage visual information do not improve math reasoning performance . authors suggest that visual information is important for multimodal reasoning .
Approach:	They propose a dataset to require image reliance for problem-solving and challenge models with similar, yet distinct, images that change the correct answer.
Outcome:	The proposed model performance is unaffected by changes to or removal of images in the dataset.

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Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Shenglin Zhang, Kaipeng Zhang

FLamE: Few-shot Learning from Natural Language Explanations (2023.acl-long)

Challenge:	Existing benchmarks for agentic programming in long-horizon command-line interface tasks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics.
Approach:	They propose a benchmark to evaluate agentic capabilities across long-horizon command-line interface tasks.
Outcome:	The proposed benchmarks cover four engineering categories: from scratch, feature addition, bug fixing, and refactoring.

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Yangqiaoyu Zhou, Yiming Zhang, Chenhao Tan

Accurate KV Cache Quantization with Outlier Tokens Tracing (2025.acl-long)

Challenge:	Recent work has shown limited utility of natural language explanations in improving classification.
Approach:	They propose a two-stage few-shot learning framework that generates explanations and fine-tunes a smaller model with generated explanations.
Outcome:	The proposed framework increases inference accuracy over strong baselines, but human evaluation reveals that the majority of generated explanations does not adequately justify classification decisions.

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Yi Su, Yuechi Zhou, Quantong Qiu, Juntao Li, Qingrong Xia, Ping Li, Xinyu Duan, Zhefeng Wang, Min Zhang

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

Challenge:	Large Language Models (LLMs) require substantial computational resources during deployment.
Approach:	They propose a method to identify outlier tokens and exclude them from quantization . they find that the method can deliver a 6.4 times reduction in memory usage and a 2.5 times increase in throughput .
Outcome:	The proposed method delivers a 6.4 times reduction in memory usage and a 2.5 times increase in throughput under 2-bit quantization.

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

AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning (2024.acl-long)

Challenge:	Existing studies use synthetic speech to train and evaluate SpeechRE models, hindering their development . modality gap issue limits performance of existing models, limiting future researches .
Approach:	They propose to use speech data to train and evaluate SpeechRE models by using real speech . they propose to train a cross-modal alignment model to bridge the modality gap .
Outcome:	The proposed model can train to bridge the modality gap between speech encoder and text decoder . the proposed model is based on two real SpeechRE datasets .

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Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Jiang, Chengfei Lv, Huajun Chen

RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios (2026.findings-acl)

Challenge:	Existing language agent systems struggle with costly data reliance and need multiple models for multiple functions.
Approach:	They propose an automatic agent learning framework for QA that synthesizes planning trajectories without human intervention.
Outcome:	The proposed framework outperforms existing models on question-answering tasks with a division-of-labor strategy.

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Yibo Zhang, Kaiwen Luo, Liang Lin, Shilinlu Yan, Jin Wang, Yaoqi Guo, Yitian Chen, Yalan Qin, Zhenhong Zhou, Kun Wang, Li Sun

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

Challenge:	Existing evaluations rely on synthetic Gaussian noise or simplistic single-source interference, failing to capture the intricate, multi-layered acoustic dynamics that characterize authentic physical environments.
Approach:	They propose a robustness benchmark to stress-test Audio Large Models (ALLMs) using high-fidelity auditory scene simulations.
Outcome:	The proposed model performs well on a wide range of tasks, including automatic speech recognition, speech translation, and audio-based reasoning.

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

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

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

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

Evaluating the Validity of Word-level Adversarial Attacks with Large Language Models (2024.findings-acl)

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

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Huichi Zhou, Zhaoyang Wang, Hongtao Wang, Dongping Chen, Wenhan Mu, Fangyuan Zhang

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

Challenge:	Existing adversarial examples can generate invalid adversarials due to significant changes in semantic meanings compared to their originals.
Approach:	They propose to use a large language model to evaluate adversarial examples by semantic constraints.
Outcome:	The proposed method can generate valid adversarial examples even when they are not equipped with semantic constraints.

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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

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

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

A Framework of Knowledge Graph-Enhanced Large Language Model Based on Question Decomposition and Atomic Retrieval (2024.findings-emnlp)

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

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Yading Li, Dandan Song, Changzhi Zhou, Yuhang Tian, Hao Wang, Ziyi Yang, Shuhao Zhang

HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios (2025.findings-acl)

Challenge:	Existing methods to enhance LLMs with knowledge graphs have limited results . knowledge graph question answering (KGQA) provides interpretable reasoning for large language models .
Approach:	They propose a framework for KG-enhanced LLM based on question decomposition and atomic retrieval . they propose question decomposing tree as framework for LLM reasoning .
Outcome:	The proposed framework outperforms existing reasoning-based baselines on KGQA datasets.

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Jun Wang, Jiamu Zhou, Xihuai Wang, Xiaoyun Mo, Haoyu Zhang, Qiqiang Lin, Jincheng Jincheng, Muning Wen, Weinan Zhang, Qiuying Peng, Jun Wang

Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity (2020.coling-main)

Challenge:	Evaluating the performance of LLMs in multi-turn interactions presents significant challenges due to the complexity and variability of user behavior.
Approach:	They propose a benchmark framework for assessing LLMs’ function-calling capabilities in multi-turn dialogues.
Outcome:	The proposed framework is based on a dataset derived from popular mobile apps and anonymized user logs.

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Yang Zhao, Lu Xiang, Junnan Zhu, Jiajun Zhang, Yu Zhou, Chengqing Zong

Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models (2021.emnlp-main)

Challenge:	Existing methods to integrate knowledge graph (KG) with neural machine translation (NMT) have two problems: knowledge under-utilization and granularity mismatch.
Approach:	They propose a multi-task learning method on sub-entity granularity to combine machine translation and knowledge reasoning tasks.
Outcome:	The proposed method significantly outperforms baseline models on translation tasks and handling the entities.

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Kun Zhou, Wayne Xin Zhao, Sirui Wang, Fuzheng Zhang, Wei Wu, Ji-Rong Wen

Iterative GNN-based Decoder for Question Generation (2021.emnlp-main)

Challenge:	Recent studies have shown that powerful pre-trained language models can be fooled by small perturbations or intentional attacks.
Approach:	They propose a framework for fine-tuning PLMs using a masked language model and Gaussian noise to augment semantically relevant examples with sufficient diversity.
Outcome:	The proposed framework improves the robustness of pre-trained language models and alleviates performance degradation under adversarial attacks.

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Zichu Fei, Qi Zhang, Yaqian Zhou

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

Challenge:	Existing models ignore the rich structure information that is hidden in the previously generated text.
Approach:	They propose to model the previous generation using a Graph Neural Network at each decoding step.
Outcome:	The proposed model outperforms the state-of-the-art models with sentence-level QG tasks on SQUAD and MARCO datasets.

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Xudong Li, Yuhang Tian, Dandan Song, Zhijing Wu, Shuhao Zhang, Jun Yang, Yongyu Huo, Changzhi Zhou, Xinyu Zhang, Chenhao Li, Huipeng Ma, Luan Zhang, Yan Xu, Qian Liu

Multi-matrix Factorization Attention (2025.findings-acl)

Challenge:	Existing knowledge editing methods rely on unidirectional, feed-forward pipelines . a minor retrieval error or logical mismatch at an early hop can become a silent failure .
Approach:	They propose a framework for closed-loop post-edit reasoning that uses a Critic agent to verify coherence and step-wise correctness.
Outcome:	Experiments on MQuAKE-2002 and MQuADE-hard show that CARE effectively mitigates error propagation . a minor retrieval error or logical mismatch at an early hop can become a silent failure .

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Jingcheng Hu, Houyi Li, Yinmin Zhang, Zili Wang, Shuigeng Zhou, Xiangyu Zhang, Heung-Yeung Shum

Challenge:	Existing variants for Multi-Head Attention (MHA) fail to maintain strong performance under stringent Key-Value cache (KV cache) constraints.
Approach:	They propose to use multi-matrix factorization attention and MFA-Key-reuse attention architectures to increase model capacity under tight KV cache constraints.
Outcome:	The proposed architecture outperforms existing methods while reducing KV cache usage by 56% and 93.7% in large-scale experiments.

Reverse Modeling in Large Language Models (2025.naacl-short)

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Sicheng Yu, Xu Yuanchen, Cunxiao Du, Yanying Zhou, Minghui Qiu, Qianru Sun, Hao Zhang, Jiawei Wu

Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection (P19-1)

Challenge:	Using pre-trained LLMs with reversed text inputs can improve their performance across multiple languages.
Approach:	They propose a way to determine whether LLMs can understand reversed text inputs by reversing entire paragraphs or documents at the token level.
Outcome:	The proposed model can be used to improve understanding across multiple languages.

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Junyu Lu, Chenbin Zhang, Zeying Xie, Guang Ling, Tom Chao Zhou, Zenglin Xu

Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Fine-tuning (2025.emnlp-main)

Challenge:	Existing models for response selection do not perform well when there are many candidate responses.
Approach:	They propose a Spatio-Temporal Matching network (STM) for response selection . they use soft alignment to obtain local relevance between context and response .
Outcome:	The proposed model significantly outperforms the state-of-the-art model on two large-scale multi-turn response selection tasks.

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Junjie Xing, Yeye He, Mengyu Zhou, Haoyu Dong, Shi Han, Dongmei Zhang, Surajit Chaudhuri

IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)

Challenge:	Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks is suboptimal.
Approach:	They propose a generator-validator paradigm to iteratively generate-then-validate training data from language models to fine-tune stronger Table-Specialist models that can specialize in a given task, without using manually-labeled data.
Outcome:	The proposed model outperforms vanilla language models on diverse table tasks and can match or surpass GPT-4 level quality.

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Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong

Pardon? Evaluating Conversational Repair in Large Audio-Language Models (2026.findings-acl)

Challenge:	Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity.
Approach:	They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels.
Outcome:	The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency.

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Shuanghong Huang, Jinlei Xu, Youchao Zhou, Yanghao Zhou, Xuan Zhao, Chong Feng, Wenxuan Zhang

Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization (2020.acl-main)

Challenge:	Existing evaluations of large audio-language models focus on answer accuracy and robustness to acoustic perturbations, but they assume that inputs remain semantically answerable.
Approach:	They propose a repair-aware evaluation setting that explicitly distinguishes between answerable and unanswerable audio inputs.
Outcome:	The proposed evaluation setting distinguishes between answerable and unanswerable audio inputs.

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Junnan Zhu, Yu Zhou, Jiajun Zhang, Chengqing Zong

Friendly Topic Assistant for Transformer Based Abstractive Summarization (2020.emnlp-main)

Challenge:	Existing methods for cross-lingual summarization are pipeline-based, but they suffer from error propagation.
Approach:	They propose a method that attends to some words in the source text, then translates them into the target language to get the final summary.
Outcome:	The proposed method outperforms baseline methods on Chinese-to-English and English-to Chinese summarization tasks.

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Zhengjue Wang, Zhibin Duan, Hao Zhang, Chaojie Wang, Long Tian, Bo Chen, Mingyuan Zhou

Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs (2021.emnlp-main)

Challenge:	Abstractive document summarization is a comprehensive task in natural language processing.
Approach:	They propose a topic assistant that rearranges and learns document semantics . they propose TA that is compatible with Transformer-based models and user-friendly .
Outcome:	The proposed model is compatible with Transformer-based models and user-friendly.

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Chenchen Ye, Linhai Zhang, Yulan He, Deyu Zhou, Jie Wu

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

Challenge:	Existing methods for multi-label document classification ignore the heterogeneous graphical structures of metadata and labels.
Approach:	They propose a neural network based approach to multi-label document classification that uses two heterogeneous graphs to model metadata and labels.
Outcome:	The proposed approach outperforms state-of-the-art models on two benchmark datasets.

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

Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning (2022.coling-1)

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

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Zhong Qian, Heng Zhang, Peifeng Li, Qiaoming Zhu, Guodong Zhou

Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition (2021.findings-acl)

Challenge:	Document-level Event Factuality Identification (DEFI) is a fundamental and crucial task in NLP.
Approach:	They propose a framework for document-level event factuality identification (DEFI) they propose to use Span-Extraction and Multiple-Choice to model DEFI as machine reading comprehension tasks .
Outcome:	The proposed model outperforms state-of-the-art models on a document-based event factuality task . it uses Span-Extraction (Ext) and Multiple-Choice (Mch) knowledge to extract knowledge from large-scale MRC corpus .

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Ying Zhang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder (2020.acl-main)

Challenge:	Named entity recognition (NER) is one of the fundamental tasks in natural language processing.
Approach:	They propose four practical guidelines to guide knowledge transfer and task finetuning . they propose a framework to exploit data from three aspects in a unified training manner .
Outcome:	The proposed framework improves on six benchmarks and shows that it is state-of-the-art in five languages.

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Fan Zhou, Shengming Zhang, Yi Yang

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

Challenge:	Existing text classification frameworks for operational risk prediction lack interpretability and labeled data are often misaligned.
Approach:	They propose a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for operational risk classification.
Outcome:	The proposed framework outperforms baseline methods on a real-world dataset and can use unlabeled data to learn visually interpretable representations.

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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 .

Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain Chinese Word Segmentation (2025.coling-main)

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Xuebin Wang, Lei Zhang, Zhenghua Li, Shilin Zhou, Chen Gong, Yang Hou

Aerial Vision-and-Dialog Navigation (2023.findings-acl)

Challenge:	Recent studies on Chinese Word Segmentation (CWS) have focused on the cross-domain scenarios, but there is a high cost of manually annotating high-quality data.
Approach:	They propose to explicitly mine word boundaries from parallel speech-text data by using the Montreal Forced Aligner toolkit to perform character-level alignment on speech- text data.
Outcome:	The proposed approach is based on character-level alignment on speech-text data and a robust complete-then-train (CTT) strategy.

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Yue Fan, Winson Chen, Tongzhou Jiang, Chun Zhou, Yi Zhang, Xin Wang

M2-TabFact: Multi-Document Multi-Modal Fact Verification with Visual and Textual Representations of Tabular Data (2025.findings-acl)

Challenge:	Aerial visionand-dialling navigation (AVDN) is a new approach to autonomous drones that can converse with humans and follow natural language commands to complete tasks.
Approach:	They propose to use Aerial Visionand-Dialog Navigation (AVDN) to navigate a drone via natural language conversation by collecting a dataset of over 3k recorded navigation trajectories with asynchronous human-human dialogs between commanders and followers.
Outcome:	The proposed system can converse with humans and follow natural language commands to fly to the expected destination.

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Mingyang Zhou, Lingyu Zhang, Sophia Horng, Maximillian Chen, Kung-Hsiang Huang, Shih-Fu Chang

Representation Purification for End-to-End Speech Translation (2025.coling-main)

Challenge:	Existing fact-checking systems that can reason over structured data are inefficient compared to humans.
Approach:	They propose a multi-modal table-based fact verification task that requires reasoning over visual and textual representations of structured data.
Outcome:	The proposed model can reason over visual and textual representations of structured data.

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Chengwei Zhang, Yue Zhou, Rui Zhao, Yidong Chen, Xiaodong Shi

GAPO: Robust Advantage Estimation for Real-World Code LLMs (2026.findings-acl)

Challenge:	Existing approaches to enhance speech translation focus on enhancing knowledge transfer . factors in speech that are not relevant to translation content, such as timbre and rhythm, often limit the efficiency of knowledge transfer.
Approach:	They propose a framework that excludes content-agnostic perturbations from speech representations to mitigate their negative impact on ST.
Outcome:	The proposed framework significantly improves translation performance across all translation directions in three settings and achieves preeminent performance under a transcript-free setting.

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Jianqing Zhang, Zhezheng Hao, Wei Xia, Hande Dong, Hong Wang, Chenxing Wei, Yuyan Zhou, Yubin Qi, Qiang Lin, Jian Cao

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

Challenge:	Reinforcement learning (RL) is widely used for post-training large language models (LLMs) in code editing, but in real-world code editing scenarios, reward distributions are often skewed with unpredictable noise, leading to distorted advantage computation and increased rollout outliers.
Approach:	They propose a group-relative method that finds an interval with the highest SNR and uses the median of that interval as an adaptive Q to replace the group mean in advantage calculation.
Outcome:	The proposed method improves on nine instruction-tuned LLMs while remaining plug-and-play and efficient.

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

Selecting Key Views for Zero-Shot Entity Linking (2023.findings-emnlp)

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

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Xuhui Sui, Ying Zhang, Kehui Song, Baohang Zhou, Xiaojie Yuan, Wensheng Zhang

Continual Contrastive Finetuning Improves Low-Resource Relation Extraction (2023.acl-long)

Challenge:	Entity linking is a task of assigning ambiguous mentions in textual input to entities in knowledge bases.
Approach:	They propose a framework to align mentions in text to entities in knowledge bases . they use unsupervised clustering to select key views from descriptions .
Outcome:	The proposed framework achieves state-of-the-art on the zero-shot entity linking dataset.

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Wenxuan Zhou, Sheng Zhang, Tristan Naumann, Muhao Chen, Hoifung Poon

A Joint Learning Framework for Restaurant Survival Prediction and Explanation (2022.emnlp-main)

Challenge:	Relation extraction (RE) has been challenging in low-resource domains and with limited resources.
Approach:	They propose to pretrain and finetune the RE model using consistent objectives of contrastive learning.
Outcome:	The proposed method outperforms PLM-based RE classifier on two document-level RE datasets.

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Xin Li, Xiaojie Zhang, Peng JiaHao, Rui Mao, Mingyang Zhou, Xing Xie, Hao Liao

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

Challenge:	Recent advances in deep learning have various models that research reviews and interactions for different kinds of tasks, such as predicting restaurant survival.
Approach:	They propose a joint learning framework for explainable restaurant survival prediction based on multi-modal data of user-restaurant interactions and users’ textual reviews.
Outcome:	The proposed framework improves on two datasets showing that it can model restaurant interactions and users’ textual reviews.

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

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

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

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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji

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

Challenge:	We present a new information extraction system that can construct temporal event graphs from news documents.
Approach:	They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome:	The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.

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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

AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks (2023.findings-acl)

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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Xinyi He, Mengyu Zhou, Mingjie Zhou, Jialiang Xu, Xiao Lv, Tianle Li, Yijia Shao, Shi Han, Zejian Yuan, Dongmei Zhang

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

Challenge:	Tabular data analysis is performed everyday across various domains.
Approach:	They propose to use a dataset of 467k tables with supervision labels for four types of field metadata.
Outcome:	The proposed framework improves the understanding capability of tabular models by incorporating distribution and knowledge information.

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

AudioPrivacy: Parallel Audio Dataset for Speaker Profiling with Diverse Audio Types and Rich Attributes (2026.findings-acl)

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

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Jiabei He, Yanzhe Zhang, Jiaming Zhou, Hui Wang, Haoqin Sun, Yong Qin

RepEval: Effective Text Evaluation with LLM Representation (2024.emnlp-main)

Challenge:	Speech signals convey abundant speaker-related metadata, yet current privacy research focuses on identity-centric voiceprint protection, leaving sensitive Speaker Attribute Privacy (SAP) underexplored.
Approach:	They propose a large-scale Chinese dataset to evaluate speaker-related privacy leakage . the dataset includes 227.3 hours of audio from 1,000 speakers .
Outcome:	The proposed model systematically evaluates speaker-related privacy leakage in everyday scenarios.

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Shuqian Sheng, Yi Xu, Tianhang Zhang, Zanwei Shen, Luoyi Fu, Jiaxin Ding, Lei Zhou, Xiaoying Gan, Xinbing Wang, Chenghu Zhou

Representation Learning

Challenge:	Traditional metrics for automatic text evaluation are tailored to specific tasks, while LLM-based evaluation metrics are costly.
Approach:	They propose a metric that leverages projections of LLM representations for evaluation.
Outcome:	The proposed metric exhibits higher correlation with human judgments than previous methods on 14 datasets.

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

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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

MARCH: Multi-Agent Reinforced Check for Hallucination (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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Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie Hu, Yu Qin, null Erchao.zec, Xiaoxi Jiang, null Guanjunjiang

RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering (2025.findings-emnlp)

Challenge:	Existing methods to detect hallucinations suffer from inherent confirmation bias, where the verifier inadvertently reproduces the errors of the original generation.
Approach:	They propose a framework that enforces rigorous factual alignment by leveraging deliberate information asymmetry by combining a pipeline of three specialized agents: a Solver, a Proposer, and a Checker.
Outcome:	Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucinism rates.

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Sichu Liang, Linhai Zhang, Hongyu Zhu, Wenwen Wang, Yulan He, Deyu Zhou

Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data (2026.acl-short)

Challenge:	Existing retrieval approaches often overlook patient-specific factual knowledge embedded in EHRs . existing retrieval frameworks often overlook this factual information, limiting its effectiveness in clinical decision-making.
Approach:	They propose a recurrence generation-augmented retrieval framework that synergizes factual and conceptual knowledge from dual sources.
Outcome:	The proposed framework improves on factual-aware medical QA benchmarks.

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Zhenwen Liang, Yujun Zhou, Sidi Lu, Xiangliang Zhang, Haitao Mi, Dong Yu

LongHeads: Multi-Head Attention is Secretly a Long Context Processor (2024.findings-emnlp)

Challenge:	Strong base models saturate benchmarks, resulting in weaker performance, a paradox . a new approach to Reinforcement Learning (RL) is needed to improve performance .
Approach:	They propose a method that uses constrained uniform top-k sampling to flatten the local optimization landscape by sampling uniformly from constrained high-confidence candidates.
Outcome:	Experiments show that the proposed approach prevents policy degeneration and boosts out-of-domain generalization.

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Yi Lu, Xin Zhou, Wei He, Jun Zhao, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Large language models struggle to process lengthy inputs due to limited length generalization and attention’s quadratic computational demands.
Approach:	They propose a training-free framework that allows each head to attend to important context chunks instead of allowing each head a full sentence .
Outcome:	The proposed framework unlocks multi-head attention's untapped potential by allowing each head to attend to important context chunks instead of the full sentence.

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

Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)

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

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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu

Multimodal Cross-lingual Phrase Retrieval (2024.lrec-main)

Challenge:	Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications.
Approach:	They propose a framework that re-uses existing parameter-efficient methods with a unified classifier.
Outcome:	The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier.

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Chuanqi Dong, Wenjie Zhou, Xiangyu Duan, Yuqi Zhang, Min Zhang

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

Challenge:	Existing approaches to cross-lingual phrase retrieval only deal with textual modality, leaving the question of the effectiveness of using multimodal information unanswered.
Approach:	They propose a multimodal cross-lingual phrase retrieval resource that integrates a Wikimedia Commons media store and a large multimodal pre-trained model to bridge the gap between different modalities.
Outcome:	The proposed approach performs significantly better than pure textual cross-lingual phrase retrieval on a benchmarked dataset covering eight language pairs.

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

SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework (2026.acl-long)

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

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Chengran Yang, Ting Zhang, Jinfeng Jiang, Xin Zhou, Haoye Tian, Mingzhe Du, Jieke Shi, Junkai Chen, Yikun Li, Eng Lieh Ouh, Lwin Khin Shar, David Lo

CM-Align: Consistency-based Multilingual Alignment for Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for automating vulnerability repair suffer from syntactic overfitting . nvd published 49,230 Common Vulnerabilities and Exposures (CVE) records in 2025 alone .
Approach:	They propose a semantic-aware reward framework that optimizes for code semantic equivalence rather than lexical mimicry.
Outcome:	The proposed framework outperforms state-of-the-art frameworks on repository-level splits . it incorporates expert-aligned reasoning mechanism that grounds patch generation in structured diagnosis.

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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

ART: rule bAsed futuRe-inference deducTion (2023.emnlp-main)

Challenge:	Current large language models (LLMs) show a significant performance gap in alignment between English and other languages.
Approach:	They propose a consistency-based method to construct high-quality multilingual preference data for improving multilingual alignment.
Outcome:	The proposed method is based on three LLMs and three common tasks and shows that it performs better than current methods.

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Mengze Li, Tianqi Zhao, Bai Jionghao, Baoyi He, Jiaxu Miao, Wei Ji, Zheqi Lv, Zhou Zhao, Shengyu Zhang, Wenqiao Zhang, Fei Wu

Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis (2025.findings-acl)

Challenge:	Existing studies focus on language-based premises and deduce valid conclusions from visual observations.
Approach:	They propose a rule-based deductive reasoning task that uses video to deduce the correct future event . they use commonsense knowledge to annotate video and a strong baseline to conduct reasoning .
Outcome:	Empirical studies validate the rationality of ARTNet in deductive reasoning upon visual observations . ART is a method that rigorously follows a set of explicit constraints to deduce valid conclusions from empirical facts .

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Yicheng Lang, Kehan Guo, Yue Huang, Yujun Zhou, Haomin Zhuang, Tianyu Yang, Yao Su, Xiangliang Zhang

From Generation to Detection: A Multimodal Multi-Task Dataset for Benchmarking Health Misinformation (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have been used to remove harmful knowledge and undesirable capabilities.
Approach:	They propose a framework that leverages Cognitive Diagnosis Modeling to evaluate LLM unlearning.
Outcome:	The proposed framework enhances evaluation and facilitates removal of harmful abilities.

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Zhihao Zhang, Yiran Zhang, Xiyue Zhou, Liting Huang, Imran Razzak, Preslav Nakov, Usman Naseem

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

Challenge:	Infodemics and health misinformation have significant negative impact on individuals and society . generative AI has significantly accelerated the spread and expanded the reach of health misinfo .
Approach:	MM-Health is a large scale multimodal misinformation dataset in the health domain . it includes human-generated multimodal information and AI-generated multiplemodal information .
Outcome:	MM-Health is a large scale misinformation dataset in the health domain . it includes human-generated multimodal information and AI-generated content .

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

Opinion Tree Parsing for Aspect-based Sentiment Analysis (2023.findings-acl)

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

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Xiaoyi Bao, Xiaotong Jiang, Zhongqing Wang, Yue Zhang, Guodong Zhou

Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation (2021.findings-emnlp)

Challenge:	Existing generative models for aspect-based sentiment analysis lack structure well-formedness guarantees and built-in elements alignments.
Approach:	They propose an opinion tree parsing model which parses all sentiment elements from an opinion-tree.
Outcome:	The proposed model is much faster than previous models and can explore correlations among sentiment elements.

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Lei Shen, Jinchao Zhang, Jiao Ou, Xiaofang Zhao, Jie Zhou

TIU-Bench: A Benchmark for Evaluating Large Multimodal Models on Text-rich Image Understanding (2025.findings-emnlp)

Challenge:	Existing models for dialogue empathy focus on the emotion flow in one direction, from context to response.
Approach:	They propose a dual-generative model to construct emotional consensus and use unpaired data to produce pseudo paired empathetic samples.
Outcome:	The proposed model outperforms baseline models in producing coherent and empathetic responses.

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Kun Zhang, Liqiang Niu, Zhen Cao, Fandong Meng, Jie Zhou

Reasoning is All You Need for Video Generalization: A Counterfactual Benchmark with Sub-question Evaluation (2025.findings-acl)

Challenge:	Existing text-rich image understanding benchmarks lack scale and fragmented scenarios . a new full-image structured output format is proposed to enable fine-grained evaluation of perception and reasoning capabilities.
Approach:	They propose a large-scale, multilingual benchmark that includes over 100,000 annotations and 22,000 question-answer pairs.
Outcome:	The proposed framework provides a comprehensive platform for developing and evaluating next-generation multimodal AI systems.

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Qiji Zhou, YiFan Gong, Guangsheng Bao, Hongjie Qiu, Jinqiang Li, Xiangrong Zhu, Huajian Zhang, Yue Zhang

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

Challenge:	Existing multimodal benchmarks often overlook counterfactual reasoning, which is crucial for robust video understanding.
Approach:	They propose a multidimensional multimodal benchmark that systematically evaluates MLLMs across the abstract-concrete and perception-cognition dimensions.
Outcome:	The proposed model decomposes complex queries into structured sub-questions, enabling fine-grained reasoning analysis.

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

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

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

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

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

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

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

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

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

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Xiaotong Zhang, Qianru Zhou, Han Liu, Hong Yu

Proactive Human-Machine Conversation with Explicit Conversation Goal (P19-1)

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

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Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, Haifeng Wang

LADR: Locality-Aware Dynamic Rescue for Efficient Text-to-Image Generation with Diffusion Large Language Models (2026.acl-long)

Challenge:	Typical human-machine conversation systems only use utterances and responses as training data, which results in uninformative and inappropriate responses.
Approach:	They propose a dataset where one acts as a conversation leader and the other as 'follower' they establish baseline results on a 270K utterances and 30k dialogues dataset using state-of-the-art models.
Outcome:	The proposed model can generate diverse multi-turn conversations using knowledge from a new dataset .

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Chenglin Wang, Yucheng Zhou, Shuang Chen, Tao Wang, Kai Zhang

DECOR: Improving Coherence in L2 English Writing with a Novel Benchmark for Incoherence Detection, Reasoning, and Rewriting (2024.emnlp-main)

Challenge:	Existing methods for inference are expensive and lack spatial redundancy . Discrete Diffusion Language Models are a promising paradigm for multimodal generation .
Approach:	They propose a locality-aware dynamic rescue method that exploits spatial Markov property of images.
Outcome:	The proposed method achieves an approximate 4 speedup over baselines on four text-to-image generation benchmarks.

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Xuanming Zhang, Anthony Diaz, Zixun Chen, Qingyang Wu, Kun Qian, Erik Voss, Zhou Yu

Understanding Conflicts in Multi-Objective Alignment through Reward Consistency (2026.findings-acl)

Challenge:	Existing automated writing evaluation systems only detect incoherence in writing . a recent study has found that incorporating specific reasons for incohence improves the quality of rewrites .
Approach:	They propose a benchmark that includes expert annotations for detecting incoherence in L2 English writing, identifying the underlying reasons, and rewriting the incoerent sentences.
Outcome:	The proposed benchmark improves coherence in L2 English writing by fine-tuning models . the authors find that incorporating specific reasons improves quality of rewrites .

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Zhihao Xu, Yongqi Tong, Xin Zhang, Jun Zhou, Xiting Wang

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

Challenge:	Existing training pipelines still face alignment conflicts where optimizing for one objective degrades performance on others.
Approach:	They propose a reward-based criterion that approximates alignment conflicts via reward models.
Outcome:	The proposed framework improves harmlessness and helpfulness scores by 23.07% over the vanilla dataset.

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

Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View (2026.findings-acl)

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

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Hao Liao, Jiwei Zhang, Jianxun Lian, Wensheng Lu, Mingqi Wu, null Shuowangg, Yong Zhang, Yitian Huang, Mingyang Zhou, Rui Mao

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

Challenge:	Existing approaches to reduce OOD recommendations fall into three grounding paradigms: retrieval, constrained generation and discrete item tokenizer generation.
Approach:	They propose a framework that instantiates three grounding paradigms under a single architecture . embedding-based retrieval, constrained generation and discrete item-tokenizer methods are implemented .
Outcome:	The proposed framework eradicates OOD recommendations across all variants and achieves state-of-the-art accuracy compared to strong ID-based and LLM-based baselines.

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

Structure-Aware Zero-Shot Relational Learning for Knowledge Graphs without External Knowledge (2026.findings-acl)

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

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Kuan Xu, Baoxin Zhang, Shuyue Fan, Ming Chen, Zhipeng Ke, Jian Yu, Xuezhong Zhou

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

Challenge:	Existing methods for Zero-shot Relational Learning depend on external knowledge, resulting in increased annotation costs and limited practical applicability.
Approach:	They propose a structure-aware paradigm that performs ZRL without external knowledge . it leverages intrinsic structural patterns in KGs to bridge semantic correlations for new relations with existing ones.
Outcome:	The proposed paradigm achieves 10.66% improvement in MRR while reducing annotation costs and enhancing practical applicability on three real-world benchmarks.

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

Enhancing LLM Capabilities Beyond Scaling Up (2024.emnlp-tutorials)

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

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Wenpeng Yin, Muhao Chen, Rui Zhang, Ben Zhou, Fei Wang, Dan Roth

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

Challenge:	general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data.
Approach:	This tutorial aims to examine the capabilities of general-purpose large language models . authors discuss adaptation of LLMs to address conflicts, defense against attacks .
Outcome:	This tutorial aims to examine the evolution of general-purpose large language models (LLMs) the authors argue that the evolution is dependent on the availability of training data and the scale of the models.

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

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

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

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

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

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

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

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

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

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

Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions (2022.emnlp-industry)

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

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Rindra Ramamonjison, Haley Li, Timothy Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-dehkordi, Zirui Zhou, Yong Zhang

Challenge:	Existing systems for operations research use NLP to suggest formulations of optimization problems.
Approach:	They propose an augmented intelligence system that can be used to simplify and enhance the modeling experience for operations research.
Outcome:	The proposed system validates and edits the proposed formulations with a dataset of linear programming problems drawn from various application domains.

Search-o1: Agentic Search-Enhanced Large Reasoning Models (2025.emnlp-main)

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Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, Zhicheng Dou

Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information (2023.findings-emnlp)

Challenge:	Large reasoning models (LRMs) have demonstrated impressive long stepwise reasoning capabilities through large-scale reinforcement learning.
Approach:	They propose a framework that enhances large reasoning models with an agentic retrieval-augmented generation mechanism and a Reason-in-Documents module for refining retrieved documents.
Outcome:	The proposed framework enhances LRMs with an agentic retrieval-augmented generation mechanism and Reason-in-Documents module for refining retrieved documents.

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Jie Zhou, Yue Zhang

Controllable Style Arithmetic with Language Models (2025.acl-long)

Challenge:	Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents.
Approach:	They propose an Efficient Context-aware ASE model that fully exploits contextual information by augmenting modeling capacity and augmenting training data.
Outcome:	The proposed model can extract argumentative discourse structure from documents and reduce reliance on specific words or less informative sentences.

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Weiqi Wang, Wengang Zhou, Zongmeng Zhang, Jie Zhao, Houqiang Li

LeLoRA: Learnable Low-Rank Adaptation of Large Language Models (2026.acl-long)

Challenge:	Existing methods for linguistic style control lack fine-grained control, require extensive computation, or introduce significant latency.
Approach:	They propose a parameter-space approach that extracts style-specific representations by analyzing parameter differences between models trained on contrasting styles and incorporates them into a model with precise control over style intensity.
Outcome:	The proposed approach achieves three key capabilities while achieving optimal computational efficiency.

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Xiaoling Zhou, Mingjie Zhang, Zhemg Lee, Wei Ye, Shikun Zhang

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

Challenge:	Existing approaches to fine-tuning large language models (LLMs) rely on manually specified and fixed hyperparameters, resulting in suboptimal performance and low parameter efficiency.
Approach:	They propose a framework that allows for dynamically learned adaptive adaptation strategies to be used to fine-tune large language models.
Outcome:	The proposed framework outperforms baselines in adapting large language 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

SEE: Signal Embedding Energy for Quantifying Noise Interference in Large Audio Language Models (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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Yuanhe Zhang, Jiayu Tian, Yibo Zhang, Shilinlu Yan, Liang Lin, Zhenhong Zhou, Li Sun, Sen Su

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts (2026.acl-long)

Challenge:	Existing studies on noise lack quantitative analysis and rely on intuition and empirical observation, thus failing to understand practical robustness.
Approach:	They propose a method for quantifying the impact of noise intensity on LALM inputs by using a structured activation subspace derived from the model's internal representations.
Outcome:	The proposed method outperforms existing denoising methods and demonstrates that noise is perceived more accurately than raw audio features.

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Haolei Xu, Haiwen Hong, Hongxing Li, Rui Zhou, Yang Zhang, Longtao Huang, Hui Xue, Yongliang Shen, Weiming Lu, Yueting Zhuang

CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)

Challenge:	Existing multimodal Mixture-of-Experts models accurately perceive image content yet fail in subsequent reasoning . Seeing but not thinking phenomenon is a puzzling phenomenon .
Approach:	They propose a routing-guided intervention method that enhances domain expert activation.
Outcome:	The proposed method achieves consistent improvements on visual reasoning tasks.

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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu

Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction (2022.acl-long)

Challenge:	a recent study shows that large language models have limited generalization in low-resource languages like Chinese.
Approach:	They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private .
Outcome:	The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language.

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Kunyuan Pang, Haoyu Zhang, Jie Zhou, Ting Wang

Bring Invariant to Variant: A Contrastive Prompt-based Framework for Temporal Knowledge Graph Forecasting (2024.lrec-main)

Challenge:	Existing FET noise learning methods rely on prediction distributions in instance-independent manner, which causes confirmation bias.
Approach:	They propose a clustering-based loss correction framework to address confirmation bias in FET . they first train a coarse backbone model as a feature extractor and noise estimator .
Outcome:	The proposed framework achieves the best performance over existing systems on three public datasets and is stable to hyperparameters.

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Ying Zhang, Xinying Qian, Yu Zhao, Baohang Zhou, Kehui Song, Xiaojie Yuan

Question-type Driven Question Generation (D19-1)

Challenge:	Existing methods for temporal knowledge graph forecasting are insufficient structural contexts to learn effective representations.
Approach:	They propose a Contrastive Prompt-based framework with Entity background information for TKG forecasting that brings time-invariant entity background information to time-variant structural information.
Outcome:	The proposed framework is effective and stays competitive in inference with limited structural information.

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Wenjie Zhou, Minghua Zhang, Yunfang Wu

Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data (2026.findings-acl)

Challenge:	Existing work suffers from mismatching between question type and answer . existing work fails to generate questions with type how while answer is personal name .
Approach:	They propose to automatically predict the question type based on the input answer and context.
Outcome:	The proposed model improves on both SQuAD and MARCO datasets and improves accuracy on the input answer and context.

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Xuanming Zhang, Shwan Ashrafi, Aziza Mirsaidova, Amir H. Rezaeian, Miguel Ballesteros, Lydia Chilton, Zhou Yu, Dan Roth

SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence (2025.emnlp-main)

Challenge:	Recent work has explored reasoning efficiency via test-time scaling and early exit strategies.
Approach:	They propose an anytime reasoning framework and the Anytime Index to improve model quality . they also propose an inference-time self-improvement method to produce better intermediate solutions .
Outcome:	The proposed method improves on NaturalPlan, AIME, and GPQA datasets and improves reasoning quality and efficiency under budget constraints.

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Yao Zhang, Chenyang Lin, Shijie Tang, Haokun Chen, Shijie Zhou, Yunpu Ma, Volker Tresp

Challenge:	Existing agentic system generation frameworks lack autonomy, autonomy, and functionality . current frameworks are too rigid, limiting adaptability and scalability.
Approach:	They propose a framework that fully automates agentic system generation, optimization, and collaboration . they construct agents from scratch and jointly refine functionality and coordination .
Outcome:	The proposed framework outperforms ADAS on six real-world, open-ended, and exploratory tasks on the TravelPlanner benchmark.

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

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

FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering (2026.findings-acl)

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

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Yixi Zhou, Fan Zhang, YU Chen, Haipeng Zhang, Preslav Nakov, Zhuohan Xie

A Survey on Training-free Alignment of Large Language Models (2025.findings-emnlp)

Challenge:	Existing reranking frameworks optimize semantic relevance, leading to unstable rankings and opaque decisions on long documents.
Approach:	They propose a structured reranking framework that reframes financial evidence selection as constraint satisfaction under a finance-aware schema.
Outcome:	FINCARDS improves early-rank retrieval over lexical and LLM-based reranking baselines while reducing ranking variance.

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Birong Pan, Yongqi Li, Weiyu Zhang, Wenpeng Lu, Mayi Xu, Shen Zhou, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

Sentiment Forecasting in Dialog (2020.coling-main)

Challenge:	a survey of large language models (LLMs) aims to ensure outputs adhere to human values, ethical standards, and legal norms.
Approach:	They present the first systematic review of TF alignment methods . they categorize them by stages of pre-decoding, in-decoder and post-decoration .
Outcome:	The proposed methods are based on training-free (TF) alignment techniques . they are able to be used in open-source and closed-source environments without retraining .

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Zhongqing Wang, Xiujun Zhu, Yue Zhang, Shoushan Li, Guodong Zhou

UICOMPASS: UI Map Guided Mobile Task Automation via Adaptive Action Generation (2025.emnlp-main)

Challenge:	Existing studies on sentiment classification focus on determining polarity of existing utterances.
Approach:	They propose a Neural Sentiment Forecasting task which simulates the next utterance based on context and a sequence influence model to learn both pair-wise and seq-wise influence.
Outcome:	The proposed model outperforms existing models over several strong baselines.

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Yuanzhang Lin, Zhe Zhang, He Rui, Qingao Dong, Mingyi Zhou, Jing Zhang, Xiang Gao, Hailong Sun

Challenge:	Mobile task automation is an emerging technology that leverages AI to automatically execute routine tasks by users’ commands on mobile devices like Android.
Approach:	They propose a UI Map-guided LLM-based approach to automate mobile tasks using static analysis and LLMs.
Outcome:	The proposed approach achieves a 15.87% higher task execution success rate than SOTA approaches even when only APK is available.

Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process (2025.acl-long)

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Ermo Hua, Biqing Qi, Kaiyan Zhang, Kai Tian, Xingtai Lv, Ning Ding, Bowen Zhou

Syntax-Aware Retrieval Augmented Code Generation (2023.findings-emnlp)

Challenge:	Supervised Fine-Tuning (SFT) and Preference Optimization (PO) are key processes for aligning Language Models with human preferences post pre-training.
Approach:	They propose to combine Supervised Fine-Tuning and Preference Optimization (PO) with two sub-processes defined at token level within the Markov Decision Process (MDP)
Outcome:	The proposed process performs comparably or even superiorly to SFT and some typical PO methods across several tasks, particularly those requires generation, reasoning, and fact-following abilities.

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Xiangyu Zhang, Yu Zhou, Guang Yang, Taolue Chen

Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction (2020.emnlp-main)

Challenge:	Neural code generation models with token-level retrieval capabilities are often noisy and time-consuming.
Approach:	They propose a token-level retrieval augmented code generation method that leverages syntax constraints for the retrieval of datastores.
Outcome:	The proposed method reduces the impact of retrieve noise on code generation on two datasets.

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Mengyun Chen, Tao Ge, Xingxing Zhang, Furu Wei, Ming Zhou

Beyond Static Persona Consistency: Dynamic Persona Coherence in LLM Role-Playing (2026.acl-long)

Challenge:	Existing methods to improve the efficiency of GEC are not efficient enough for GEC.
Approach:	They propose a language-independent approach to improve the efficiency of GEC by dividing the task into two subtasks: ESD and ESC.
Outcome:	The proposed approach performs comparably to conventional seq2seq approaches in English and Chinese GEC benchmarks with less than 50% time cost for inference.

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Yirui QI, Xiaoming Zhang, Ruilin Zeng, Mengyao Liu, Ziyi Zhou, Dezhuang Miao, Bingyu Yan, Zhenyu Guan

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

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

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

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

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

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

EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning (2026.acl-long)

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

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Chuanrui Hu, Xingze Gao, Zuyi Zhou, Dannong Xu, Yi Bai, Xintong Li, Hui Zhang, Tong Li, Chong Zhang, Lidong Bing, Yafeng Deng

MovieChats: Chat like Humans in a Closed Domain (2020.emnlp-main)

Challenge:	Existing memory systems for LLMs store isolated records and retrieve fragments . Existing systems store isolated data and fragments, limiting their ability to consolidate evolving experience and resolve conflicts.
Approach:	They propose an engram-inspired memory operating system that implements an 'engram'-inspired lifecycle for computational memory.
Outcome:	Experiments on LoCoMo, LongMemEval, and PersonaMeM-v2 show that EverMemeOS outperforms state-of-the-art methods on memory-augmented reasoning tasks.

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Hui Su, Xiaoyu Shen, Zhou Xiao, Zheng Zhang, Ernie Chang, Cheng Zhang, Cheng Niu, Jie Zhou

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

Challenge:	Currently, open-domain chatbots are far from satisfactory.
Approach:	They propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval.
Outcome:	The proposed approach outperforms commercial systems replying on complex rules on static and interactive tests and shows that the results are remarkably good.

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

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

From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning (2024.naacl-long)

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Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, Jing Xiao

Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have revolutionized the landscape of artificial intelligence.
Approach:	They propose a self-guided method to identify and select cherry samples from open-source datasets, minimizing manual curation and potential cost for instruction tuning an LLM.
Outcome:	The proposed method enables LLMs to identify discrepancies between expected responses and intrinsic generation capability, and a marked uptick in model training efficiency.

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Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu

RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library (2026.findings-eacl)

Challenge:	Large language models struggle with complex reasoning tasks, such as mathematical problem-solving.
Approach:	They constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning.
Outcome:	The proposed algorithm improves on 7 multi-step reasoning datasets, while introducing only 132 trainable parameters.

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Jiapeng Wang, Jinhao Jiang, Zhiqiang Zhang, Jun Zhou, Xin Zhao

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

Challenge:	Existing methods for generating high-quality reasoning data are limited in quality and availability.
Approach:	They propose a method that constructs mathematical operations and generates verifiable graphs that are back-translated into complex problems.
Outcome:	The proposed method achieves a 6.3% performance gain over existing methods on LLaMA-3-8B and outperforms others with only half the training data (50k vs. 100k).

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

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

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

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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li

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

Challenge:	Document AI parsing semi-structured image form is a key information extraction task.
Approach:	They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework.
Outcome:	The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings.

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

Neural Latent Extractive Document Summarization (D18-1)

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

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Xingxing Zhang, Mirella Lapata, Furu Wei, Ming Zhou

Unraveling LoRA Interference: Orthogonal Subspaces for Robust Model Merging (2025.acl-long)

Challenge:	Existing summarization paradigms focus on extractive summarizing based on sentence level labels .
Approach:	They propose a latent variable extractive model where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries.
Outcome:	The proposed model outperforms a strong extractive baseline trained on rule-based labels and performs competitively with several recent models.

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Haobo Zhang, Jiayu Zhou

CoRE: A Fine-Grained Code Reasoning Benchmark Beyond Output Prediction (2026.findings-acl)

Challenge:	Existing methods for fine-tuning large language models fail due to performance degradation . existing methods fail for models fine- tuned with low-rank adaptation .
Approach:	They propose to constrain the LoRA subspace prior to fine-tuning to ensure that updates relevant to one task do not adversely shift outputs for others.
Outcome:	The proposed method can integrate with most existing merging algorithms, reducing unintended interference among tasks.

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Jun Gao, Yun Peng, Qian Qiao, Changhai Zhou, Yuhua Zhou, Shiyang Zhang, Shichao Weng, Zhenchang Xing, Xiaoxue Ren

DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding (2024.lrec-main)

Challenge:	Existing code reasoning benchmarks evaluate final output correctness under a single implementation.
Approach:	They propose a Code Reasoning benchmark that evaluates code reasoning through implementation invariance and process transparency.
Outcome:	The proposed benchmarks lack implementation invariance and process transparency . they observe superficial execution where models arrive at correct outputs without reasoning .

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Jianhao Yan, Jin Xu, Fandong Meng, Jie Zhou, Yue Zhang

Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)

Challenge:	Existing methods for decoding target language are degenerate, hallucinating or empty.
Approach:	They propose a method that tunes down the Softmax temperature to reduce autoregressive over-smoothness by label smoothing the output distributions.
Outcome:	The proposed method improves MBR in various settings.

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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao

GroundingGPT: Language Enhanced Multi-modal Grounding Model (2024.acl-long)

Challenge:	Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications.
Approach:	They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions.
Outcome:	The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions.

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Zhaowei Li, Qi Xu, Dong Zhang, Hang Song, YiQing Cai, Qi Qi, Ran Zhou, Junting Pan, Zefeng Li, Vu Tu, Zhida Huang, Tao Wang

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

Challenge:	Existing multi-modal large language models focus on capturing global information while neglecting the fine-grained local information in multimodal inputs.
Approach:	They propose an end-to-end language enhanced multi-modal grounding model that performs fine-grained grounding tasks for image, video and audio.
Outcome:	The proposed model achieves impressive fine-grained understanding of multi-modal inputs while maintaining or improving its global comprehension capabilities.

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

Unveiling Inherent Visual Grounding in Multimodal LLMs for Text-Rich Images (2026.findings-acl)

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

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Shijie Zhou, Jihyung Kil, Ming Li, Jiuxiang Gu, Curtis Wigington, Rajiv Jain, Changyou Chen, Ruiyi Zhang

HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization (P19-1)

Challenge:	Existing multimodal large language model (MLLM) approaches struggle to align query tokens with visual–text patches, heavily relying on lengthy OCR inputs.
Approach:	They propose an OCR-free approach that leverages the MLLM's inherent multi-head attention for multi-patch grounding.
Outcome:	Empirical results show that the proposed approach outperforms existing approaches on challenging document grounding benchmarks.

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Xingxing Zhang, Furu Wei, Ming Zhou

Understanding and Mitigating Overrefusal in LLMs from an Unveiling Perspective of Safety Decision Boundary (2025.emnlp-main)

Challenge:	Neural extractive summarization models employ hierarchical encoders with inaccurate sentence-level labels.
Approach:	They propose a method to pre-train a hierarchical encoder with unlabeled data.
Outcome:	The proposed model outperforms its initialized counterpart by 1.25 ROUGE on CNN and 2.0 ROUGEE on a version of New York Times dataset.

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Licheng Pan, Yongqi Tong, Xin Zhang, Xiaolu Zhang, Jun Zhou, Zhixuan Chu

Dynamic Model-Bank Test-Time Adaptation for Automatic Speech Recognition (2025.emnlp-main)

Challenge:	Large language models (LLMs) often refuse to answer legitimate queries, causing models to treat many reasonable prompts as potentially risky.
Approach:	They propose a framework that automatically generates and selects overrefusal prompts near the safety boundary.
Outcome:	The proposed framework identifies and curates boundary-aligned prompts, enabling more effective and targeted mitigation of overrefusal.

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Yanshuo Wang, Yanghao Zhou, Yukang Lin, Haoxing Chen, Jin Zhang, Wentao Zhu, Jie Hong, Xuesong Li

CMD: a framework for Context-aware Model self-Detoxification (2024.emnlp-main)

Challenge:	Existing ASR TTA methods struggle with instability under continual and long-term distribution shifts.
Approach:	They propose a continuous adaptive model-bank framework that adapts to domain shifts in ASR test-time scenarios.
Outcome:	Experiments on diverse, continuously shifting ASR benchmarks show that DMSUTA outperforms existing continual TTA baselines.

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Zecheng Tang, Keyan Zhou, Juntao Li, Yuyang Ding, Pinzheng Wang, Yan Bowen, Renjie Hua, Min Zhang

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

Challenge:	Existing methods of text detoxification fail to achieve a decent balance between effectiveness and generation quality.
Approach:	They propose a text detoxification framework that pays attention to both context and detoxification process.
Outcome:	Experiments on various LLMs show that the proposed framework can yield the best performance compared to baselines.

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

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

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

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

Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation (2022.naacl-main)

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

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Siyu Lai, Zhen Yang, Fandong Meng, Xue Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

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

Challenge:	Existing approaches to generate adversarial examples for NMT use the meaning-preserving restriction.
Approach:	They propose a new definition for adversarial examples based on the Doubly Round-Trip Translation (DRTT) they introduce masked language models to construct bilingual adversarials based upon DRTT .
Outcome:	The proposed approach significantly improves the robustness of the NMT model on clean and noisy test sets.

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

XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)

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

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Fang Guo, Qinglin Qi, Jie Zhou, Yue Zhang

Improving Neural Machine Translation with Soft Template Prediction (2020.acl-main)

Challenge:	Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration.
Approach:	They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification.
Outcome:	The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions.

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Jian Yang, Shuming Ma, Dongdong Zhang, Zhoujun Li, Ming Zhou

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

Challenge:	Recent advances in neural machine translation (NMT) depend on source text to generate translation.
Approach:	They propose to use extracted templates from tree structures as soft target templates to guide the translation procedure.
Outcome:	The proposed model outperforms baseline models on four benchmarks and demonstrates the effectiveness of soft target templates.

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

CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases (2025.naacl-long)

Challenge:	With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks.
Approach:	They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information.
Outcome:	The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority.

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Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Qizhe Shieh, Wenmeng Zhou

Reduce Redundancy Then Rerank: Enhancing Code Summarization with a Novel Pipeline Framework (2024.lrec-main)

Challenge:	Large Language Models excel in stand-alone code tasks but struggle with handling entire code repositories.
Approach:	They propose a system that integrates LLM agents with graph database interfaces extracted from code repositories.
Outcome:	The proposed system integrates LLM agents with graph database interfaces extracted from code repositories.

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Xiaoyu Hu, Xu Zhang, Zexu Lin, Deyu Zhou

Enhancing Byzantine-Resistant Aggregations with Client Embedding (2024.findings-emnlp)

Challenge:	Existing code summarization models lack redundant tokens and are plagued by exposure bias.
Approach:	They propose a pipeline framework to reduce redundancy then rerank that eliminates redundant information in code representation space and a re-ranking model to select more suitable summary candidates.
Outcome:	The proposed framework overrides state-of-the-art approaches on six datasets from the CodeSearchNet benchmark.

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Zhiyuan Zhang, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Pretraining Context Compressor for Large Language Models with Embedding-Based Memory (2025.acl-long)

Challenge:	Existing Byzantine-resistant aggregations detect poisonous clients but cannot defend against backdoor injection by malicious attackers in natural language tasks.
Approach:	They propose to embed client parameters to enhance Byzantine-resistant aggregations.
Outcome:	The proposed client embeddings detect poisonous clients and discard them . the proposed algorithms can't defend against backdoor injection by malicious attackers in natural language tasks .

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Yuhong Dai, Jianxun Lian, Yitian Huang, Wei Zhang, Mingyang Zhou, Mingqi Wu, Xing Xie, Hao Liao

Adversarial Attack against Cross-lingual Knowledge Graph Alignment (2021.emnlp-main)

Challenge:	Efficient processing of long contexts in large language models is essential for real-world applications such as retrieval-augmented generation and in-context learning.
Approach:	They propose a decoupled compressor-LLM framework that preserves contextual information within condensed embedding representations.
Outcome:	The proposed framework outperforms baseline models in three domains and across eight datasets while adapting to different downstream LLMs.

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Zeru Zhang, Zijie Zhang, Yang Zhou, Lingfei Wu, Sixing Wu, Xiaoying Han, Dejing Dou, Tianshi Che, Da Yan

CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation (2023.acl-long)

Challenge:	Existing studies on cross-lingual entity alignment under adversarial attacks have not been conducted.
Approach:	They propose to use adversarial attack techniques to perturb cross-lingual entity alignment under adversarials.
Outcome:	The proposed model hides the attacked entities in dense regions in two KGs, and reduces the gradient vanishing issues in the process of adversarial attacks for further improving the attack effectiveness.

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Jinfeng Zhou, Chujie Zheng, Bo Wang, Zheng Zhang, Minlie Huang

PD3F: A Pluggable and Dynamic DoS-Defense Framework against resource consumption attacks targeting Large Language Models (2025.findings-emnlp)

Challenge:	Existing empathetic dialogue models only consider the affective aspect of empathy, which limits the capability of emotional response generation.
Approach:	They propose a model that aligns the user's cognition and affection at both the coarse-grained and fine-grounded levels and then automatically and manually evaluates the model.
Outcome:	The proposed model outperforms state-of-the-art models and generates more empathetic and informative responses.

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Yuanhe Zhang, Xinyue Wang, Haoran Gao, Zhenhong Zhou, Fanyu Meng, Yuyao Zhang, Sen Su

RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)

Challenge:	Existing work lacks mitigation strategies against resource consumption attacks . existing work does not provide mitigation strategies for real-world LLM deployments .
Approach:	They propose a pluggable and dynamic doS-Defense framework which employs a two-stage approach to defend against resource consumption attacks from both the input and output sides.
Outcome:	The proposed framework significantly mitigates resource consumption attacks, improving users’ access capacity by up to 500% during adversarial load.

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Junjie Ye, Jie Zhou, Junfeng Tian, Rui Wang, Qi Zhang, Tao Gui, Xuanjing Huang

Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention (2024.emnlp-main)

Challenge:	Recent studies have shown that current TMSC systems rely on textual information, and the progress in tackling this task has slowed down.
Approach:	They propose to integrate both visual and textual information to improve the performance of TMSC by considering multimodal information.
Outcome:	The proposed model integrates both visual and textual information to improve performance.

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Xingtai Lv, Ning Ding, Kaiyan Zhang, Ermo Hua, Ganqu Cui, Bowen Zhou

MA2P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion (2026.findings-acl)

Challenge:	Existing studies have found that low-rank pre-training often compromises effectiveness.
Approach:	They propose to apply low-dimensional module only to the attention layer to improve both effectiveness and efficiency.
Outcome:	The proposed model saves 12.4% time while improving test perplexity and on downstream tasks compared with vanilla Transformer.

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Dingyi Zhang, Ziqing Zhuang, Linhai Zhang, Ziyang Gao, Deyu Zhou

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

Challenge:	Existing approaches to persuasion generate generic or weakly grounded responses even when such cues are identified.
Approach:	They propose a meta-cognitive autonomous intelligent agent framework for complex persuasion that coordinates perception management, mental-state inference, strategy execution, memory maintenance, and performance evaluation.
Outcome:	The proposed framework achieves a higher persuasion success rate than baselines.

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

Neural Topic Modeling based on Cycle Adversarial Training and Contrastive Learning (2023.findings-acl)

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

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Boyu Wang, Linhai Zhang, Deyu Zhou, Yi Cao, Jiandong Ding

Exploring Artificial Image Generation for Stance Detection (2025.emnlp-main)

Challenge:	Neural topic models have been widely used to extract common topics across documents.
Approach:	They propose a framework to apply contrastive learning directly to the decoder . they propose 'self-supervised' contrastive loss to make the generator capture similar topic information .
Outcome:	The proposed framework outperforms baselines on four benchmark datasets.

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Zhengkang Zhang, Zhongqing Wang, Guodong Zhou

Multilingual Knowledge Editing with Language-Agnostic Factual Neurons (2025.coling-main)

Challenge:	Existing approaches to stance detection focus on textual content, which may not capture the implicit stance conveyed by the author.
Approach:	They propose a novel approach that transforms original texts into artificially generated images and uses the visual representation to enhance stance detection.
Outcome:	The proposed model is able to detect author's stance from a set of artificially generated images and then leverages both the original textual content and the generated image to identify the author' stance.

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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

INFORM : Information eNtropy based multi-step reasoning FOR large language Models (2023.emnlp-main)

Challenge:	Existing methods to update factual knowledge overlook connections of same knowledge between different languages, resulting in knowledge conflicts and limited edit performance.
Approach:	They propose a method to edit multilingual knowledge simultaneously that avoids knowledge conflicts and improves edit performance.
Outcome:	The proposed method avoids knowledge conflicts and improves edit performance on bi-ZsRE and MzsRE benchmarks.

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Chuyue Zhou, Wangjie You, Juntao Li, Jing Ye, Kehai Chen, Min Zhang

Subspace Defense: Discarding Adversarial Perturbations by Learning a Subspace for Clean Signals (2024.lrec-main)

Challenge:	Large language models (LLMs) have demonstrated exceptional performance with dedicated Chain-of-Thought (CoT) prompts.
Approach:	They propose a new method by introducing information entropy as a criteria on for CoT prompt selection.
Outcome:	The proposed model outperforms existing models on seven reasoning benchmarks using two language models.

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Rui Zheng, Yuhao Zhou, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Existing models that extract discrete inputs into fixed-length representations are vulnerable to adversarial attacks that place perturbations on clean inputs to fool DNNs.
Approach:	They propose to inspect the subspaces of sample features through spectral analysis to better understand adversarial attacks.
Outcome:	The proposed strategy enables the model to inherently suppress adversaries, which boosts model robustness and motivates new directions of effective adversarial defense.

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Yidan Zhang, Yu Wan, Boyi Deng, Baosong Yang, Hao-Ran Wei, Fei Huang, Bowen Yu, Dayiheng Liu, Junyang Lin, Fei Huang, Jingren Zhou

LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)

Challenge:	Recent advances in large language models showcase varied multilingual capabilities across tasks . previous assessments focused on fundamental natural language processing (NLP) or isolated capability-specific tasks.
Approach:	They propose a multilingual multitask benchmark to assess multilingual capabilities . they use a large-scale benchmark covering fundamental and capability-specialized datasets .
Outcome:	The proposed benchmark compares models and tasks across languages and tasks and examines knowledge transfer from English to other languages.

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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou

TECA: A Two-stage Approach with Controllable Attention Soft Prompt for Few-shot Nested Named Entity Recognition (2024.lrec-main)

Challenge:	Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks.
Approach:	They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework.
Outcome:	The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks.

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Yuanyuan Xu, Linhai Zhang, Deyu Zhou

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

Challenge:	Existing methods for few-shot nested named entity recognition (NER) ignore relationship between inner and outer entities, which is crucial for fewshot ner.
Approach:	They propose a span-based method with a controllable attention soft prompt for few-shot nested named entity recognition (TECA) the span part identification provides possible entity mentions without an extra filtering module.
Outcome:	The proposed method outperforms baseline models on four benchmark datasets and outperformed competing models on F1-score by 5.62% on ACE04, 5.11% on ace05, 3.41% on KBP2017 and 0.7% on GENIA on the 10-shot setting.

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

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

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

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

Sentiment Analysis on Streaming User Reviews via Dual-Channel Dynamic Graph Neural Network (2023.emnlp-main)

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

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Xin Zhang, Linhai Zhang, Deyu Zhou

HyQE: Ranking Contexts with Hypothetical Query Embeddings (2024.findings-emnlp)

Challenge:	Existing methods for sentiment analysis on user reviews neglect their time-varying characteristics.
Approach:	They propose a dual-channel framework that models temporal user and product dynamics for sentiment analysis.
Outcome:	The proposed framework is superior to existing methods on five real-world datasets.

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Weichao Zhou, Jiaxin Zhang, Hilaf Hasson, Anu Singh, Wenchao Li

Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information (2024.findings-emnlp)

Challenge:	Existing approaches to rank contexts rely on similarity between contexts and queries, but these methods are limited by the number of candidate contexts.
Approach:	They propose a scalable ranking framework that combines embedding similarity and large language models without fine-tuning.
Outcome:	The proposed framework improves the performance across multiple benchmarks.

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Yongheng Zhang, Qiguang Chen, Jingxuan Zhou, Peng Wang, Jiasheng Si, Jin Wang, Wenpeng Lu, Libo Qin

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

Challenge:	Chain-of-Thought (CoT) is a key technique for enhancing the performance of Large Language Models.
Approach:	They propose a framework that optimizes outputs by utilizing wrong information and multi-perspective verification.
Outcome:	The proposed framework surpasses all baselines on 8 datasets and 5 LLMs.

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

Sinkhorn Distance Minimization for Knowledge Distillation (2024.lrec-main)

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

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Xiao Cui, Yulei Qin, Yuting Gao, Enwei Zhang, Zihan Xu, Tong Wu, Ke Li, Xing Sun, Wengang Zhou, Houqiang Li

SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding (2026.findings-acl)

Challenge:	Existing knowledge distillation methods investigate divergence measures but fail to deliver effective supervision when few distribution overlap exists between teacher and student.
Approach:	They propose a knowledge distillation method that exploits the Sinkhorn distance to ensure a nuanced assessment of the disparity between teacher and student distributions.
Outcome:	The proposed method outperforms state-of-the-art methods on all kinds of LLMs with encoder-only, encoder decoder, and decoded architectures.

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Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen

Learning to Ideate for Machine Learning Engineering Agents (2026.eacl-short)

Challenge:	Existing benchmarks for agentic repository-level code understanding overlook long tail topics and rely on memorized knowledge.
Approach:	They propose a repository-level agentic code understanding benchmark that uses long-tail repositories with executable environments to enforce topical balance.
Outcome:	Empirically, a Qwen3-8B model trained with the proposed benchmark outperforms GPT-4o by 2.3 points.

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Yunxiang Zhang, Kang Zhou, Zhichao Xu, Kiran Ramnath, Yun Zhou, Sangmin Woo, Haibo Ding, Lin Lee Cheong

Challenge:	Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness.
Approach:	They propose a framework that separates ideation from implementation that allows an implementation agent to request strategic help from a dedicated Ideator.
Outcome:	The proposed framework outperforms implementation-only agent baselines on MLE-Bench and can be trained with reinforcement learning to generate more effective ideas.

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

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

SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis (2022.naacl-main)

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

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Zheng Zhang, Zili Zhou, Yanna Wang

Plan Dynamically, Express Rhetorically: A Debate-Driven Rhetorical Framework for Argumentative Writing (2025.emnlp-main)

Challenge:	Aspect-based Sentiment Analysis (ABSA) aims to predict sentiment polarity towards aspects in sentences . a novel model for ABSA is proposed, but how to harness it is still a challenge .
Approach:	They propose a syntactic and semantic enhanced Graph Convolutional Network (SSEGCN) model for ABSA task using aspect-aware attention mechanism and self-attention.
Outcome:	The proposed model outperforms state-of-the-art methods on benchmark datasets.

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Xueguan Zhao, Wenpeng Lu, Chaoqun Zheng, Weiyu Zhang, Jiasheng Si, Deyu Zhou

Linguistic Theories

Challenge:	Argumentative essay generation (AEG) is a complex task that requires advanced semantic understanding, logical reasoning, and organized integration of perspectives.
Approach:	They propose a debate-driven rhetorical framework for argumentative writing that integrates Bitzer’s rhetorical situation theory to improve logical depth, argumentative diversity, and rhetorical persuasiveness.
Outcome:	The proposed framework improves logical depth, argumentative diversity, and rhetorical persuasiveness over existing state-of-the-art models.

ACIArena: Toward Unified Evaluation for Agent Cascading Injection (2026.acl-long)

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Hengyu An, Minxi Li, Jinghuai Zhang, Naen Xu, Chunyi Zhou, Changjiang Li, Xiaogang Xu, Tianyu Du, Shouling Ji

MWPO: Enhancing LLMs Performance through Multi-Weight Preference Strength and Length Optimization (2025.findings-acl)

Challenge:	Existing studies consider only limited attack strategies and simplified MAS settings, limiting their generalizability and comprehensive evaluation.
Approach:	They propose a framework to evaluate the robustness of Multi-Agent Systems (MAS) they propose unified evaluation suites spanning attack surfaces and attack objectives .
Outcome:	ACIArena provides a benchmark of 1,356 test cases for evaluating MAS robustness . it covers six widely used MAS implementations and provides measurable results .

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Shiyue Xu, Fu Zhang, Jingwei Cheng, Linfeng Zhou

WYWEB: A NLP Evaluation Benchmark For Classical Chinese (2023.findings-acl)

Challenge:	Existing offline alternatives to Reinforcement Learning from Human Feedback (RLHF) are available at https://github.com/AIR-hl/MWPO.
Approach:	They propose an offline method to optimize preference pairs based on implicit reward margins and response length margins by reweighting them using a geometric mixture.
Outcome:	The proposed method outperforms state-of-the-art methods on four different scales and reduces generation length by 9.4%.

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Bo Zhou, Qianglong Chen, Tianyu Wang, Xiaomi Zhong, Yin Zhang

Challenge:	Existing benchmarks for classical Chinese are inadequate to evaluate performance of different NLP models.
Approach:	They propose an evaluation benchmark for classical Chinese NLP, which evaluates existing models.
Outcome:	The proposed benchmark evaluates the performance of existing models in classical Chinese.

Automatic Slide Updating with User-Defined Dynamic Templates and Natural Language Instructions (2026.findings-acl)

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Kun Zhou, Jiakai He, Wenmian Yang, Zhensheng Wang, Yiquan Zhang, Weijia Jia

Challenge:	Existing automation methods follow fixed template filling and cannot support dynamic updates for diverse, user-authored decks.
Approach:	They propose a framework that combines multimodal slide parsing, natural language instruction grounding, and tool-augmented reasoning for tables, charts, and textual conclusions.
Outcome:	The proposed framework updates content while preserving layout and style while maintaining a strong reference baseline on DynaSlide.

Implicit Sentiment Analysis with Event-centered Text Representation (2021.emnlp-main)

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Deyu Zhou, Jianan Wang, Linhai Zhang, Yulan He

DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank (2023.findings-emnlp)

Challenge:	Existing methods for implicit sentiment analysis simply view noun phrases or entities in text as events or indirectly model events with sophisticated models.
Approach:	They propose an event-centric implicit sentiment analysis that utilizes the sentiment-aware event contained in a sentence to infer sentiment polarity.
Outcome:	The proposed model can detect sentiment in sentences without sentiment words and is compared to existing models on a benchmark dataset.

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Henry Zou, Yue Zhou, Weizhi Zhang, Cornelia Caragea

The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)

Challenge:	Existing studies utilize social media platforms such as Twitter to build models for crisis event analysis, but semi-supervised approaches require annotating vast amounts of data and are impractical due to limited response time.
Approach:	They propose a method that stores and performs equal sampling for generated pseudo-labels from each class at each training iteration.
Outcome:	The proposed method performs better than existing methods in both in-distribution and out-of-difference settings.

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Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam

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

Challenge:	Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others.
Approach:	They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM.
Outcome:	The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants.

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

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

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

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Kai Liu, Ze Chen, Zhihang Fu, Wei Zhang, Rongxin Jiang, Fan Zhou, Yaowu Chen, Yue Wu, Jieping Ye

AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)

Challenge:	Structure-aware Continual Pre-Training (SCPT) and Structure-Aware Supervised Fine-Tuning (SSFT) are two-stage strategies for knowledge injection and alignment that reduces the training corpus needs to 5% while achieving 100% of traditional knowledge injection performance.
Approach:	They propose a method to efficiently transform foundation Large Language Models into domain specialists by using two-stage strategies: Structure-aware Continual Pre-Training and Structure-Aware Supervised Fine-Tuning.
Outcome:	The proposed method significantly reduces the training corpus needs to a mere 5% while achieving 100% of traditional knowledge injection performance.

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Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin

Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse (2026.acl-long)

Challenge:	Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora .
Approach:	They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process .
Outcome:	The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query .

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Chi Zhang, Mengqi Zhang, Xiaotian Ye, Runxi Cheng, Zisheng Zhou, Ying Zhou, Pengjie Ren, Zhumin Chen

Challenge:	Existing approaches to reducing the effects of knowledge editing are insufficiently understood.
Approach:	They propose a plug-and-play framework that preserves the dominant subspace of the original weights and analyzes parameter updates in the spectral basis of the weights.
Outcome:	The proposed framework improves editing efficacy while preserving general abilities under long-horizon sequential editing, including extreme settings with up to 20,000 edits.

GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond (2024.findings-naacl)

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Shen Zheng, Yuyu Zhang, Yijie Zhu, Chenguang Xi, Pengyang Gao, Zhou Xun, Kevin Chang

Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation (2026.acl-long)

Challenge:	Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may encourage cherry-picking favored settings and for better results.
Approach:	They propose an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals that systematically evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories.
Outcome:	The evaluation suite is built on top of OpenAI Evals and evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories.

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Jiawei Zhou, Chi Zhang, Xiang Feng, Qiming Zhang, Haibo Qiu, Lihuo He, Dengpan Ye, Xinbo Gao, Jing Zhang

Ask Question First for Enhancing Lifelong Language Learning (2022.coling-1)

Challenge:	a benchmark is designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executable code.
Approach:	They propose a benchmark to evaluate the capability of Large Multimodal Models to convert digital graphics into executable code.
Outcome:	The proposed benchmark exposes the performance gap among leading LMMs . the benchmark features 1130 meticulously curated samples .

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Han Wang, Ruiliu Fu, Xuejun Zhang, Jun Zhou, Qingwei Zhao

Challenge:	Existing approaches to stream learning NLP tasks suffer from catastrophic forgetting and are exacerbated when the previous task’s pseudo data is insufficient.
Approach:	They propose to use a new data format to train pseudo questions of previous tasks to stream learning NLP tasks while retaining knowledge of previous ones.
Outcome:	The proposed model is more robust to sufficient and insufficient pseudo-data when the task boundary is both clear and unclear.

View-R1: Asymmetric Policy Optimization for Difficulty-Aware Multimodal Reinforcement Learning (2026.findings-acl)

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Minjie Hong, Zirun Guo, Jiabao Zhang, Zehan Wang, Ziang Zhang, Tao Jin, Zhou Zhao

OpenAttack: An Open-source Textual Adversarial Attack Toolkit (2021.acl-demo)

Challenge:	Multimodal Large Language Models (MLLMs) are powerful at integrating diverse data but struggle with complex reasoning.
Approach:	They propose a method which separates responses into positive and negative groups to stabilize training and preserve knowledge.
Outcome:	The proposed model View-R1 achieves a 10.55% improvement in reasoning and outperforms larger models while maintaining and improving performance on general tasks.

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Guoyang Zeng, Fanchao Qi, Qianrui Zhou, Tingji Zhang, Zixian Ma, Bairu Hou, Yuan Zang, Zhiyuan Liu, Maosong Sun

Perceiving the World: Question-guided Reinforcement Learning for Text-based Games (2022.acl-long)

Challenge:	Various attack models are distinct and implemented with different programming frameworks and settings, which hinders quick utilization and fair comparison of attack models.
Approach:	They propose an open-source textual adversarial attack toolkit to solve these issues by combining 15 typical attack models into one toolkit.
Outcome:	The proposed toolkit supports all attack types, multilinguality, and parallel processing.

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Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Joey Zhou, Chengqi Zhang

A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment (2025.findings-acl)

Challenge:	Text-based games provide an interactive way to study natural language processing.
Approach:	They propose a two-phase training framework to decouple language learning from reinforcement learning and improve the sample efficiency.
Outcome:	The proposed method significantly improves performance and sample efficiency against compound error and limited pre-training data.

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Quanwei Tang, Sophia Yat Mei Lee, Junshuang Wu, Dong Zhang, Shoushan Li, Erik Cambria, Guodong Zhou

MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark (2024.findings-acl)

Challenge:	Existing studies struggle with achieving global understanding of large language models . GraphMPA is a graph-based framework with mode-seeking preference alignment .
Approach:	They propose a graph-based framework with mode-seeking preference alignment to improve model outputs.
Outcome:	The proposed framework constructs a hierarchical document graph mimicking human cognitive processes for information understanding and synthesis.

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Hongwei Liu, Zilong Zheng, Yuxuan Qiao, Haodong Duan, Zhiwei Fei, Fengzhe Zhou, Wenwei Zhang, Songyang Zhang, Dahua Lin, Kai Chen

Numerical Reasoning

Challenge:	Recent advances in large language models have showcased significant improvements in mathematics, but traditional benchmarks like GSM8k offer a unidimensional perspective.
Approach:	MathBench is a benchmark that rigorously assesses the mathematical capabilities of large language models.
Outcome:	MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills.

SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation (2025.acl-long)

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Jialong Wu, Zhenglin Wang, Linhai Zhang, Yilong Lai, Yulan He, Deyu Zhou

Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models (2023.findings-acl)

Challenge:	Excessive compression during the prefill phase impairs comprehension of reasoning tasks . SCOPE is a framework that performs KV cache optimization during the decoding and prefill phases .
Approach:	They propose a framework that performs optimization during the prefill and decoding phases . they propose enabling a sliding strategy to select essential heavy hitters for the decoding phase .
Outcome:	Experiments show that SCOPE can optimize key-value cache for long-context generation tasks . the framework can preserve essential information while minimizing memory usage and transfer .

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Yuhui Zhang, Michihiro Yasunaga, Zhengping Zhou, Jeff Z. HaoChen, James Zou, Percy Liang, Serena Yeung

MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time (2025.findings-naacl)

Challenge:	Recent studies show that some tasks exhibit inverse scaling, or U-shaped scaling, where the performance degrades as models are scaled up.
Approach:	They propose a task that asks questions with negation to show positive scaling . they hypothesize that solving NeQA depends on question answering and negation understanding .
Outcome:	The proposed task can exhibit inverse scaling, U-shaped scaling, or positive scaling, and the scaling trends shift as the task is more powerful.

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Mozhi Zhang, Pengyu Wang, Chenkun Tan, Mianqiu Huang, Dong Zhang, Yaqian Zhou, Xipeng Qiu

Selecting Stickers in Open-Domain Dialogue through Multitask Learning (2022.findings-acl)

Challenge:	Existing methods to align large language models with human preferences often result in a static alignment that cannot account for the diversity of human preferences in practical applications.
Approach:	They propose a method to help large language models dynamically align with various explicit or implicit preferences specified at inference time.
Outcome:	The proposed method can help LLMs dynamically align with various explicit or implicit preferences specified at the inference stage, validating the feasibility of MetaAlign.

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Zhexin Zhang, Yeshuang Zhu, Zhengcong Fei, Jinchao Zhang, Jie Zhou

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

Challenge:	Existing methods to select appropriate stickers in open-domain dialogues have not been explored.
Approach:	They propose a multitask learning method consisting of three auxiliary tasks to combine multimodal information to enhance the understanding of dialogue history, emotion and semantic meaning of stickers.
Outcome:	The proposed model can combine multimodal information and achieve significantly higher accuracy over strong baselines.

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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo

Beyond Static Artifacts: An Evolutionary Framework for Synthetic Claim Generation (2026.acl-long)

Challenge:	Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task.
Approach:	They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5.
Outcome:	The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers.

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Yeqing Teng, Jiasheng Si, Shuxia Lin, Linhai Zhang, Weiyu Zhang, Wenpeng Lu, Deyu Zhou, Xiaoming Wu

Challenge:	Existing claim detection benchmarks treat claims as static textual artifacts . current research ignores sociological etiology of how information naturally emerges and mutates .
Approach:	They propose a socially generative framework for synthetic claim generation . they propose utterance, proposition and context-based simulations to capture truth decay .
Outcome:	The proposed paradigm models claims as socially evolving entities . it allows precise simulation of truth decay and intervened propagation with multi-auditor oversight .

WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines (2025.naacl-long)

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Genta Indra Winata, Frederikus Hudi, Patrick Amadeus Irawan, David Anugraha, Rifki Afina Putri, Wang Yutong, Adam Nohejl, Ubaidillah Ariq Prathama, Nedjma Ousidhoum, Afifa Amriani, Anar Rzayev, Anirban Das, Ashmari Pramodya, Aulia Adila, Bryan Wilie, Candy Olivia Mawalim, Cheng Ching Lam, Daud Abolade, Emmanuele Chersoni, Enrico Santus, Fariz Ikhwantri, Garry Kuwanto, Hanyang Zhao, Haryo Akbarianto Wibowo, Holy Lovenia, Jan Christian Blaise Cruz, Jan Wira Gotama Putra, Junho Myung, Lucky Susanto, Maria Angelica Riera Machin, Marina Zhukova, Michael Anugraha, Muhammad Farid Adilazuarda, Natasha Christabelle Santosa, Peerat Limkonchotiwat, Raj Dabre, Rio Alexander Audino, Samuel Cahyawijaya, Shi-Xiong Zhang, Stephanie Yulia Salim, Yi Zhou, Yinxuan Gui, David Ifeoluwa Adelani, En-Shiun Annie Lee, Shogo Okada, Ayu Purwarianti, Alham Fikri Aji, Taro Watanabe, Derry Tanti Wijaya, Alice Oh, Chong-Wah Ngo

Augmenting Multi-Agent Communication with State Delta Trajectory (2025.emnlp-main)

Challenge:	Vision Language Models struggle with cultural-specific knowledge, especially in languages other than English and in underrepresented cultural contexts.
Approach:	They propose a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects and a training dataset.
Outcome:	The proposed model performs better with correct location context, but struggles with adversarial contexts and predicting specific regional cuisines and languages.

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Yichen Tang, Weihang Su, Yujia Zhou, Yiqun Liu, Min Zhang, Shaoping Ma, Qingyao Ai

Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)

Challenge:	Multi-agent systems based on large language models (LLMs) have shown to be effective in downstream tasks.
Approach:	They propose a protocol that transfers both natural language tokens and token-wise state transition trajectory from one agent to another.
Outcome:	The proposed protocol can transfer both natural language tokens and token-wise state transition trajectory from one agent to another.

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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang, Wentao Zhang, Zhiqiang Gao

Challenge:	Existing information-seeking (IS) agents rely on the web for their information acquisition.
Approach:	They propose a browser-action framework that decouples interaction control from page exploration through a nested structure.
Outcome:	Empirical results show that NestBrowse offers clear benefits in practice.

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021.acl-demo)

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Weizhen Qi, Yeyun Gong, Yu Yan, Can Xu, Bolun Yao, Bartuer Zhou, Biao Cheng, Daxin Jiang, Jiusheng Chen, Ruofei Zhang, Houqiang Li, Nan Duan

Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration (2023.emnlp-main)

Challenge:	Existing models for pre-training are not convenient for users to find and set them up.
Approach:	They propose to extend ProphetNet into other domains and languages by pre-training models . they pre-train a cross-lingual generation model ProphetNet-Multi and a Chinese generation model .
Outcome:	The proposed models achieve new state-of-the-art on 10 benchmarks.

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Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang

TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data (2022.emnlp-main)

Challenge:	Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
Approach:	They propose a framework that leverages the strength of both LLMs and domain-specific models in the context of precedents.
Outcome:	The proposed framework leverages the strength of both LLM and domain models in the context of precedents.

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Fan Zhou, Mengkang Hu, Haoyu Dong, Zhoujun Cheng, Fan Cheng, Shi Han, Dongmei Zhang

mDPO: Conditional Preference Optimization for Multimodal Large Language Models (2024.emnlp-main)

Challenge:	Existing auto-regressive pre-trained language models are challenged by recent emerging numerical reasoning datasets due to the error-prone implicit calculation.
Approach:	They propose a pre-computation tool to pre-compute aggregation/arithmetic results for the table in advance, so they are handy and readily available for PLMs to answer numerical reasoning questions.
Outcome:	The proposed model improves on TAT-QA and T5 and BART-large on multiple benchmarks.

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

DeepRTL2: A Versatile Model for RTL-Related Tasks (2025.findings-acl)

Challenge:	Recent studies have attempted to apply DPO to multimodal scenarios but have found it challenging to achieve consistent improvement.
Approach:	They propose a multimodal DPO objective that prevents the over-prioritization of language-only preferences by also optimizing image preference.
Outcome:	The proposed method significantly improves performance on two multimodal LLMs of different sizes and three widely used benchmarks.

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Yi Liu, Hongji Zhang, Yunhao Zhou, Zhengyuan Shi, Changran Xu, Qiang Xu

A Generative Framework for Personalized Sticker Retrieval (2025.findings-emnlp)

Challenge:	Integration of large language models into electronic design automation has been a key driver in eDA.
Approach:	They propose a family of large language models that unifies generation- and embedding-based tasks related to RTL.
Outcome:	The proposed model achieves state-of-the-art performance across all evaluated tasks.

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Changjiang Zhou, Ruqing Zhang, Jiafeng Guo, Yu-An Liu, Fan Zhang, Ganyuan Luo, Xueqi Cheng

Reward Modeling Requires Automatic Adjustment Based on Data Quality (2024.findings-emnlp)

Challenge:	Existing relevance-based generative retrieval methods lack personalization, leading to a mismatch between diverse user expectations and the retrieved results.
Approach:	They propose a representation learning model that learns discriminative user representations to encode user-specific sticker preferences.
Outcome:	The proposed framework outperforms state-of-the-art methods in generating relevant stickers for queries.

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Binghai Wang, Rui Zheng, Lu Chen, Zhiheng Xi, Wei Shen, Yuhao Zhou, Dong Yan, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is a method for aligning language models with human values.
Approach:	They propose a method that automatically adjusts reward modeling based on data quality . they use preference data to train a reward model that is more aligned with human values .
Outcome:	The proposed method stabilizes reward model training and significantly improves alignment performance on human preference datasets.

Improving Model Factuality with Fine-grained Critique-based Evaluator (2025.acl-long)

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Yiqing Xie, Wenxuan Zhou, Pradyot Prakash, Di Jin, Yuning Mao, Quintin Fettes, Arya Talebzadeh, Sinong Wang, Han Fang, Carolyn Rose, Daniel Fried, Hejia Zhang

DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains (2025.findings-emnlp)

Challenge:	Factuality evaluation aims to detect factual errors produced by language models and guide the development of more factual models.
Approach:	They propose a framework that leverages FenCE to improve the factuality of LM generators by constructing training data.
Outcome:	The proposed framework improves the factuality of LM generators by enhancing their training data.

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Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang

The Lessons of Developing Process Reward Models in Mathematical Reasoning (2025.findings-acl)

Challenge:	Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs . current approaches encode graph context in textual form, which fails to exploit its potential .
Approach:	a new method is proposed to predict missing triples in knowledge graphs by leveraging existing triples and textual information.
Outcome:	The proposed model learns structural embeddings and logical rules within the KG and extracts a subgraph for each query guided by the learned rules.

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Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin

MedDialog: Large-scale Medical Dialogue Datasets (2020.emnlp-main)

Challenge:	a recent study shows that process reward models can make mistakes, leading to wrong conclusions.
Approach:	They propose a consensus filtering mechanism that integrates MC estimation with LLM-as-a-judge to improve model performance and data efficiency.
Outcome:	The proposed model outperforms existing open-source alternatives and provides practical guidelines for future research.

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Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie

Multi 3 WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems (2023.tacl-1)

Challenge:	telemedicine is a medical practice that provides patient care remotely using video conferencing tools.
Approach:	They build large-scale medical dialogue datasets to facilitate research . they pretrain several models on the Chinese MedDialog dataset and compare their performance .
Outcome:	The proposed datasets show that models trained on MedDialog can generate doctor-like medical dialogues.

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Songbo Hu, Han Zhou, Mete Hergul, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Ivan Vulić, Anna Korhonen

Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)

Challenge:	Task-oriented dialog (TOD) is one of the central objectives, hallmarks, and applications of machine intelligence.
Approach:	They propose a multilingual, multi-domain, multiparallele ToD dataset that offers culturally adapted dialogs in 4 languages for training and evaluation of multilingual and cross-lingual systems.
Outcome:	The proposed dataset is large-scale and culturally adapted to enable training and evaluation of multilingual and cross-lingual ToD systems.

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Wei Zhao, Jianbo Ye, Min Yang, Zeyang Lei, Suofei Zhang, Zhou Zhao

Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompts (2023.findings-emnlp)

Challenge:	Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts .
Approach:	They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules.
Outcome:	The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods .

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Gangwei Jiang, Caigao Jiang, Siqiao Xue, James Zhang, Jun Zhou, Defu Lian, Ying Wei

FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)

Challenge:	Continual pre-training has been used for a multitude of domains and tasks . a continually pre-trained model can show a non-decreasing performance on unseen domains .
Approach:	They propose a method that generates domain-specific prompts by agreement and disagreement losses.
Outcome:	The proposed method achieves improvements of 3.57% and 3.4% on two real-world datasets.

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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie

Challenge:	FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions .
Approach:	They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures .
Outcome:	The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes.

Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation (2021.findings-acl)

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Peng Wang, Junyang Lin, An Yang, Chang Zhou, Yichang Zhang, Jingren Zhou, Hongxia Yang

Emotion Detection with Neural Personal Discrimination (D19-1)

Challenge:	Existing methods for table-to-text generation suffer from poor faithfulness and low coverage.
Approach:	They propose a method that combines Autoregressive and Non-Autoregressive generation to generate a table-to-text from a key-value table using a skeleton and an edit-based non-autoregressively generation model.
Outcome:	The proposed method outperforms the existing methods on WikiPerson and WikiBio datasets on coverage and faithfulness.

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Xiabing Zhou, Zhongqing Wang, Shoushan Li, Guodong Zhou, Min Zhang

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

Challenge:	Existing approaches to automatically predict the emotions of posts consider each post individually and predict their emotions independently.
Approach:	They propose a Neural Personal Discrimination approach to identify personal attributes from posts and connect relevant posts with similar attributes to jointly learn their emotions.
Outcome:	The proposed approach improves on existing models by capturing attributes-aware words and predicting emotions among relevant posts.

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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances (2021.acl-long)

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

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Zekang Li, Jinchao Zhang, Zhengcong Fei, Yang Feng, Jie Zhou

You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations (2026.findings-acl)

Challenge:	Recent intelligent open-domain chatbots have made substantial progress thanks to the rapid development of large-scale pre-training approaches.
Approach:	They propose a dynamic flow mechanism to model the context flow and a model to capture the information dynamics across dialogue utterances.
Outcome:	The proposed model outperforms the DialoGPT on the dialogue generation task.

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Hongbin Na, Zimu Wang, Zhaoming Chen, Peilin Zhou, Yining Hua, Grace Ziqi Zhou, Haiyang Zhang, Tao Shen, Wei Wang, John Torous, Shaoxiong Ji, Ling Chen

ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models (2023.emnlp-demo)

Challenge:	Psychological defenses are strategies people use to manage distress.
Approach:	They propose a dialogue corpus with help seeker utterances labeled for defense level and a DMRS Co-Pilot pipeline that provides evidence-based pre-annotations.
Outcome:	The proposed framework reduces annotation time by 24.0% in a counterbalanced study.

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Chenliang Li, He Chen, Ming Yan, Weizhou Shen, Haiyang Xu, Zhikai Wu, Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi, Ji Zhang, Fei Huang, Jingren Zhou

Unsupervised Context Rewriting for Open Domain Conversation (D19-1)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
Approach:	They propose a framework that equips large language models with tool-use capabilities . they propose LLaMA and Chat-GLM as controllers, and a model-based agent framework .
Outcome:	The proposed framework equips open-source LLMs with tool-use capabilities . it provides a user-friendly system library with a customizable engine design .

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Kun Zhou, Kai Zhang, Yu Wu, Shujie Liu, Jingsong Yu

TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation (2026.findings-acl)

Challenge:	Existing approaches to model conversation context have drawbacks, such as lack of coreferences and long dependency.
Approach:	They propose a context rewriting method which explicitly rewrites the last utterance by considering context history.
Outcome:	The proposed method outperforms baselines in terms of rewriting quality, multi-turn response generation, and end-to-end retrieval-based chatbots.

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Bangde Du, Minghao Guo, Songming He, Ziyi Ye, Xi Zhu, Weihang Su, Shuqi Zhu, Yujia Zhou, Yongfeng Zhang, Qingyao Ai, Yiqun Liu

Human or LLM as Standardized Patients? A Comparative Study in Medical Education (2026.acl-long)

Challenge:	Existing studies show that advanced LLMs produce text indistinguishable from human writing.
Approach:	They propose a benchmark to assess persona simulation across diverse contexts by decomposing the evaluation into six fundamental capabilities including opinion consistency, memory recall, logical reasoning, persona tone, and syntactic style.
Outcome:	The proposed model achieves moderate accuracy but falls short of the basic capabilities needed to simulate personas in real-world contexts.

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Bingquan Zhang, Xiaoxiao Liu, Yuchi Wang, Zhou Lei, Qianqian Xie, Benyou Wang

TrojanSQL: SQL Injection against Natural Language Interface to Database (2023.emnlp-main)

Challenge:	Standardized patients (VSPs) are indispensable for clinical skills training but remain expensive and difficult to scale.
Approach:	They propose a multi-agent VSP framework that separates case-grounded information disclosure from response generation to support stable, inquiry-conditioned patient behavior.
Outcome:	The proposed framework more closely matches human SP behavior than existing VSPs, particularly in case consistency and controlled disclosure.

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

ALLSH: Active Learning Guided by Local Sensitivity and Hardness (2022.findings-naacl)

Challenge:	Existing studies on text-to-SQL systems have not investigated its security aspects . however, how to implement such attacks remains an open question.
Approach:	They propose a backdoor-based SQL injection framework for text-to-SQL systems that uses boolean-based injection and union-based injecting techniques to exploit SQL injection vulnerabilities.
Outcome:	The proposed framework can produce harmful SQL statements invalidating user queries or compromise sensitive information about the database.

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Shujian Zhang, Chengyue Gong, Xingchao Liu, Pengcheng He, Weizhu Chen, Mingyuan Zhou

SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe (2026.acl-long)

Challenge:	Existing studies show that labeling in crowdsourcing annotations is not an annotation artifact but rather a core linguistic phenomenon.
Approach:	They propose to retrieve unlabeled data with a local sensitivity and hardness-aware acquisition function.
Outcome:	The proposed method achieves consistent gains over the commonly used active learning strategies in various classification tasks.

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Yuxin Xiao, Shujian Zhang, Marzyeh Ghassemi, Wenxuan Zhou

WeTS: A Benchmark for Translation Suggestion (2022.emnlp-main)

Challenge:	Efforts to improve instruction tuning often focus on higher-quality supervised fine-tuning datasets, typically requiring data filtering with proprietary LLMs or human annotation.
Approach:	They propose a Mixup-based recipe that elevates LLM instruction tuning without relying on well-curated datasets.
Outcome:	The proposed model improves instruction-following and healthcare-specific tasks with consistent improvements across LLM families and SFT datasets.

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Zhen Yang, Fandong Meng, Yingxue Zhang, Ernan Li, Jie Zhou

A Query-Response Framework for Whole-Page Complex-Layout Document Image Translation with Relevant Regional Concentration (2025.findings-acl)

Challenge:	Existing studies focus on overall performance of machine translation but ignore TS performance, authors say . if TS is applied into post-editing, it will reduce the time and cost of post-production.
Approach:	They propose to use a golden corpus annotated by experts to generate a translation suggestion model.
Outcome:	The proposed model improves on the golden corpus annotated by translators on four translation directions.

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Zhiyang Zhang, Yaping Zhang, Yupu Liang, Zhiyuan Chen, Lu Xiang, Yang Zhao, Yu Zhou, Chengqing Zong

Decoupled Proxy Alignment: Mitigating Language Prior Conflict for Multimodal Alignment in MLLMs (2025.findings-emnlp)

Challenge:	Existing methods for document image translation rely on the vanilla encoder-decoder paradigm . a novel dynamic aggregation mechanism is designed to enhance the text semantics in query features toward translation.
Approach:	They propose a Query-Response DIT framework that reformulates the DIT task into a parallel response/translation process of multiple queries.
Outcome:	The proposed framework improves translation quality on four translation directions on three benchmarks.

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Chenkun Tan, Pengyu Wang, Shaojun Zhou, Botian Jiang, Zhaowei Li, Dong Zhang, Xinghao Wang, Yaqian Zhou, Xipeng Qiu

Sugar-Coated Poison: Benign Generation Unlocks Jailbreaking (2025.findings-emnlp)

Challenge:	Recent advances in multimodal large language models focus on improving performance . however, language prior conflict leads to suboptimal vision-language alignment .
Approach:	They propose a method to decouple the alignment process from language prior interference . they use a proxy LLM to detach from language interference during pretraining .
Outcome:	The proposed method improves training performance and generalizes training data.

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Yuhang Wu, Yu-Jie Xiong, Hao Zhang, Jia-Chen Zhang, Zheng Zhou

TopViewRS: Vision-Language Models as Top-View Spatial Reasoners (2024.emnlp-main)

Challenge:	Existing methods to jailbreak large language models rely on black-box manipulation of prompt templates, resulting in high costs and poor generalizability.
Approach:	They propose a sugar-coated poison attack paradigm that uses a "semantic reversal" strategy to induce the model into a safety response mode.
Outcome:	The proposed attack paradigm outperforms baselines in the study.

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Chengzu Li, Caiqi Zhang, Han Zhou, Nigel Collier, Anna Korhonen, Ivan Vulić

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

Challenge:	Top-view perspective is a typical way in which humans read and reason over different types of maps, but spatial reasoning capabilities of modern VLMs in this setup remain unattested and underexplored.
Approach:	They introduce a top-view spatial reasoning dataset and use it to evaluate VLMs across 4 perception and reasoning tasks with different levels of complexity.
Outcome:	The proposed model can understand and reason over spatial relations from the top view and can be controlled at different granularities of spatial reasoning.

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

Causal Reasoning of Entities and Events in Procedural Texts (2023.findings-eacl)

Challenge:	Recent advances in Large Language Models have opened new avenues for solving complex optimization problems, including Neural Architecture Search (NAS).
Approach:	They propose a framework that leverages LLMs for cross-domain neural architecture optimization without extensive domain-specific tuning.
Outcome:	The proposed framework achieves competitive performance in both in-domain and out-of-domain tasks.

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Li Zhang, Hainiu Xu, Yue Yang, Shuyan Zhou, Weiqiu You, Manni Arora, Chris Callison-Burch

MAVIS: Multi-Agent Video Retrieval via Structured Video Understanding (2026.findings-acl)

Challenge:	Existing work on entity state tracking or event reasoning is limited to procedural texts.
Approach:	They propose a benchmark for causal reasoning of event plausibility and entity states . they represent entities as programming languages while prompting language models .
Outcome:	The proposed model outperforms existing models on human reasoning and event reasoning.

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Jie Zhang, Qilang Ye, Hao Zhou, Haochen Liang, Fei Luo

ALW: Adaptive Layer-Wise contrastive decoding enhancing reasoning ability in Large Language Models (2025.findings-acl)

Challenge:	Existing methods for video retrieval rely on embedding-based full-corpus scanning, but there is a bottleneck in semantic asymmetry and computational redundancy.
Approach:	They propose a multi-agent framework that rethinks retrieval as cooperative reasoning . they parse raw videos into a structured semantic library, enabling explicit attribute-level indexing .
Outcome:	The proposed framework bridges the granularity mismatch gap by parsing raw videos into a structured semantic library . it employs a Logic-aware Debate mechanism with a strict veto protocol . the proposed framework achieves competitive performance without task-specific fine-tuning .

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Yuechi Zhou, Chuyue Zhou, Jianxin Zhang, Juntao Li, Min Zhang

Aspect Sentiment Classification with Document-level Sentiment Preference Modeling (2020.acl-main)

Challenge:	Existing research has demonstrated that contrast decoding of two different models can improve text quality in open-ended text generation but with limited gains on reasoning tasks.
Approach:	They propose a framework that dynamically disentangles noise in shallow layers from critical signals in deep layers to enhance reasoning ability.
Outcome:	The proposed framework improves answer accuracy while maintaining inference efficiency.

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Xiao Chen, Changlong Sun, Jingjing Wang, Shoushan Li, Luo Si, Min Zhang, Guodong Zhou

Danger Depends on the Mind: A Theory-of-Mind Grounded Dataset and Model for Context-Dependent Dangerous Speech (2026.findings-acl)

Challenge:	Existing studies consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect.
Approach:	They propose a Cooperative Graph Attention Networks approach for cooperatively learning aspect-related sentence representation.
Outcome:	The proposed approach outperforms the state-of-the-art methods in document-level sentiment classification.

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Yuanchen Shi, Longyin Zhang, Guodong Zhou, Fang Kong

Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding (2022.coling-1)

Challenge:	Existing methods for dangerous speech detection rely on binary labels that ignore who is speaking and in what mental state.
Approach:	They propose a context-dependent variant of dangerous speech detection by grounding it in Theory-of-Mind.
Outcome:	The proposed model outperforms proprietary and open-source models with significantly fewer parameters.

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Linhai Zhang, Deyu Zhou

You Only Query Twice: Multimodal Rumor Detection via Evidential Evaluation from Dual Perspectives (2025.coling-main)

Challenge:	Existing TKGC methods are based on deterministic vector embeddings, which are not flexible and expressive enough.
Approach:	They propose a method that maps entities and relations to multivariate Gaussian processes by mapping global trends and local fluctuations in TKGs.
Outcome:	The proposed method can predict global trends and local fluctuations in the TKGs and can be optimized on two real-world benchmark datasets.

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Junyi Chen, Leyuan Liu, Tian Lan, Fan Zhou, Xiaosong Zhang

Reinforcement Learning for Large Language Models via Group Preference Reward Shaping (2025.emnlp-main)

Challenge:	Existing rumor detectors exhibit limitations in fully exploiting responses to the source tweet as essential public opinions, and in explaining and indicating the reliability of the results obtained. Existing research mainly combats this with content and response-based detection methods.
Approach:	They propose a Large Language Model with both multimodal source content and the corresponding response set to extract contrasting evidence to enable maximal utilization of informative responses.
Outcome:	The proposed approach can indicate the model’s uncertainty (i.e., reliability) of the results.

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Huaisheng Zhu, Siyuan Xu, Hangfan Zhang, Teng Xiao, Zhimeng Guo, Shijie Zhou, Shuyue Hu, Vasant G. Honavar

Jailbreak Large Vision-Language Models Through Multi-Modal Linkage (2025.acl-long)

Challenge:	Existing methods for fine-tuning Large Language Models (LLMs) are expensive and sensitive to reward model quality.
Approach:	They propose a method that leverages preference-based comparisons rather than precise numerical rewards.
Outcome:	Experiments show that GPRS outperforms critic-model-free RL algorithms on RLHF and reasoning tasks.

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Yu Wang, Xiaofei Zhou, Yichen Wang, Geyuan Zhang, Tianxing He

Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework (2026.findings-acl)

Challenge:	Existing methods to jailbreak large vision-language models fail against cutting-edge models such as GPT-4o, despite having undergone safety alignment training.
Approach:	They propose a new framework for jailbreaking large vision-language models that uses an encryption-decryption process to mitigate the over-exposure of harmful information.
Outcome:	The proposed framework jailbreaks GPT-4o with 99.40% success rates on SafeBench, 98.81% on MM-SafeBench and 99.07% on HADES-Dataset.

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Jiaqi Weng, Han Zheng, Hanyu Zhang, Ej Zhou, Qinqin He, Jialing Tao, Hui Xue, Zhixuan Chu, Xiting Wang

A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models (2024.emnlp-main)

Challenge:	Existing studies on how SAEs derive most fine-grained latent features for safety remain unexplored.
Approach:	They propose a framework for interpreting SAE features in safety-critical domains . they train a suite of SAEs with human-readable explanations and systematic evaluations based on pornography, politics, violence, and terror .
Outcome:	The proposed framework reduces interpretation cost by 55% and improves safety-critical features.

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Houquan Zhou, Zhenghua Li, Bo Zhang, Chen Li, Shaopeng Lai, Ji Zhang, Fei Huang, Min Zhang

KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)

Challenge:	Using an LLM for Chinese spelling correction tasks is completely different from previous approaches . given a Chinese character, there may exist many others with the same or similar pronunciations, or with similar shapes.
Approach:	They propose a training-free prompt-free approach to leverage large language models for Chinese spelling correction task.
Outcome:	The proposed model significantly improves performance on five public datasets, enabling them to compete with state-of-the-art domain-general CSC models.

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Jun Zhang, Yan Yang, Chencai Chen, Liang He, Zhou Yu

Context-Sensitive Generation of Open-Domain Conversational Responses (C18-1)

Challenge:	Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity.
Approach:	They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process.
Outcome:	The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation.

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Weinan Zhang, Yiming Cui, Yifa Wang, Qingfu Zhu, Lingzhi Li, Lianqiang Zhou, Ting Liu

Paper Abstract Writing through Editing Mechanism (P18-2)

Challenge:	Existing studies on single-turn conversation generation focus on coherence and context-sensitive generation of open-domain conversational responses.
Approach:	They propose static and dynamic attention based approaches for context-sensitive generation of open-domain conversational responses.
Outcome:	The proposed model outperforms all baselines on automatic and human evaluation on two public datasets.

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Qingyun Wang, Zhihao Zhou, Lifu Huang, Spencer Whitehead, Boliang Zhang, Heng Ji, Kevin Knight

Challenge:	a paper abstract writing system can automatically generate an abstract from a title . a typical recurrent neural network (RNN) based approach easily loses focus.
Approach:	They propose a paper abstract writing system that automatically generates an abstract from a title.
Outcome:	The proposed system passes Turing tests by junior domain experts and non-experts at a rate up to 80%.

Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective (2025.findings-emnlp)

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Zhihao Zhang, Sophia Yat Mei Lee, Dong Zhang, Shoushan Li, Guodong Zhou

Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory (2026.acl-long)

Challenge:	Existing approaches to cross-lingual Named Entity Recognition focus on Latin script language (LSL) for non-Latin script language, performance often degrades due to deep structural differences.
Approach:	They propose an entity-aligned translation approach to align entities between NSL and English .
Outcome:	The proposed approach aims to transfer knowledge from high-resource languages to low-resourced languages.

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Zihao Tang, Xin Yu, Ziyu Xiao, Zengxuan Wen, Zelin Li, Jiaxi Zhou, Hualei Wang, Haohua Wang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang

Reducing Token Redundancy in LVLMs: A Systematic Review of Token Pruning Methods (2026.acl-long)

Challenge:	Existing methods for retrieving historical messages are based on similarity-based mechanisms.
Approach:	They propose a system that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection.
Outcome:	The proposed framework achieves state-of-the-art on long-term memory benchmarks and 93.9 on LoCoMo and 91.6 on LongMemEval-S.

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Hanzhang Yuan, Mengxuan Hu, Wenhao Zhang, Tianlong Wang, Zhongliang Zhou, Jiasen Lu, Sheng Li

Self-Paced Learning for Neural Machine Translation (2020.emnlp-main)

Challenge:	Large Vision-Language Models (LVLMs) excel at visual understanding but face severe computational bottlenecks when processing high-resolution images and long videos due to massive visual token counts.
Approach:	They propose a taxonomy categorizing methods into vision-side, LLM-side and hybrid paradigms and analyze token selection mechanisms and pruning strategy.
Outcome:	The proposed method selectively removes less informative tokens while maintaining performance.

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Yu Wan, Baosong Yang, Derek F. Wong, Yikai Zhou, Lidia S. Chao, Haibo Zhang, Boxing Chen

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)

Challenge:	Existing studies have shown that the training of neural machine translation (NMT) rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word rarity.
Approach:	They propose to train NMT model using a self-paced learning approach that allows it to quantify the learning confidence over training examples and flexibly govern its learning via regulating the loss in each iteration step.
Outcome:	The proposed model outperforms baseline models and those trained with human-designed curricula on translation quality and convergence speed.

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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

Revisit Self-Debugging with Self-Generated Tests for Code Generation (2025.acl-long)

Challenge:	Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work.
Approach:	They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations.
Outcome:	The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work.

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Xiancai Chen, Zhengwei Tao, Kechi Zhang, Changzhi Zhou, Xinyu Zhang, Wanli Gu, Yuanpeng He, Mengdi Zhang, Xunliang Cai, Haiyan Zhao, Zhi Jin

Plug-and-Play Knowledge Injection for Pre-trained Language Models (2023.acl-long)

Challenge:	Large language models (LLMs) have made significant advances in code generation, but they still face challenges when tackling complex programming tasks beyond their basic capabilities.
Approach:	They propose to integrate self-generated tests into the code generation process . they propose to use post-execution and in-exection self-debugging to mitigate test bias .
Outcome:	The proposed method improves the performance of large language models in code generation tasks by leveraging execution feedback from tests.

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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Huadong Wang, Deming Ye, Chaojun Xiao, Xu Han, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Learning Logic Rules for Document-Level Relation Extraction (2021.emnlp-main)

Challenge:	Existing knowledge injection methods are not suitable for enhancing pre-trained language models with external knowledge bases.
Approach:	They propose a plug-and-play knowledge injection method where knowledge bases are injected into frozen existing downstream models by a knowledge plugin.
Outcome:	The proposed method improves the performance of knowledge injection on knowledge-driven tasks while keeping model parameters frozen.

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Dongyu Ru, Changzhi Sun, Jiangtao Feng, Lin Qiu, Hao Zhou, Weinan Zhang, Yong Yu, Lei Li

Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging (2022.findings-acl)

Challenge:	Existing models for document-level relation extraction relied on implicitly powerful representations, which makes the model less transparent.
Approach:	They propose a probabilistic model for document-level relation extraction by learning logic rules.
Outcome:	The proposed model outperforms baseline models in relation performance and logical consistency.

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Houquan Zhou, Yang Li, Zhenghua Li, Min Zhang

Advancing SMoE for Continuous Domain Adaptation of MLLMs: Adaptive Router and Domain-Specific Loss (2025.acl-long)

Challenge:	Large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks, but they fail to achieve state-of-the-art (SOTA) performance.
Approach:	They propose a Guassian HMM variant for unsupervised POS tagging that incorporates contexualized word representations into the decoder.
Outcome:	The proposed model outperforms state-of-the-art models on Penn Treebank and multilingual Universal Dependencies treebank v2.0.

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Liang Zhang, Ziyao Lu, Fandong Meng, Hui Li, Jie Zhou, Jinsong Su

Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues (2022.emnlp-main)

Challenge:	Recent studies have explored Continual Instruction Tuning (CIT) in Multimodal Large Language Models (MLLMs), with a primary focus on Task-incremental CIT, where MLLM are required to continuously acquire new tasks.
Approach:	They propose a Sparse Mixture of Expert (SMoE) based method for domain-incremental CIT in Multimodal Large Language Models (MLLMs) . they equip the SMoA module with a domain-specific autoregressive loss (DSAL) they establish a new benchmark to evaluate the efficacy of their method .
Outcome:	The proposed method outperforms all baselines and is based on a Sparse Mixture of Experts (SMoE) module .

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Jiao Ou, Jinchao Zhang, Yang Feng, Jie Zhou

Sign2Vis: Automated Data Visualization from Sign Language (2025.findings-acl)

Challenge:	Existing methods to augment dialogue datasets are labor-intensive and time-consuming . Currently, smoking is harmful for your health.
Approach:	They propose a data augmentation method to augment dialogue responses with different semantics by counterfactual inference.
Outcome:	The proposed method outperforms baselines on multiple downstream tasks.

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Yao Wan, Yang Wu, Zhen Li, Guobiao Zhang, Hongyu Zhang, Zhou Zhao, Hai Jin, April Wang

Challenge:	Existing methods to translate natural language descriptions into visualization queries focus on spoken languages, not sign languages.
Approach:	They propose a sign language interface that enables the DHH community to engage more fully with data analysis.
Outcome:	The proposed interface can be used by the deaf and hard-of-hearing community.

The Stochastic Parrot on LLM’s Shoulder: A Summative Assessment of Physical Concept Understanding (2025.naacl-long)

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Mo Yu, Lemao Liu, Junjie Wu, Tsz Ting Chung, Shunchi Zhang, Jiangnan Li, Dit-Yan Yeung, Jie Zhou

Challenge:	Recent years have witnessed remarkable advancements in large language models (LLMs) many researchers argue that LLMs may not * Equal contribution.
Approach:	They propose a task that summarises the memorization issue by using grid inputs that abstractly describe physical phenomena.
Outcome:	The proposed task alleviates the memorization issue by using grid-format inputs that abstractly describe physical phenomena.

Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting (2022.naacl-main)

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Linzhi Wu, Pengjun Xie, Jie Zhou, Meishan Zhang, Ma Chunping, Guangwei Xu, Min Zhang

Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching (2025.findings-acl)

Challenge:	Prior research has focused on reducing noise for specific methods to achieve an effective integration.
Approach:	They propose to use token substitution and mixup to improve named entity recognition (NER) using a meta-reweighting strategy, which is extensible and requires little effort.
Outcome:	The proposed method is extensible, imposing little effort on a specific self-augmentation method.

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Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou, Yipeng Zhang, Haitao Mi, Helen M. Meng

V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)

Challenge:	Existing approaches to keeping large language models current involve continued pre-training on new documents.
Approach:	They propose a learning framework that augments documents with knowledge-intensive tasks created in a self-supervised manner, focusing on memorization, comprehension, and self-reflection.
Outcome:	The proposed learning framework improves an LLM’s ability to acquire new knowledge from unseen raw documents through self-teaching.

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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou

Pinpointing Diffusion Grid Noise to Enhance Aspect Sentiment Quad Prediction (2024.findings-acl)

Challenge:	Existing code-related benchmarks focus on single modality rather than visual game development.
Approach:	They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis.
Outcome:	The proposed framework assesses code generation and visual game generation using a sandbox environment.

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Linan Zhu, Xiangfan Chen, Xiaolei Guo, Chenwei Zhang, Zhechao Zhu, Zehai Zhou, Xiangjie Kong

Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs (2026.findings-acl)

Challenge:	Current studies on aspect-based sentiment analysis focus on essential content for model generation, ignoring the incorporation of various noise during training.
Approach:	They propose a grid noise-diffusion pinpoint network (GDP) model that incorporates three new modules to tackle generation instability.
Outcome:	The proposed model reduces the generation instability of model learning and outputs by incorporating Consistency Likelihood Learning and GDP-FOR.

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Linhao Zhang, Yuhan Song, Aiwei Liu, Chuhan Wu, Sijun Zhang, Wei Jia, Yuan Liu, Houfeng Wang, Zhou Xiao

WebWalker: Benchmarking LLMs in Web Traversal (2025.acl-long)

Challenge:	Recent Audio Large Language Models (AudioLLMs) excel at reasoning tasks, but struggle at elementary auditory perception.
Approach:	They propose a framework that organizes audio information into three explicit components in a unified JSON format.
Outcome:	The proposed framework boosts fine-grained perception by 10.9% on MMSU over state-of-the-art models while preserving robust reasoning capabilities.

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Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang

LongInsightBench: A Comprehensive Benchmark for Evaluating Omni-Modal Models on Human-Centric Long-Video Understanding. (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks.
Approach:	They propose a benchmark to assess the ability of LLMs to perform web traversal by using an explore-critic paradigm.
Outcome:	The proposed framework mimics human-like web navigation through an explore-critic paradigm and demonstrates the effectiveness of RAG combined with WebWalker in real-world scenarios.

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ZhaoYang Han, Qihan Lin, Hao Liang, Bowen Chen, Zhou Liu, Wentao Zhang

Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks (2021.emnlp-main)

Challenge:	LongInsightBench is the first benchmark designed to assess models’ ability to understand long videos, with a focus on human language, viewpoints, actions, and other contextual elements.
Approach:	They propose a benchmark to assess models’ ability to understand long videos with a focus on human language, viewpoints, actions, and other contextual elements.
Outcome:	The proposed model excels in three key areas: a) long-duration, human-centric videos; b) diversifying and challenging task scenarios; c) quality assurance pipeline; and d) reliability.

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Yao Qiu, Jinchao Zhang, Jie Zhou

Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning (2026.acl-long)

Challenge:	Pre-trained models can be fine-tuned on domain-specific unlabeled data . however, most further pre-training works just keep running the conventional pre- training task .
Approach:	They propose to add a further pre-training phase to the model to improve downstream tasks . they propose to use a domain-adaptive pre-tuning phase to fine-tune the models on unlabeled data .
Outcome:	The proposed method improves multiple task-oriented dialogue downstream tasks.

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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Kaiyu Huang, Yufeng Chen, Xu Jinan, Jie Zhou

Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)

Challenge:	Current Large Reasoning Models exhibit two critical limitations when processing non-English languages: (1) They struggle to maintain input-output language consistency; (2) They generally perform poorly with wrong reasoning paths and lower answer accuracy compared to English.
Approach:	They propose a language-consistency reward and a cross-lingual thinking alignment reward to improve the model's interpretability and accuracy.
Outcome:	The proposed model achieves nearly 100% language consistency and superior performance on two multilingual benchmarks (MMATH and PolyMath).

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Zhaochen Su, Juntao Li, Jun Zhang, Tong Zhu, Xiaoye Qu, Pan Zhou, Yan Bowen, Yu Cheng, Min Zhang

Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable (2021.findings-emnlp)

Challenge:	Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections.
Approach:	They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models.
Outcome:	The proposed benchmarks show that current LLMs struggle on CoTempQA tasks even when enhanced with Chain of Thought methodologies.

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Ruiliu Fu, Han Wang, Xuejun Zhang, Jun Zhou, Yonghong Yan

Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)

Challenge:	Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the reasoning process.
Approach:	They propose a three-stage framework based on complex question decomposition that decomposes the complex question, then reads the sub-questions and then performs numerical comparison to get the final answer.
Outcome:	The proposed framework achieves state-of-the-art in the 2WikiMultiHopQA dataset, with a winning joint F1 score of 53.58 on the leaderboard.

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Guanying Wang, Wen Zhang, Ruoxu Wang, Yalin Zhou, Xi Chen, Wei Zhang, Hai Zhu, Huajun Chen

TimeR4 : Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering (2024.emnlp-main)

Challenge:	Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities.
Approach:	They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier.
Outcome:	The proposed method performs well in the current distant supervision dataset.

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Xinying Qian, Ying Zhang, Yu Zhao, Baohang Zhou, Xuhui Sui, Li Zhang, Kehui Song

Robust Lottery Tickets for Pre-trained Language Models (2022.acl-long)

Challenge:	Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge in Temporal knowledge graphs (TKTs).
Approach:	They propose a Time-aware retrieve-rewrite-retrieve-rerank framework to integrate temporal knowledge from TKGs into Large Language Models (LLMs) to reduce temporal hallucination, they propose rewrite module to rew questions using background knowledge stored in TKG's, then implement a retrieve-rank module to retrieve semantically and temporally relevant facts from Tkgs and rerank them according to temporal constraints.
Outcome:	The proposed approach achieves relative gains of 47.8% and 22.5% on two datasets, underscoring its effectiveness in boosting the temporal reasoning abilities of LLMs.

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Rui Zheng, Bao Rong, Yuhao Zhou, Di Liang, Sirui Wang, Wei Wu, Tao Gui, Qi Zhang, Xuanjing Huang

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)

Challenge:	Recent studies have shown that pre-trained language models contain smaller matching subnetworks that are not robust to adversarial examples.
Approach:	They propose a method to find robust tickets hidden in pre-trained language models by learning binary weight masks and an adversarial loss objective to guide the search.
Outcome:	The proposed method improves on previous work on adversarial robustness evaluation.

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Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li

Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency (2021.findings-acl)

Challenge:	End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity.
Approach:	They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends.
Outcome:	The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity.

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Zekang Li, Jinchao Zhang, Zhengcong Fei, Yang Feng, Jie Zhou

LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models (2026.acl-long)

Challenge:	Existing methods to evaluate consistency capacity of open-domain chatbots are costly and low-efficient.
Approach:	They propose an efficient framework for evaluating consistency of open-domain chatbots . they use human judges to interact with chatbot, which is costly and low-efficient .
Outcome:	The proposed framework can assess the consistency capacity of chatbots and achieve a high ranking correlation with the human evaluation.

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Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Zhou, Yankai Lin, Ji-Rong Wen, Chongxuan Li

SDAR-VL: Stable and Efficient Block-wise Diffusion for Vision-Language Understanding (2026.acl-long)

Challenge:	Masked diffusion language models have achieved significant progress in language modeling . however, the systematic analysis and empirical validation of their alignment on general tasks remains underexplored.
Approach:	They propose a framework that analyzes the bias and variance of preference optimization loss and gradient based on Direct Preference Optimization.
Outcome:	The proposed model outperforms its SFT-only predecessor on general benchmarks . it consistently outperformed other strong language models and ARMs on general tasks .

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Shuang Cheng, Yuhua Jiang, Zineng Zhou, Dawei Liu, Tao Wang, Linfeng Zhang, Biqing Qi, Bowen Zhou

Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation (2025.acl-long)

Challenge:	Existing block-wise discrete diffusion models lack robust autoregressive (AR) decoders.
Approach:	They propose a block-wise discrete diffusion framework for large-scale vision-language understanding with a progressive beta noise curriculum.
Outcome:	The proposed framework improves training efficiency, convergence stability, and task performance over conventional block diffusion.

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Yupu Liang, Yaping Zhang, Zhiyang Zhang, Yang Zhao, Lu Xiang, Chengqing Zong, Yu Zhou

ConFiguRe: Exploring Discourse-level Chinese Figures of Speech (2022.coling-1)

Challenge:	Document Image Machine Translation (DIMT) faces generalization challenges due to limited training data and the complex interplay between visual and textual information.
Approach:	They propose a single-to-mix Modality alignment framework leveraging Multimodal Large Language Models (MLLMs) this framework aligns an imageonly encoder with multimodal representations of an MLLM pre-trained on large-scale document image datasets.
Outcome:	The proposed framework improves translation quality in cross-domain generalization and challenging document image scenarios.

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Dawei Zhu, Qiusi Zhan, Zhejian Zhou, Yifan Song, Jiebin Zhang, Sujian Li

Scheduled Dialog Policy Learning: An Automatic Curriculum Learning Framework for Task-oriented Dialog System (2021.findings-acl)

Challenge:	Figures of speech often deviate from their literal meanings to express deeper semantic implications.
Approach:	They propose a concept of figurative unit, which is the carrier of a figure, and build a Chinese corpus for Contextualized Figure Recognition.
Outcome:	The proposed model is based on 12 types of figures commonly used in Chinese . it shows that the proposed tasks are challenging for existing models .

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Sihong Liu, Jinchao Zhang, Keqing He, Weiran Xu, Jie Zhou

GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)

Challenge:	et al., 2013) show that dialog policy learning is an important component of the task-oriented dialogue system.
Approach:	They propose a framework that integrates curriculum learning and policy optimization . they propose to train dialog agents from easy dialogues to complex ones .
Outcome:	The proposed framework outperforms the state-of-the-art model on multi-task dialogues.

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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan

PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation (2025.acl-long)

Challenge:	Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models.
Approach:	They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks.
Outcome:	The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages.

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Linhai Zhang, Jialong Wu, Deyu Zhou, Yulan He

Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation (2023.acl-long)

Challenge:	Personalized large language models (LLMs) aim to tailor outputs to user preferences . however, user data is typically sparse, making it challenging to adapt LLMs to specific user patterns.
Approach:	They propose a progressive learning framework that groups users based on preferences and adapts LLMs in stages.
Outcome:	The proposed approach outperforms SOTA models across multiple tasks.

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Hongyi Wu, Hao Zhou, Man Lan, Yuanbin Wu, Yadong Zhang

LawBench: Benchmarking Legal Knowledge of Large Language Models (2024.emnlp-main)

Challenge:	Existing methods for implicit discourse relation recognition (IDRR) lack connectives, which is a major challenge in discourse analysis research.
Approach:	They propose a method to predict latent correlations between connectives and discourse relations using a knowledge distillation approach.
Outcome:	The proposed method outperforms state-of-the-art models on coarse-grained and fine-grain discourse relations and can be transferred to explicit discourse relation recognition and achieve acceptable performance.

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Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng

Focusing, Bridging and Prompting for Few-shot Nested Named Entity Recognition (2023.findings-acl)

Challenge:	LegalBench evaluated 20 LLMs in 162 legal tasks in 20 countries and jurisdictions.
Approach:	They present a comprehensive evaluation of 21 popular Large Language Models and the first comparative analysis of the empirical results.
Outcome:	The proposed benchmarks are based on the Bloom’s cognitive taxonomy and are compared to 21 popular LLMs.

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Yuanyuan Xu, Zeng Yang, Linhai Zhang, Deyu Zhou, Tiandeng Wu, Rong Zhou

You Impress Me: Dialogue Generation via Mutual Persona Perception (2020.acl-main)

Challenge:	Existing work on few-shot named entity recognition (NER) addresses flat entities instead of nested entities.
Approach:	They propose a method based on focusing, bridging and prompting for few-shot nested NER without using source domain data.
Outcome:	The proposed method outperforms baseline models on four benchmark datasets and outperformed several competing models on F1-score by 9.33% on ACE2004, 6.17% on ace2005, 9.40% on GENIA and 5.12% on KBP2017.

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Qian Liu, Yihong Chen, Bei Chen, Jian-Guang Lou, Zixuan Chen, Bin Zhou, Dongmei Zhang

IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents (2025.emnlp-main)

Challenge:	Existing chit-chat systems tend to generate uninformative responses and lack coherent personality traits due to the diversity of speakers.
Approach:	They propose a transmitter-receiver framework which explicitly models understanding between interlocutors.
Outcome:	The proposed framework improves on a large public dataset, Persona-Chat, with a significant boost over the state-of-the-art frameworks.

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Hengyu An, Jinghuai Zhang, Tianyu Du, Chunyi Zhou, Qingming Li, Tao Lin, Shouling Ji

ORTicket: Let One Robust BERT Ticket Transfer across Different Tasks (2024.lrec-main)

Challenge:	Existing methods for detecting Indirect Prompt Injection (IPI) attacks rely on assumptions about the model's inherent security, which lacks structural constraints on agent behaviors.
Approach:	They propose a novel task execution paradigm that models the agents’ task execution process as a traversal over a planned Tool Dependency Graph (TDG).
Outcome:	The proposed model reduces unintended tool invocations triggered by injected instructions, enhancing robustness against IPI attacks.

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Yuhao Zhou, Wenxiang Chen, Rui Zheng, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

TextFusion: Privacy-Preserving Pre-trained Model Inference via Token Fusion (2022.emnlp-main)

Challenge:	Pretrained language models are susceptible to subtle perturbations and require multiple adversarial training during fine-tuning to improve their robustness.
Approach:	They propose a novel adversarial defense method ORTicket that fine-tunes a model for downstream tasks.
Outcome:	The proposed method achieves comparable robustness to other defense methods while maintaining the efficiency of fine-tuning.

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Xin Zhou, Jinzhu Lu, Tao Gui, Ruotian Ma, Zichu Fei, Yuran Wang, Yong Ding, Yibo Cheung, Qi Zhang, Xuanjing Huang

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

Challenge:	Existing methods to preserve inference privacy are available as cloud services . however, the risk of privacy leakage remains, according to recent studies .
Approach:	They propose a method to preserve inference privacy by fusing token representations in the cloud.
Outcome:	The proposed method preserves inference privacy without sacrificing performance on different scenarios.

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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

CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding (D19-1)

Challenge:	Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences.
Approach:	They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.
Outcome:	The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner.

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

Challenge:	Existing models for slot filling and intent detection fail to fully utilize cooccurrence relations between slots and intents, which restricts their potential performance.
Approach:	They propose a novel Collaborative Memory Network (CM-Net) that captures slot-specific and intent-specific features in a collaborative manner.
Outcome:	The proposed network outperforms existing models on two benchmarks and a self-collected corpus.

Document Image Machine Translation with Dynamic Multi-pre-trained Models Assembling (2024.naacl-long)

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Yupu Liang, Yaping Zhang, Cong Ma, Zhiyang Zhang, Yang Zhao, Lu Xiang, Chengqing Zong, Yu Zhou

Learning “O” Helps for Learning More: Handling the Unlabeled Entity Problem for Class-incremental NER (2023.acl-long)

Challenge:	Existing TIMT tasks focus on text-line-level images.
Approach:	They propose to extend the existing TIMT task and introduce a new framework to translate a source document image to markdown-formatted target translation.
Outcome:	The proposed task aims to translate a source document image with long context and complex layout structure to markdown-formatted target translation.

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Ruotian Ma, Xuanting Chen, Zhang Lin, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, Yun Wen Chen

Parallel Attention Network with Sequence Matching for Video Grounding (2021.findings-acl)

Challenge:	Existing Named Entity Recognition systems are typically trained on a large-scale dataset with predefined entity classes, then deployed for entity recognition on the test data without further adaptation or refinement.
Approach:	They propose a representation learning method that adaptively detects entity clusters in "O" and two effective distance-based relabeling strategies for better learning the old classes.
Outcome:	The proposed method achieves 10.62% improvement over the baseline methods.

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Hao Zhang, Aixin Sun, Wei Jing, Liangli Zhen, Joey Tianyi Zhou, Siow Mong Rick Goh

SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities (2023.findings-emnlp)

Challenge:	Existing approaches to video grounding are sensitive to quality of proposals and inefficient because all proposal-query pairs are compared.
Approach:	They propose a Parallel Attention Network with Sequence matching to capture selfmodal contexts and cross-modal attentive information between video and text.
Outcome:	The proposed approach is superior to state-of-the-art methods on three datasets.

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Dong Zhang, Shimin Li, Xin Zhang, Jun Zhan, Pengyu Wang, Yaqian Zhou, Xipeng Qiu

Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers (2020.findings-emnlp)

Challenge:	Existing multi-modal large language models typically adopt the cascade paradigm, preventing inter-modal knowledge transfer.
Approach:	They propose a large language model with intrinsic cross-modal conversational abilities . they construct a cross-text speech instruction dataset and employ a three-stage training strategy .
Outcome:	The proposed model can follow cross-modal human instructions and handle multiple modalities with one model.

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Shusheng Xu, Xingxing Zhang, Yi Wu, Furu Wei, Ming Zhou

Beyond Timestamps: Bridging Forward and Backward Reasoning in Temporal Numerical and Relational Understanding (2026.acl-long)

Challenge:	Existing methods for document summarization use graphs and unlabeled documents . Existing models require labeled data, and it is expensive to create summarized documents.
Approach:	They propose to rank sentences using transformer attentions and pre-training objectives by unlabeled documents.
Outcome:	The proposed model achieves state-of-the-art on unsupervised summarization and is less dependent on sentence positions.

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Xinying Qian, Ying Zhang, Xuhui Sui, Yu Zhao, Baohang Zhou, Jeff Z. Pan

SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling (2025.findings-acl)

Challenge:	Existing benchmarks for Temporal Numerical and Relational reasoning rely on single-task evaluation paradigms.
Approach:	They propose a benchmark to evaluate Temporal Numerical and Relational reasoning . they propose QA and verification, and a Consistency Rate to quantify robustness .
Outcome:	The proposed framework evaluates both Temporal Numerical and Relational reasoning . it measures the alignment between QA and FV and the Consistency Rate measures robustness across these directions.

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Xin Zhang, Qiyu Wei, Yingjie Zhu, Linhai Zhang, Deyu Zhou, Sophia Ananiadou

Interpretable Relevant Emotion Ranking with Event-Driven Attention (D19-1)

Challenge:	Traditional sentiment analysis methods focus on static reviews, failing to capture temporal relationship between user sentiment rating and textual content.
Approach:	They propose a dynamic graph-based framework that addresses data sparsity in streaming reviews.
Outcome:	The proposed framework reduces data sparsity by categorizing users into mid-tail, long-tail and extreme scenarios and incorporating LLM enhancements within a dynamic graph-based structure.

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Yang Yang, Deyu Zhou, Yulan He, Meng Zhang

FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow (2026.acl-long)

Challenge:	Existing studies ignore the latent event information in documents . Existing methods for detecting emotions are limited to a few words .
Approach:	They propose to integrate event information into a deep learning architecture to extract relevant emotion ranking models using corpus-level event embeddings and document-level events.
Outcome:	The proposed model performs better than state-of-the-art emotion detection and multi-label approaches on three real-world corpora and interpretable results shed light on the events which trigger certain emotions.

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Yusong Hu, Runmin Ma, Yue Fan, Jinxin Shi, Zongsheng Cao, Yuhao Zhou, Jiakang Yuan, Shuaiyu Zhang, Shiyang Feng, Xiangchao Yan, Shufei Zhang, Wenlong Zhang, Lei Bai, Bo Zhang

PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models (2026.findings-acl)

Challenge:	FlowSearch is a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning.
Approach:	They propose a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning.
Outcome:	The proposed framework achieves competitive performance on GAIA, HLE, GPQA and TRQA benchmarks and is available to download.

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Han Bao, Penghao Zhang, Yue Huang, Zhengqing Yuan, Yanchi Ru, SU Rui, Yujun Zhou, Xiangqi Wang, Kehan Guo, Nitesh V Chawla, Yanfang Ye, Xiangliang Zhang

Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) are increasingly integrated into real-world decision-making, but their ability to comprehend and reason about policy-related content remains underexplored.
Approach:	They propose a bilingual benchmark evaluating policy comprehension comprising 21K cases across a broad spectrum of policy areas.
Outcome:	The proposed model shows stronger performance on application-oriented policy tasks than on memorization or conceptual understanding, and yields the highest accuracy on structured reasoning tasks.

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Yue Zhou, Henry Zou, Barbara Di Eugenio, Yang Zhang

Skill Weaving: Efficient LLM Improvement via Modular Skillpacks (2026.findings-acl)

Challenge:	Existing research has shown that large language models have difficulty discerning the veracity of their intrinsic answers.
Approach:	They propose a jailbreak attack method that generates an aligned language model for malicious output.
Outcome:	The proposed method achieves competitive performance with more harmful outputs.

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Zhuo Li, Guodong DU, Zesheng Shi, Weiyang Guo, Weijun Yao, Yuan Zhou, Jiabo Zhang, Jing Li

CoCoST: Automatic Complex Code Generation with Online Searching and Correctness Testing (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) can specialize under fixed memory and inference budgets, but they struggle to achieve high performance across heterogeneous domains.
Approach:	They propose a modular improvement framework that partitions full capabilities of a general-purpose model into domain-specific delta modules that reorganize and refine the model's internal knowledge.
Outcome:	The proposed framework outperforms monolithic models on multi-task and agentic benchmarks and achieves up to 4 speedup.

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Xinyi He, Jiaru Zou, Yun Lin, Mengyu Zhou, Shi Han, Zejian Yuan, Dongmei Zhang

EmoBench: Evaluating the Emotional Intelligence of Large Language Models (2024.acl-long)

Challenge:	Existing methods to improve code generation from natural language descriptions are difficult due to complex structure, subtle bugs, and lack of supplementary contents.
Approach:	They propose a framework that enhances complex code generation by online searching for more information with planned queries and correctness testing for code refinement.
Outcome:	The proposed framework improves the quality of complex code generation on the DS-1000 and ClassEval datasets.

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Sahand Sabour, Siyang Liu, Zheyuan Zhang, June Liu, Jinfeng Zhou, Alvionna Sunaryo, Tatia Lee, Rada Mihalcea, Minlie Huang

InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)

Challenge:	Existing benchmarks for Emotional Intelligence (EI) focus on emotion recognition, neglecting essential EI capabilities.
Approach:	They propose a benchmark that proposes a comprehensive definition for machine EI . they propose 400 hand-crafted questions in English and Chinese to evaluate EI.
Outcome:	The proposed benchmarks focus on emotion recognition, neglecting EI capabilities . they are constructed from existing datasets, which include frequent patterns and errors . the proposed benchmark includes questions in English and Chinese that require thorough reasoning and understanding .

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Junyou Su, He Zhu, Xiao Luo, Liyu Zhang, Hong-Yu Zhou, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning (2024.findings-emnlp)

Challenge:	Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks .
Approach:	They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking.
Outcome:	Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data.

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Jiaru Zou, Mengyu Zhou, Tao Li, Shi Han, Dongmei Zhang

Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue (2022.aacl-main)

Challenge:	Recent advances in fine-tuning large language models have greatly enhanced their usage in domain-specific tasks.
Approach:	They propose a method which internalizes prompt knowledge during model fine-tuning to achieve efficient inference and save costs.
Outcome:	The proposed approach reduces input tokens by 90%, accelerates inference by 4.2 times, and reduces monetary inference costs by 88.3%.

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Maximillian Chen, Weiyan Shi, Feifan Yan, Ryan Hou, Jingwen Zhang, Saurav Sahay, Zhou Yu

Dynamically Fused Graph Network for Multi-hop Reasoning (P19-1)

Challenge:	Persuasive dialogue systems are designed for chatbots to communicate with and influence users with specific goals.
Approach:	They propose a modular dialogue system framework that integrates factual information and social content into persuasive dialogues.
Outcome:	The proposed framework is generalizable to any dialogue tasks that have mixed social and task contents.

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Lin Qiu, Yunxuan Xiao, Yanru Qu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu

KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model (2023.findings-emnlp)

Challenge:	Text-based question answering (TBQA) has been studied extensively in recent years.
Approach:	They propose a Dynamically Fused Graph Network to answer questions requiring multiple scattered evidence and reasoning over them.
Outcome:	The proposed method achieves competitive results on a public TBQA dataset and produces interpretable reasoning chains.

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Lei Geng, Xu Yan, Ziqiang Cao, Juntao Li, Wenjie Li, Sujian Li, Xinjie Zhou, Yang Yang, Jun Zhang

Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning (D18-1)

Challenge:	Existing models for multilingual biomedical training are monolingual, resulting in limited cross-lingual capability.
Approach:	They propose a model that transforms a multilingual biomedical corpus into a biomedically domain using a knowledge-anchored approach.
Outcome:	The proposed model outperforms monolingual and multilingual models in cross-lingual scenarios.

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Tianyi Liu, Xinsong Zhang, Wanhao Zhou, Weijia Jia

Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention (2020.coling-main)

Challenge:	Existing methods for extracting relations are slow and lack precision . a novel approach to extract relations is proposed to reduce noise between sentences .
Approach:	They propose a word-level distant supervised approach for relation extraction using New York Times and Freebase.
Outcome:	The proposed method improves the area of precision/call(PR) from 0.35 to 0.39 over the state-of-the-art methods.

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Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment (2026.acl-long)

Challenge:	Existing approaches to handle wrong labeling and long-tail relations are labor-intensive and scarce training data.
Approach:	They propose a neural network to handle wrong labeling and long-tail relations by collaborating relation-augmented attention.
Outcome:	The proposed neural network improves the state-of-the-art on the NYT dataset .

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Yuming Yang, Mingyoung Lai, Wanxu Zhao, Xiaoran Fan, Zhiheng Xi, Mingqi Wu, Chiyue Huang, Jun Zhao, Haijun Lv, Jian Tong, Yunhua Zhou, Yicheng Zou, Qipeng Guo, Tao Gui, Qi Zhang, Xuanjing Huang

WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback (2025.findings-emnlp)

Challenge:	Existing methods assess suitability primarily through student likelihood, favoring trajectories that align closely with the student model’s current behavior but overlooking more informative ones.
Approach:	They propose a Rank–Surprisal Ratio metric that captures both alignment and informativeness to assess the suitability of a reasoning trajectory.
Outcome:	The proposed metric captures both alignment and informativeness to assess the suitability of a reasoning trajectory.

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Minda Hu, Tianqing Fang, Jianshu Zhang, Jun-Yu Ma, Zhisong Zhang, Jingyan Zhou, Hongming Zhang, Haitao Mi, Dong Yu, Irwin King

Unsupervised Fine-tuning for Text Clustering (2020.coling-main)

Challenge:	Web agents powered by Large Language Models lack the ability to perform in uncertain web environments.
Approach:	They propose to reconstruct web agents' reasoning skills into chain-of-thought rationales by fine-tuning their LLM backbone into a web-based model.
Outcome:	The proposed approach significantly improves the agent self-improving benchmark OpenWebVoyager, demonstrating that it can be used to improve the agent's reasoning skills.

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Shaohan Huang, Furu Wei, Lei Cui, Xingxing Zhang, Ming Zhou

Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study (P19-1)

Challenge:	Existing approaches to text clustering fine-tune pre-trained models have been limited.
Approach:	They propose a method to fine-tune pre-trained models unsupervisedly for text clustering by learning text representations and cluster assignments using a clustering oriented loss.
Outcome:	The proposed model outperforms baseline methods and achieves state-of-the-art results on three text clustering datasets.

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Tao Ge, Xingxing Zhang, Furu Wei, Ming Zhou

Would LLMs be Good Historical Linguists and Chinese Dialect Learners? (2026.acl-long)

Challenge:	Sequence-to-sequence (seq2sequ) models have a weakness: they cannot always generate sentences without grammatical errors.
Approach:	They propose to use automatic grammatical error correction to improve seq2seq models . they conduct experiments on machine translation, formality style transfer, sentence compression and simplification .
Outcome:	The proposed system can improve grammaticality of generated text and improve formal style tasks.

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Yicheng Liu, Shumin Shi, Youchao Zhou, Xingchen Zhang

SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture (2025.naacl-long)

Challenge:	Large language models struggle with low-resource Chinese dialects due to substantial phonological divergence.
Approach:	They propose to incorporate Middle Chinese, the common historical ancestor of modern Chinese dialects, into LLMs to improve dialectal pronunciation modeling.
Outcome:	The proposed approach improves on standard Chinese but struggles with low-resource Chinese dialects . the proposed model improves over baselines while revealing variation across dialects.

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Jiayi Han, Liang Du, Hongwei Du, Xiangguo Zhou, Yiwen Wu, Yuanfang Zhang, Weibo Zheng, Donghong Han

Temporal Evidence Chain for Temporal Knowledge Graph Question Answering with Large Language Models (2026.acl-long)

Challenge:	balancing the training budget, downstream performance, and general capabilities of large language models remains a challenge in many applications.
Approach:	They propose a mixture of expert framework based on Soft LoRA and Identity Mixture . SLIM allows dynamic routing between LoRA adapters and identity layers .
Outcome:	The proposed framework reduces training cost while maintaining general capabilities . it can be open-sourced upon publication.

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Shihao Liu, Xiaofei Zhou, Bo Wang, Geyuan Zhang

WPO: Enhancing RLHF with Weighted Preference Optimization (2024.emnlp-main)

Challenge:	Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge from Temporal knowledge graphs.
Approach:	They propose a framework to construct temporal evidence chains for LLM reasoning using Temporal Knowledge Graphs.
Outcome:	TECQA outperforms existing methods on MultiTQ and CronQuestions.

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Wenxuan Zhou, Ravi Agrawal, Shujian Zhang, Sathish Reddy Indurthi, Sanqiang Zhao, Kaiqiang Song, Silei Xu, Chenguang Zhu

Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions (2025.naacl-long)

Challenge:	Off-policy preference optimization suffers from a distributional gap between the policy used for data collection and the target policy, leading to suboptimal optimization.
Approach:	They propose a method to simulate on-policy learning with off-police preference data.
Outcome:	The proposed method outperforms Direct Preference Optimization (DPO) by up to 5.6% on Alpaca Eval 2 and MT-bench.

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Hongru Wang, Boyang Xue, Baohang Zhou, Tianhua Zhang, Cunxiang Wang, Huimin Wang, Guanhua Chen, Kam-Fai Wong

Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection (P18-1)

Challenge:	Existing studies focus on leveraging internal knowledge of Large Language Models (LLMs) to answer known questions.
Approach:	They propose a framework that allows LLMs to choose between internal and external knowledge . they use a dataset to analyze compositional questions that are composed of unknown sub-questions .
Outcome:	The proposed framework can achieve comparable or even better performance with much fewer external calls compared with several strong baselines.

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Yu Hong, Wenxuan Zhou, Jingli Zhang, Guodong Zhou, Qiaoming Zhu

Enhancing the General Agent Capabilities of Low-Paramter LLMs through Tuning and Multi-Branch Reasoning (2024.findings-naacl)

Challenge:	Recent studies show that neural networks can be used for event detection but can be contaminated by spurious features.
Approach:	They propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features.
Outcome:	The proposed method is highly effective and adaptable on the ACE 2005 and TAC-KBP 2015 corpora.

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Qinhao Zhou, Zihan Zhang, Xiang Xiang, Ke Wang, Yuchuan Wu, Yongbin Li

Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning (2024.acl-long)

Challenge:	Open-source pre-trained Large Language Models exhibit strong language understanding and generation capabilities, making them highly successful in a variety of tasks.
Approach:	They propose a method to construct agent-specific data using GPT-4 and supervised fine-tuning . they find that supervised tunning can significantly reduce hallucination outputs and formatting errors in agent tasks .
Outcome:	The proposed method improves on five agent tasks of AgentBench.

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Ming Li, Yong Zhang, Shwai He, Zhitao Li, Hongyu Zhao, Jianzong Wang, Ning Cheng, Tianyi Zhou

CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code (2024.findings-emnlp)

Challenge:	Earlier studies of instruction tuning on Large Language Models focus on creating large, varied, and high-quality datasets with responses curated by human experts.
Approach:	They propose to use a smaller and weaker model to fine tune a larger and stronger model . they find it can largely speed up the data filtering and improve performance .
Outcome:	The proposed model can filter instruction data faster and better on benchmarks.

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Batu Guan, Yao Wan, Zhangqian Bi, Zheng Wang, Hongyu Zhang, Pan Zhou, Lichao Sun

DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) have shown significant potential in code generation, but they also present challenges regarding the protection of Intellectual Property (IP) related to model architectures, weights, and training data.
Approach:	They propose a multi-bit watermarking technique that embeds additional information to preserve provenance details, such as the vendor ID of an LLM.
Outcome:	The proposed technique preserves provenance details while maintaining syntactical correctness of generated code.

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Ranchi Zhao, Zhen Thai, Yifan Zhang, Shengding Hu, Jie Zhou, Yunqi Ba, Jie Cai, Zhiyuan Liu, Maosong Sun

AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications (2022.coling-1)

Challenge:	Large Language Models (LLMs) are pre-trained on vast datasets composed of billions of tokens harvested from diverse text sources.
Approach:	They propose a data engineering method to refine the pretraining corpus through data rating, tagging and editing.
Outcome:	The proposed method improves the quality of the pretraining corpus by enhancing 100 billion tokens of the training corpus.

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Yusen Zhang, Zhongli Li, Qingyu Zhou, Ziyi Liu, Chao Li, Mina Ma, Yunbo Cao, Hongzhi Liu

CE-GPPO: Coordinating Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning (2026.acl-long)

Challenge:	Existing methods to correct handwritten assignments are to use OCR to recognize characters and compare them to answers.
Approach:	They propose a multimodal approach to correct handwritten Chinese characters by combining the visual information of students' handwriting with the encoded representations of answers.
Outcome:	The proposed model outperforms OCR-based methods by a large margin.

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Zhenpeng Su, Leiyu Pan, Minxuan Lv, Yuntao Li, Wenping Hu, Fuzheng Zhang, Kun Gai, Guorui Zhou

HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)

Challenge:	Existing methods for proximal policy optimization discard valuable gradient signals from low-probability tokens due to the clipping mechanism.
Approach:	They propose an algorithm that reintroduces gradients from clipped tokens in native PPO in a gentle and bounded manner.
Outcome:	The proposed algorithm outperforms strong baselines on reasoning benchmarks on different model scales.

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Fan Xu, Xinyu Hu, Zhenghan Yu, Li Lin, Xu Zhang, Yang Zhang, Wei Zhou, Jinjie Gu, Xiaojun Wan

A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking (2020.acl-main)

Challenge:	relying on large language models for information has raised concerns about reliability and accuracy of outputs.
Approach:	They propose a hallucination taxonomy with 11 categories for various NLG tasks and propose HAllucination Detection models which integrate hallucinism detection, span-level identification, and correction into a single inference process.
Outcome:	The proposed models outperform baselines on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility.

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Yong Shan, Zekang Li, Jinchao Zhang, Fandong Meng, Yang Feng, Cheng Niu, Jie Zhou

A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data (2024.lrec-main)

Challenge:	Existing methods for dialogue state tracking ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots.
Approach:	They propose to employ a contextual hierarchical attention network to enhance the DST by learning contextual representations.
Outcome:	The proposed approach achieves 52.68% and 58.55% joint accuracy on multiWOZ 2.0 and MultiWOZ 2.1 datasets and significantly improves performance (+1.24% and +5.98%)

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Hongxiao Zhang, Mingtong Liu, Chunyou Li, Yufeng Chen, Jinan Xu, Ming Zhou

CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization (2021.emnlp-main)

Challenge:	Existing methods for fine-tuning domain adaptation have overfitting problem in low-resource domains . lack of parallel data makes it difficult for model to learn domain-specific knowledge .
Approach:	They propose a Reinforcement Learning Domain Adaptation method for Neural Machine Translation that uses in-domain source monolingual data to make up for the lack of parallel data.
Outcome:	The proposed method can alleviate overfitting and reinforce the model to learn domain-specific knowledge.

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Haitao Lin, Liqun Ma, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong

TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models (2025.acl-long)

Challenge:	Existing summarization methods are prone to generate redundant and incoherent summaries, causing the performance to be worse.
Approach:	They propose a Chinese dataset for Customer Service Dialogue Summarization (CSDS) that provides role-oriented summaries to acquire different speakers' viewpoints.
Outcome:	The proposed dataset improves the abstractive summaries in two aspects . it also provides role-oriented summary to acquire different speakers’ viewpoints .

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Xinyi He, Yihao Liu, Mengyu Zhou, Yeye He, Haoyu Dong, Shi Han, Zejian Yuan, Dongmei Zhang

MELOV: Multimodal Entity Linking with Optimized Visual Features in Latent Space (2024.findings-acl)

Challenge:	Tabular data are crucial in many fields and their understanding by large language models (LLMs) under high parameter efficiency paradigm is important.
Approach:	They propose a module that uses 2D LoRA to encode low-rank information on cell positions to improve table serialization and representation of two-dimensional structured information within a one-dimensional sequence.
Outcome:	Experiments on four tabular-related datasets show that TableLoRA outperforms vanilla LoRA and surpasses table encoding methods tested in control.

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Xuhui Sui, Ying Zhang, Yu Zhao, Kehui Song, Baohang Zhou, Xiaojie Yuan

Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning (2021.findings-acl)

Challenge:	Existing approaches to multimodal entity linking focus on textual contexts but lack in social media vision modality.
Approach:	They propose a latent space vision feature optimization framework MELOV to address these challenges . they exploit variational autoencoder to mine shared information and generate text-based visual features .
Outcome:	The proposed framework is superior to existing methods on three benchmark datasets.

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Yucheng Zhou, Xiubo Geng, Tao Shen, Jian Pei, Wenqiang Zhang, Daxin Jiang

There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It (2021.tacl-1)

Challenge:	Existing methods focus on graph triples with event overlap, but ignore more supportive triples . Script reasoning relies on understanding the relationship between two events .
Approach:	They propose a model to learn the inferential relations between events from the whole eventuality KG . they propose 'script adapter' to extend the model to infer the associated relations between an event chain and a subsequent event candidate.
Outcome:	The proposed model is compared with baselines using external KG or not on a script reasoning task.

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Jianyou Wang, Xiaoxuan Zhang, Yuren Zhou, Christopher Suh, Cynthia Rudin

Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)

Challenge:	Existing algorithms for limerick generation are difficult to use, as they must follow strict structural, meter, and rhyming constraints.
Approach:	They propose a system for automatic limerick generation that outperforms state-of-the-art models.
Outcome:	The proposed system outperforms state-of-the-art models and rule-based models in generating limericks.

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Linzhuang Sun, Mingyang Chen, Hao Liang, Tianpeng Li, Zhou Yijie, Chenzheng Zhu, Tianyu Guo, Huanyao Zhang, Jingxuan Wei, Bihui Yu, Fan Yang, Wentao Zhang

Challenge:	Existing methods for training effective AI agents often resort to synthetic data generation.
Approach:	They propose a plug-and-play framework for data quality control in tool-use scenarios . they construct a tool-verify dataset and release a benchmark to assess its performance .
Outcome:	The proposed framework surpasses Qwen2.5-72B-Instruct on Tool-V-Bench and the previous APIGen-MT dataset.

CFBench: A Comprehensive Constraints-Following Benchmark for LLMs (2025.acl-long)

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Tao Zhang, ChengLIn Zhu, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang, Tao Zhang, Fan Yang, Mingan Lin, Yujing Qiao, Weipeng Chen, Bin Cui, Wentao Zhang, Zenan Zhou

LSSF: Safety Alignment for Large Language Models through Low-Rank Safety Subspace Fusion (2025.acl-long)

Challenge:	Existing evaluations of Large Language Models (LLMs) focus on fragmented constraints or narrow scenarios, but they overlook the comprehensiveness and authenticity of constraints from the user’s perspective.
Approach:	They propose a Chinese Comprehensive Constraints Following Benchmark for LLMs that compiles constraints from real-world instructions and constructs a systematic framework for constraint types.
Outcome:	The proposed framework integrates multi-dimensional assessment criteria with requirement prioritization, covering various perspectives of constraints, instructions, and requirement fulfillment.

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Guanghao Zhou, Panjia Qiu, Cen Chen, Hongyu Li, Jason Chu, Xin Zhang, Jun Zhou

RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction (2022.acl-srw)

Challenge:	Existing safety alignment methods rely on fine-tuning, which inadvertently leads to the increased complexity and computational resources required.
Approach:	They propose a safety re-alignment framework with Low-Rank Safety Subspace Fusison that exploits low-rank safety characteristics of LLMs by constructing a low-ranked projection matrix to extract the principal components of safety vectors.
Outcome:	The proposed method exploits low-rank safety subspace of the LLMs and is stable during fine-tuning process and is isolated from the model’s general capabilities.

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Zhe Li, Luoyi Fu, Xinbing Wang, Haisong Zhang, Chenghu Zhou

On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering (2024.findings-naacl)

Challenge:	Existing methods for relational triple extraction ignore semantic information of relations or predict subjects and objects sequentially.
Approach:	They propose a relation-first blank filling network to capture semantic information of relations . they transform relations into relation templates with blanks which contain the fine-grained semantic representation of relations.
Outcome:	The proposed model outperforms current state-of-the-art methods on public benchmark datasets.

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Linyong Nan, Ellen Zhang, Weijin Zou, Yilun Zhao, Wenfei Zhou, Arman Cohan

Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training (2024.emnlp-main)

Challenge:	a new study evaluates how Large Language Models interact with a SQL interpreter . the model is limited in context and is stochastic, making it less suited for tasks requiring high precision and extensive computations.
Approach:	They propose and evaluate two interaction strategies to evaluate how LLMs interact with a SQL interpreter.
Outcome:	The proposed framework improves the accuracy and reliability of the evaluations.

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Zhou Zhang, Dongzeng Tan, Jiaan Wang, Yilong Chen, Jiarong Xu

LIMIT-BERT : Linguistics Informed Multi-Task BERT (2020.findings-emnlp)

Challenge:	Emojis have gained immense popularity on social media platforms, serving as a common means to supplement or replace text.
Approach:	They propose a graph pre-train framework for text and emoji co-modeling that incorporates two tasks: node-level graph contrastive learning and edge-level link reconstruction learning.
Outcome:	The proposed framework improves on the Xiaohongshu and Twitter datasets with two types of downstream tasks.

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Junru Zhou, Zhuosheng Zhang, Hai Zhao, Shuailiang Zhang

Synchronously Generating Two Languages with Interactive Decoding (D19-1)

Challenge:	Existing language models are usually trained on large amounts of unlabeled text data.
Approach:	They propose a multi-task language representations learning framework for multi-linguistics tasks by Multi-Task Learning.
Outcome:	The proposed model outperforms the baseline Whole Word Masking BERT on both dependency and constituent syntactic/semantic parsing, GLUE benchmark, and SNLI task.

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Yining Wang, Jiajun Zhang, Long Zhou, Yuchen Liu, Chengqing Zong

Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering (2025.acl-short)

Challenge:	Experimental results show that multilingual NMT models handle multiple language pairs in one model.
Approach:	They propose an interactive approach to translate a source language into two different languages simultaneously and interactively.
Outcome:	The proposed approach improves on IWSLT and WMT datasets.

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Songtao Jiang, Chenyi Zhou, Yan Zhang, Yeying Jin, Zuozhu Liu

Optimizing Language Models with Fair and Stable Reward Composition in Reinforcement Learning (2024.emnlp-main)

Challenge:	Current approaches generate visual markers for all questions, generating excessive visual markers.
Approach:	They propose a plug-and-play approach that adapts to the complexity of questions . they propose combining fast intuitive judgments with deliberate analytical reasoning .
Outcome:	The proposed approach improves performance on four benchmarks on ScienceQA, TextQA, VizWiz, and MME.

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Jiahui Li, Hanlin Zhang, Fengda Zhang, Tai-Wei Chang, Kun Kuang, Long Chen, Jun Zhou

ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning (2026.findings-acl)

Challenge:	Recent research has developed algorithms for reinforcement learning from human feedback and AI-generated feedback.
Approach:	They propose a method for reinforcement learning from human feedback and AI-generated feedback that incorporates weighting, ranking, and constraining to handle disparate rewards.
Outcome:	The proposed method reduces disparity and enhances stability among rewards . empirical results show that the proposed method is efficient and straightforward .

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Zhensheng Wang, Xiaole Liu, Wenmian Yang, Kun Zhou, Yiquan Zhang, Weijia Jia

Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together (N19-1)

Challenge:	Existing tabular question answering systems cannot perform future-oriented numerical prediction . open-domain tabular questions are a popular approach for QA tasks .
Approach:	They propose a task that covers time-series forecasting and forecast-based reasoning scenarios using real estate data.
Outcome:	The proposed framework decomposes the problem into three collaborative roles that synthesize the results to construct a precise and consistent final answer.

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Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

One vs. Many QA Matching with both Word-level and Sentence-level Attention Network (C18-1)

Challenge:	Neural networks equipped with self-attention have parallelizable computation and the ability to capture both long-range and local dependencies.
Approach:	They propose a novel attention mechanism called "Multi-mask Tensorized Self-Attention" it captures pairwise and global dependencies by a compatibility function composed of dot-product and additive attentions .
Outcome:	The proposed model outperforms CNN-/RNN-/attention-based models on nine NLP benchmarks with compelling memory- and time-efficiency.

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Lu Wang, Shoushan Li, Changlong Sun, Luo Si, Xiaozhong Liu, Min Zhang, Guodong Zhou

Versatile Framework for Song Generation with Prompt-based Control (2025.findings-emnlp)

Challenge:	Existing studies on question answer matching focus on formal text . however, there exists many scenarios where the QA text is informal .
Approach:	They propose a novel QA matching approach using informal text from a product review site.
Outcome:	The proposed approach improves word-level and sentence-level attentions for solving the noisy problem in the informal text.

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Yu Zhang, Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Jingyu Lu, Rongjie Huang, Ruiyuan Zhang, Zhiqing Hong, Ziyue Jiang, Zhou Zhao

MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning (2026.findings-acl)

Challenge:	Existing methods for song generation fail to generate vocals with prompt-based control and proper alignment.
Approach:	VersBand is a multi-task song generation framework for synthesizing high-quality songs with prompt-based control.
Outcome:	Experimental results show that VersBand performs better than baseline models across multiple song generation tasks.

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Jiahang Lin, Kai Hu, Binghai Wang, Yuhao Zhou, Zhiheng Xi, Honglin Guo, Shichun Liu, Junzhe Wang, Shihan Dou, Enyu Zhou, Hang Yan, Zhenhua Han, Tao Gui, Qi Zhang, Xuanjing Huang

WebQuality: A Large-scale Multi-modal Web Page Quality Assessment Dataset with Multiple Scoring Dimensions (2025.naacl-long)

Challenge:	Existing work on long document visual question answering is based on Retrieval-Augmented Generation (RAG) where textual or visual content is encoded into embeddings and relevance is determined by similarity scores with respect to the original query.
Approach:	They propose a framework that employs an agentic, vision-aware workflow to address long document visual question answering through iterative information discovery and synthesis.
Outcome:	The proposed framework outperforms existing RL systems by 10.4% on the MMLongbench-Doc benchmark and demonstrates superior training performance over GRPO.

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Tao Zhang, Yige Wang, ZhuHangyu ZhuHangyu, Li Xin, Chen Xiang, Tian Hua Zhou, Jin Ma

Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning (2024.naacl-long)

Challenge:	Existing studies on web page quality assessment neglect the aspect of web page content.
Approach:	They propose a Chinese dataset for web page quality assessment . the dataset includes over 65,000 detailed an-notations spanning four sub-dimensions .
Outcome:	The proposed dataset includes over 65,000 detailed an-notations spanning four sub-dimensions and incorporates elements such as HTML+CSS, text, and visual screenshot.

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Yunchao Zhang, Zonglin Di, Kaiwen Zhou, Cihang Xie, Xin Wang

Categorizing Semantic Representations for Neural Machine Translation (2022.coling-1)

Challenge:	Towards Byzantine-robust federated embodied agent learning, we study the attack and defense for the task of vision-and-language navigation (VLN)
Approach:	They propose a new method to defend against a navigation-and-language navigation attack using navigation as wish (NAW) the method provides the server with a 'prompt' of the vision-and language alignment variance between benign and malicious clients so they can be distinguished during training.
Outcome:	The proposed method outperforms other state-of-the-art defense methods on two VLN datasets.

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Yongjing Yin, Yafu Li, Fandong Meng, Jie Zhou, Yue Zhang

Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition (2021.naacl-main)

Challenge:	Modern neural machine translation models suffer limitation in compositional generalization, resulting in weakened translation performance on unseen compounds.
Approach:	They propose to introduce categorization to the contextualized representations to improve generalization by reducing sparsity and overfitting.
Outcome:	The proposed method reduces compositional generalization error rates by 24% on a dedicated MT dataset.

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Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, Jie Zhou

Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network (P19-1)

Challenge:	Existing models fail to fully utilize contextual information which plays an important role in interpreting sentences.
Approach:	They propose a graph-based Context Tracking Network to model the discourse context for IDRR.
Outcome:	The proposed model can integrate sentence-level and token-level contextual semantics better than existing models.

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Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis (2025.findings-acl)

Challenge:	Existing studies on aspect sentiment classification focus on non-interactive reviews . a new task aims to predict sentiment polarities for specific aspects from interactive reviews based on annotated corpus .
Approach:	They propose a task to predict aspects from interactive QA style reviews using an annotated corpus.
Outcome:	The proposed approach is compared with state-of-the-art methods against a high-quality corpus of data.

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Yu Zhang, Wenxiang Guo, Changhao Pan, Dongyu Yao, Zhiyuan Zhu, Ziyue Jiang, Yuhan Wang, Tao Jin, Zhou Zhao

RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)

Challenge:	Existing zero-shot singing voice synthesis models depend on phoneme and note boundary annotations, limiting their robustness and producing poor transitions between phonemes and notes.
Approach:	They propose a multi-task multilingual zero-shot SVS model with style transfer and style control based on various prompts.
Outcome:	Experimental results show that TCSinger 2 outperforms baseline models in subjective and objective metrics across multiple related tasks.

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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang

Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020.findings-emnlp)

Challenge:	Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks.
Approach:	They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions.
Outcome:	The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks.

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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou

Challenge:	Standard test sets for supervised learning evaluate in-distribution generalization but are misleading when a dataset has systematic gaps.
Approach:	They propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data.
Outcome:	The proposed model performs significantly lower on contrast sets than on the original test sets—up to 25% in some cases.

A Split-and-Recombine Approach for Follow-up Query Analysis (D19-1)

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Qian Liu, Bei Chen, Haoyan Liu, Jian-Guang Lou, Lei Fang, Bin Zhou, Dongmei Zhang

Semantic Parsing

Challenge:	Context-dependent semantic parsing has proved to be an important but challenging task.
Approach:	They propose to perform follow-up query analysis to restate context-dependent queries with contextual information.
Outcome:	The proposed approach outperforms the state-of-the-art by nearly 8% on the FollowUp dataset . the extensibility of STAR on the SQA dataset is also promising .

GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval (2026.acl-long)

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Minghan Li, Tianrui Lv, Chao Zhang, Guodong Zhou

Unveiling Opinion Evolution via Prompting and Diffusion for Short Video Fake News Detection (2024.findings-acl)

Challenge:	Existing dense retrieval methods neglect the explicit legal logic that underpins legal relevance.
Approach:	They propose a framework that reformulates retrieval as an inference process over latent legal variables.
Outcome:	GLIER outperforms strong baselines like SAILER and KELLER in a legal case-based retrieval task . the framework exhibits exceptional data efficiency even when trained with only 10% of the data .

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Linlin Zong, Jiahui Zhou, Wenmin Lin, Xinyue Liu, Xianchao Zhang, Bo Xu

Can Multimodal Large Language Models Understand Spatial Relations? (2025.acl-long)

Challenge:	Existing methods for short video fake news detection ignore the implicit opinions and evolving nature of opinions across modalities.
Approach:	They propose a short video fake news model that mines implicit opinions within short videos and promotes the evolution of both explicit and implicit opinions across all modalities.
Outcome:	The proposed model outperforms existing methods on a publicly available dataset for short video fake news detection.

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Jingping Liu, Ziyan Liu, Zhedong Cen, Yan Zhou, Yinan Zou, Weiyan Zhang, Haiyun Jiang, Tong Ruan

Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data (2022.acl-long)

Challenge:	Spatial relation reasoning is a crucial task for multimodal large language models to understand the objective world.
Approach:	They propose a human-annotated spatial relation reasoning benchmark based on COCO2017 to improve MLLMs' spatial relation thinking.
Outcome:	The proposed benchmark achieves 48.14% accuracy, far below the human-level accuracy of 98.40%.

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Shuyan Zhou, Li Zhang, Yue Yang, Qing Lyu, Pengcheng Yin, Chris Callison-Burch, Graham Neubig

MPBench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification (2025.findings-acl)

Challenge:	Existing work has treated procedures as shallow structures without modeling the parent-child relation.
Approach:	They propose to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow . they link steps in an article to other articles with similar goals, recursively building the KB .
Outcome:	The proposed method significantly outperforms baselines according to automatic evaluation, human judgment, and application to downstream tasks such as instructional video retrieval.

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xu Zhao Pan, Pengfei Zhou, Jiaxin Ai, Wangbo Zhao, Kai Wang, Xiaojiang Peng, Wenqi Shao, Hongxun Yao, Kaipeng Zhang

Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction (2022.findings-emnlp)

Challenge:	Existing benchmarks of large language models focus on error detection, neglecting other scenarios like reasoning search.
Approach:	et al. propose a multi-task, multimodal benchmark to assess effectiveness of PRMs . step correctness, answers aggregation and reasoning process search are evaluated . ethical principles of MPBench are based on a set of evaluation paradigms based in a text-based benchmark .
Outcome:	a new benchmark assesses the effectiveness of large language models (LLMs) in multiple scenarios . it uses three evaluation paradigms to assess the effectiveness and compares them with existing models . a the proposed model improves reasoning accuracy by providing stepwise feedback for multi-step reasoning results .

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Shirong Ma, Yinghui Li, Rongyi Sun, Qingyu Zhou, Shulin Huang, Ding Zhang, Li Yangning, Ruiyang Liu, Zhongli Li, Yunbo Cao, Haitao Zheng, Ying Shen

BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation (2022.naacl-main)

Challenge:	Chinese Grammatical Error Correction (CGEC) is a challenging NLP task and a common application in human daily life.
Approach:	They propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors.
Outcome:	The proposed method improves performance of existing CGEC models and the benchmark is excellent resource for further development.

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Yuchen Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, Ming Zhou

Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning (2022.emnlp-main)

Challenge:	Standard evaluation metrics, e.g., BLEU, TER and METEOR, focus on the quality of translations at the sentence level and do not consider discourse-level features.
Approach:	They propose to use a metric to take discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans.
Outcome:	The proposed metric possesses better selectivity and interpretability at the document-level, and is more sensitive to document- level nuances.

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Xingwei He, Yeyun Gong, A-Long Jin, Weizhen Qi, Hang Zhang, Jian Jiao, Bartuer Zhou, Biao Cheng, Sm Yiu, Nan Duan

Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)

Challenge:	Existing work on commonsense generation requires models to have relational reasoning and compositional generalization capabilities.
Approach:	They propose a metric distillation rule to distill knowledge from a standard metric to a ranker and transfer it to re-ranking a retriever.
Outcome:	The proposed method surpasses the previous SOTA.

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Changzhi Sun, Xinbo Zhang, Jiangjie Chen, Chun Gan, Yuanbin Wu, Jiaze Chen, Hao Zhou, Lei Li

Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models (2024.acl-long)

Challenge:	Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs.
Approach:	They propose a novel approach for joint answer prediction and proof generation using an induced graphical model.
Outcome:	The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions.

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Zihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou, Yuxiang Zhang, Qifan Wang, Wenpeng Yin, Lifu Huang

Can LLMs Act as Historians? Evaluating Historical Research Capabilities of LLMs via the Chinese Imperial Examination (2026.acl-long)

Challenge:	Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new knowledge into Large Language Models (LLMs).
Approach:	They propose to evaluate LLMs with single edit only and parameter-modifying ME with parameter-preserving ME.
Outcome:	The proposed method can maintain LLMs’ fundamental capabilities but struggles to accurately recall edited knowledge presented in a different format.

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Lirong Gao, Zeqing Wang, Yuyan Cai, Jiayi Deng, Yanmei Gu, Yiming Zhang, Jia Zhou, Yanfei Zhang, Junbo Zhao

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

Challenge:	Existing benchmarks assess basic knowledge breadth or lexical understanding, failing to capture higher-order skills that are central to historical research.
Approach:	They propose a benchmark anchored in the Chinese Imperial Examination system that assesses historical knowledge and lexical understanding.
Outcome:	The new benchmark aims to assess the ability of LLMs to process historical materials and documents.

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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)

Challenge:	Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation.
Approach:	They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets.
Outcome:	The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark.

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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou

LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning (2026.findings-acl)

Challenge:	XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios .
Approach:	They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora.
Outcome:	The proposed dataset is labeled in English and includes only natural language understanding tasks.

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Zhengjun Huang, Zhoujin Tian, Qintian Guo, Fangyuan Zhang, Yingli Zhou, Di Jiang, Zeying Xie, Xiaofang Zhou

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

Challenge:	Large Language Models are constrained by limited context windows and lack of persistent memory . recent efforts address these limitations via external memory architectures .
Approach:	They propose an end-to-end agentic memory framework for real-time updating and retrieval that integrates hierarchical and temporal indexing layers.
Outcome:	The proposed framework outperforms established benchmarks in temporal reasoning, multi-session consistency, and retrieval efficiency.

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

Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts (2025.acl-long)

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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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

Kill two birds with one stone: generalized and robust AI-generated text detection via dynamic perturbations (2025.naacl-long)

Challenge:	Existing large language models (LLMs) have remarkable ability in high-resource languages, but their performance in multilingual scenarios is still limited.
Approach:	They propose a layer-wise expert allocation algorithm to determine the appropriate number of new experts for each layer.
Outcome:	The proposed method outperforms the previous state-of-the-art baseline with 60% fewer experts in the single-expansion setting and 33.3% fewer in the lifelong-expanding setting.

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Yinghan Zhou, Juan Wen, Wanli Peng, Xue Yiming, ZiWei Zhang, Wu Zhengxian

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

Challenge:	Existing methods focus on model generalization or focus on robustness.
Approach:	They propose a model-based AIGT detection method that can be generalized and robust under two adversarial attacks.
Outcome:	The proposed method outperforms state-of-the-art methods for generalization and robustness under two text adversarial attacks.

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang

Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder (2021.findings-acl)

Challenge:	Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question.
Approach:	They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance.
Outcome:	Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance.

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Yao Qiu, Jinchao Zhang, Jie Zhou

Enhancing In-Context Learning via Implicit Demonstration Augmentation (2024.acl-long)

Challenge:	Recent work has shown that models can be easily fooled by intentionally designed adversarial examples.
Approach:	They propose two efficient approaches for generating adversarial perturbations on embeddings and propose two new approaches to help model learn adversarials more efficiently.
Outcome:	The proposed approaches outperform strong baselines on various text classification datasets and the model's performance drops less under adversarial attack.

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Xiaoling Zhou, Wei Ye, Yidong Wang, Chaoya Jiang, Zhemg Lee, Rui Xie, Shikun Zhang

FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture (2024.emnlp-main)

Challenge:	In-context learning (ICL) is a new paradigm for pre-trained language models that can make predictions for unseen inputs without updating parameters.
Approach:	They propose a method that enables a model to augmented copies of a demonstration by leveraging their deep feature distribution and a logit calibration mechanism.
Outcome:	The proposed method significantly improves the average and worst-case accuracy across diverse PLMs and tasks.

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Wenyan Li, Crystina Zhang, Jiaang Li, Qiwei Peng, Raphael Tang, Li Zhou, Weijia Zhang, Guimin Hu, Yifei Yuan, Anders Søgaard, Daniel Hershcovich, Desmond Elliott

NILE: Internal Consistency Alignment in Large Language Models (2025.emnlp-main)

Challenge:	FoodieQA is a manually curated, fine-grained image-text dataset capturing the intricate features of food cultures across various regions in China.
Approach:	They evaluate vision–language Models and large language models on unseen food images and corresponding questions.
Outcome:	The proposed dataset evaluates vision–language Models and large language models on unseen food images and corresponding questions.

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Minda Hu, Qiyuan Zhang, Yufei Wang, Bowei He, Hongru Wang, Jingyan Zhou, Liangyou Li, Yasheng Wang, Chen Ma, Irwin King

Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation (2025.findings-naacl)

Challenge:	Recent advances show that the world knowledge in the Instruction Fine-Tuning (IFT) dataset, which is incompatible with LLMs’ internal knowledge, can greatly hurt the IFT performance.
Approach:	They propose a framework to optimize the effectiveness of IFT by carefully aligning the world and internal knowledge of LLMs.
Outcome:	The proposed framework can significantly improve performance across multiple LLM ability evaluation datasets.

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Sirui Xia, Xintao Wang, Jiaqing Liang, Yifei Zhang, Weikang Zhou, Jiaji Deng, Fei Yu, Yanghua Xiao

Evolving Agentic Workflow Driven by Human-Agent Collaboration (2026.findings-acl)

Challenge:	Existing methods to enhance credibility and verifiability of large language models (LLMs) mainly focus on passage-level or paragraph-level references or citations, which fall short in verifikatability.
Approach:	They propose a method that provides sentence-level citations in LLM-generated responses.
Outcome:	The proposed method achieves 90% accuracy in long-form question-answering tasks.

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Yuxin Liu, Jinxuan Zhang, Yuezhang Peng, Hefeng Zhou, Xiangfeng Wang, Jiong Lou, Chentao Wu, Jie LI, Jingjing Qu, Chaochao Lu

Challenge:	Existing approaches to generate agentic workflows using large language models are limited by high manual design costs, inefficient agentic search, and poor dynamic adaptability to new tasks and human preferences.
Approach:	They propose an evolutionary framework for generating agentic workflows through human-agent collaboration using evolutionary algorithms that mutate and cross over their structures, prompts, and LLM backbones.
Outcome:	The proposed framework surpasses other automated baselines by 27.34% while achieving comparable performance to o1-preview at only one-fourth of the cost.

CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference (2025.acl-long)

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Jinglong Luo, Guanzhong Chen, Yehong Zhang, Shiyu Liu, Hui Wang, Yue Yu, Xun Zhou, Yuan Qi, Zenglin Xu

D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)

Challenge:	Existing privacy-preserving Transformer Inference frameworks suffer from high computational overhead and performance losses.
Approach:	They propose a framework that integrates random permutations and SMPC to address the "impossible trinity" CENTAUR resists diverse data reconstruction attacks and boosts inference speed by 5.030.4 times .
Outcome:	CENTAUR achieves an unprecedented balance between privacy, efficiency, and performance.

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Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang

UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs (2024.acl-demos)

Challenge:	MRC requires machines to understand text and answer questions about the text.
Approach:	They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Outcome:	The proposed system is ranked at top 1 of all participants in terms of averaged F1 score.

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Chaoqun He, Renjie Luo, Shengding Hu, Ranchi Zhao, Jie Zhou, Hanghao Wu, Jiajie Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments (2026.acl-long)

Challenge:	Existing evaluation platforms are complex and poorly modularized, hindering seamless incorporation into researcher’s workflows.
Approach:	They propose a lightweight evaluation framework characterized by lightweight, comprehensiveness, modularity, and efficiency that integrates models, data, and metrics into a unified evaluation workflow.
Outcome:	The proposed evaluation framework is lightweight, comprehensive, modular, and efficient.

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Quyu Kong, Xu Zhang, Zhenyu Yang, Nolan Gao, Chen Liu, Panrong Tong, Chenglin Cai, Hanzhang Zhou, Jianan Zhang, Liangyu Chen, Zhidan Liu, Steven Hoi, Yue Wang

WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning (2026.findings-acl)

Challenge:	AndroidWorld is the dominant mobile GUI agent evaluation benchmark, but its success rates are low . despite reproducible emulator environment, it lacks key application categories such as e-commerce and enterprise communication.
Approach:	They propose a benchmark for mobile GUI agents that reflects real-world usage through long-horizon, cross-application workflows.
Outcome:	The proposed framework achieves over 90% success rates, while AndroidWorld is the dominant benchmark.

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Xinmiao Yu, Liwen Zhang, Xiaocheng Feng, Pengjun Xie, Jingren Zhou, Bing Qin, Yong Jiang

Improving the Adversarial Robustness of NLP Models by Information Bottleneck (2022.findings-acl)

Challenge:	Existing methods for reinforcement learning (RL)-based agents struggle with long-horizon planning and strategy coherence.
Approach:	They propose a reinforcement learning framework that decouples planning and execution.
Outcome:	The proposed framework outperforms baseline and first-step RL frameworks on four benchmarks.

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Cenyuan Zhang, Xiang Zhou, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh

OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs (2024.findings-emnlp)

Challenge:	Existing studies have shown that adversarial examples can be directly attributed to the presence of non-robust features.
Approach:	They propose to capture task-specific robust features while eliminating non-robust ones . they show that models can achieve significant improvement in robust accuracy .
Outcome:	The proposed method outperforms all defense methods on SST-2, AGNEWS and IMDB datasets while achieving no performance drop.

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Jintian Zhang, Cheng Peng, Mengshu Sun, Xiang Chen, Lei Liang, Zhiqiang Zhang, Jun Zhou, Huajun Chen, Ningyu Zhang

STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)

Challenge:	Recent advances in Large Language Models (LLMs) have significantly enhanced the generative capabilities for various NLP tasks, but they still suffer from hallucinations due to their exclusive reliance on parametric knowledge.
Approach:	They propose a framework that integrates retrieval tokens generated autoregressively into a single LLM to handle both tasks simultaneously in a unified forward pass.
Outcome:	The proposed framework bridges the traditionally separate training approaches for generation and retrieval by incorporating retrieval tokens generated autoregressively.

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Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang

GASim: A Graph-Accelerated Hybrid Framework for Social Simulation (2026.acl-long)

Challenge:	Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience.
Approach:	They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story.
Outcome:	The proposed approach outperforms existing methods in 84.33% of the trials.

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Xuan Zhou, Yanhui Sun, Hantao Yao, Allen He, Yongdong Zhang, Wu Liu

Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules (2023.findings-emnlp)

Challenge:	Large-scale social simulators require high latency due to expensive memory retrieval and sequential ABM execution.
Approach:	They propose a graph-accelerated hybrid multi-agent framework for large-scale social simulations that uses large language models and numerical agent-based models to scale up simulations.
Outcome:	The proposed framework delivers 9.94 speedup over the traditional framework and consumes less than 20% of tokens.

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Chaojun Xiao, Yuqi Luo, Wenbin Zhang, Pengle Zhang, Xu Han, Yankai Lin, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou

CCIM: Cross-modal Cross-lingual Interactive Image Translation (2023.findings-emnlp)

Challenge:	Large language models (LLMs) have been successful on NLP tasks but require huge parameter sizes and computational resources.
Approach:	They propose a parameter-efficient acceleration method that enhances computational efficiency through plug-and-play compression plugins.
Outcome:	The proposed method saves 53% computational costs using only 0.9% additional parameters with a performance drop of less than 2%.

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Cong Ma, Yaping Zhang, Mei Tu, Yang Zhao, Yu Zhou, Chengqing Zong

NCLS: Neural Cross-Lingual Summarization (D19-1)

Challenge:	Existing research on text image machine translation (TIMT) lacks recognized source language information resulting in a decrease in translation performance.
Approach:	They propose a cross-modal cross-lingual interactive model which incorporates source language information by synchronizing source and target language results.
Outcome:	The proposed model outperforms end-to-end models and has faster decoding speed with smaller model size than cascade models.

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Junnan Zhu, Qian Wang, Yining Wang, Yu Zhou, Jiajun Zhang, Shaonan Wang, Chengqing Zong

R-Judge: Benchmarking Safety Risk Awareness for LLM Agents (2024.findings-emnlp)

Challenge:	Existing approaches to cross-lingual summarization divide the task into two steps: summarizing and translation.
Approach:	They propose to integrate two related tasks into the training process of CLS under multi-task learning to improve cross-lingual summarization.
Outcome:	The proposed framework improves on English-to-Chinese and Chinese-to English CLS human-corrected test sets.

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Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, Gongshen Liu

Challenge:	Large language models (LLMs) have shown compelling abilities in reasoning, decision-making, and instruction following.
Approach:	They propose a benchmark to evaluate the proficiency of large language models (LLMs) in judging and identifying safety risks given agent interaction records.
Outcome:	The proposed model outperforms the best-performing model, GPT-4o, while no other models significantly exceed the random.

On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)

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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie

Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020.emnlp-main)

Challenge:	under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors.
Approach:	They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients .
Outcome:	The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets.

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Dongqin Xu, Junhui Li, Muhua Zhu, Min Zhang, Guodong Zhou

A General Knowledge Injection Framework for ICD Coding (2025.findings-acl)

Challenge:	Abstract meaning representation (AMR) parsing is limited by the size of curated datasets.
Approach:	They propose a seq2seq pre-training approach to build pre-trained models on three relevant tasks.
Outcome:	The proposed model improves performance on three relevant tasks while maintaining the response of pre-trained models.

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Xu Zhang, Kun Zhang, Wenxin Ma, Rongsheng Wang, Chenxu Wu, Yingtai Li, S Kevin Zhou

Challenge:	Existing methods to improve ICD coding focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other.
Approach:	They propose a general knowledge injection framework that integrates three key types of knowledge without specialized design of additional modules.
Outcome:	The proposed framework outperforms baseline models and is comparable to models relying on extra human annotations.

CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models (2025.acl-long)

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Yongheng Zhang, Xu Liu, Ruoxi Zhou, Qiguang Chen, Hao Fei, Wenpeng Lu, Libo Qin

T-REG: Preference Optimization with Token-Level Reward Regularization (2025.acl-long)

Challenge:	Existing studies on hallucinations in large language models are limited to a single scenario, either cross-lingual or cross-modal.
Approach:	They propose a joint Cross-lingual and Cross-modal hallucinations benchmark to fill this gap . they incorporate cross-lingual, cross-modal scenarios to assess hallucinic capabilities .
Outcome:	The proposed benchmark incorporates both cross-lingual and cross-modal hallucination scenarios to assess the cross-linguistic and crossmodal capabilities of LLMs.

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Wenxuan Zhou, Shujian Zhang, Lingxiao Zhao, Tao Meng

HyperNetwork-based Decoupling to Improve Model Generalization for Few-Shot Relation Extraction (2023.emnlp-main)

Challenge:	Reinforcement learning from human feedback (RLHF) is a dominant approach for large language models to follow instructions and produce meaningful alignment.
Approach:	They propose a method that leverages human feedback to optimize large language models . they propose to use sequence-level and token-level rewards to optimize preference .
Outcome:	The proposed method outperforms baseline methods on Alpaca Eval 2 and Arena-Hard benchmarks.

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Liang Zhang, Chulun Zhou, Fandong Meng, Jinsong Su, Yidong Chen, Jie Zhou

ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction (2024.findings-acl)

Challenge:	Existing studies cannot generalize well to unseen relations using Prototypical Networks . current approaches are dependent on large amount of labeled data and cannot deal with unseense relations well.
Approach:	They propose a HyperNetwork-based Decoupling approach to improve FSRE generalization . they propose FSre models with an encoder, network generator and refined classifiers .
Outcome:	The proposed method improves the generalization of few-shot relation extraction models.

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Henry Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip Yu, Cornelia Caragea

Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness (2020.acl-main)

Challenge:	Existing datasets for attribute value extraction focus on explicit attribute values while neglecting the implicit ones.
Approach:	They present a multimodal dataset for implicit attribute value extraction that includes AVE and multimodality.
Outcome:	The proposed dataset includes 68k training and 1.6k testing data across five domains.

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Sixing Wu, Ying Li, Dawei Zhang, Yang Zhou, Zhonghai Wu

Vector Calligrapher: Generating Scalable Vector Graphics via Structured Linguistic Supervision (2026.acl-long)

Challenge:	Generative dialogue systems tend to produce generic and boring responses, causing boring conversations . a novel commonsense knowledge-aware dialogue generation model is proposed to solve this problem .
Approach:	They propose to retrieve and introduce knowledge facts from knowledge graphs to reduce boring conversations . they use a Felicitous Fact mechanism to help the model focus on context-relevant knowledge facts .
Outcome:	The proposed model outperforms the state-of-the-art approach in most experiments.

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Bo Zhou, Xikang Chen, Yan Gong, Yin Zhang

Understanding Large Language Model Vulnerabilities to Social Bias Attacks (2025.acl-long)

Challenge:	Existing approaches to generate SVG-based fonts struggle with semantic ambiguity and inefficiency . edward mcginley: generic text tokenizers fragment coordinate-dense SVG XML into excessively long sequences .
Approach:	They propose a system that treats SVG generation as a conditional language modeling task . they propose linguistic supervision framework that decomposes typographic style into interpretable linguistic dimensions .
Outcome:	The proposed system improves CLIP score by +23% while reducing geometric error by 48% and boosts generation efficiency by 18% Command-per-Token (C/T) ratio.

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Jiaxu Zhao, Meng Fang, Fanghua Ye, Ke Xu, Qin Zhang, Joey Tianyi Zhou, Mykola Pechenizkiy

DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable linguistic capabilities across tasks . however, there is a growing concern about their potential to perpetuate social biases .
Approach:	They evaluate LLMs across gender, racial, and religious bias types . they also explore cross-bias and multiple-biases attacks .
Outcome:	The proposed models are more susceptible to gender bias attacks than racial or religious biases.

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Minxuan Lv, Zhenpeng Su, Leiyu Pan, Yizhe Xiong, Zijia Lin, Hui Chen, Wei Zhou, Jungong Han, Guiguang Ding, Wenwu Ou, Di Zhang, Kun Gai, Songlin Hu

Learning to Ignore Adversarial Attacks (2023.eacl-main)

Challenge:	Existing sparsification methods like pruning can lose model knowledge through parameter removal.
Approach:	They propose a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks.
Outcome:	The proposed approach achieves superior performance across language modeling and downstream tasks under equivalent computational constraints.

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Yiming Zhang, Yangqiaoyu Zhou, Samuel Carton, Chenhao Tan

ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification (2026.acl-long)

Challenge:	Despite the strong performance of current NLP models, they can be brittle against adversarial inputs.
Approach:	They propose a rationale model that explicitly learns to ignore adversarial tokens . their approach leads to sizable improvements in robustness over baseline models .
Outcome:	The proposed model outperforms data augmentation with adversarial examples and closes the gap between model performance and an attacked test set.

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Zhensheng Wang, ZhanTeng Lin, Wenmian Yang, Kun Zhou, Yiquan Zhang, Weijia Jia

Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study (2025.findings-emnlp)

Challenge:	Existing approaches to tabular QA are limited to closed-domain scenarios . existing approaches do not solve the core challenge of generating correct answers without user clarification .
Approach:	They propose a benchmark to tackle underspecified or uncertain queries in tabular question answering . they propose ODUTQA-MDC task and a multi-agent framework to detect ambiguities .
Outcome:	The proposed framework excels at detecting ambiguities, clarifying them through dialogue, and refining answers.

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Yujun Zhou, Jiayi Ye, Zipeng Ling, Yufei Han, Yue Huang, Haomin Zhuang, Zhenwen Liang, Kehan Guo, Taicheng Guo, Xiangqi Wang, Xiangliang Zhang

ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects (2025.findings-emnlp)

Challenge:	Existing benchmarks that rely on final-answer accuracy fail to capture the quality of the reasoning process.
Approach:	They propose a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing.
Outcome:	The proposed framework assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing.

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Jipeng Zhang, Haolin Yang, Kehao Miao, Ruiyuan Zhang, Renjie Pi, Jiahui Gao, Xiaofang Zhou

Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning (2024.findings-naacl)

Challenge:	Existing text-to-SQL models are limited to SQLite due to dataset limitations . data generated through static prompting is noisy and unreliable, authors say .
Approach:	They propose a text-to-SQL framework with execution-driven, agentic bootstrapping . ExeSQl bridges the dialect gap in text- to-Sql, achieving average improvements .
Outcome:	ExeSQL bridges the dialect gap in text-to-SQl, with average improvements of 15.2%, 10.38%, and 4.49% over GPT-4o on PostgreSQLE, MySQL, and Oracle.

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Tianqing Fang, Zhaowei Wang, Wenxuan Zhou, Hongming Zhang, Yangqiu Song, Muhao Chen

Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)

Challenge:	Event temporal reasoning aims at identifying the temporal relations between two or more events from narratives.
Approach:	They propose to detect knowledge conflicts in event temporal reasoning using bias indicators such as event relation prior bias, tense bias, narrative bias, and dependency bias.
Outcome:	The proposed method can be applied to Pre-trained Language Models and Large Language Model (LLMs) as additional training data or demonstrations for In- Context Learning.

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Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Weinan Zhang, Yong Yu, Lei Li

PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides (2025.emnlp-main)

Challenge:	Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup.
Approach:	They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup.
Outcome:	The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer.

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Hao Zheng, Xinyan Guan, Hao Kong, Wenkai Zhang, Jia Zheng, Weixiang Zhou, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

Entropy-based Exploration Conduction for Multi-step Reasoning (2025.findings-acl)

Challenge:	Existing methods for generating presentations from documents focus on improving and evaluating content quality in isolation, overlooking visual appeal and structural coherence.
Approach:	They propose an edit-based presentation generation system that analyzes and iterates on slides to create new slides.
Outcome:	The proposed presentation generation tool outperforms existing methods in three dimensions . it analyzes slides, iterates and generates edit actions based on selected slides .

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Jinghan Zhang, Xiting Wang, Fengran Mo, Yeyang Zhou, Wanfu Gao, Kunpeng Liu

Dynamic PMI-Guided Contrastive Decoding Reduces Hallucination in Large Language Models: A Unified Framework of Fine-Grained Input Transformations (2026.findings-acl)

Challenge:	Existing methods to automatically decide the depth of exploration of the reasoning procedure lead to high cost and a lack of flexibility.
Approach:	They propose a method that dynamically adjusts the exploration depth during multi-step reasoning by monitoring LLM’s output entropy and variance entropic.
Outcome:	The proposed method captures the uncertainty of the current step and the fluctuation of uncertainty across consecutive reasoning steps and then selects whether to deepen, expand, or stop exploration according to the probability.

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Dongsheng Chen, Yingqi Zhu, Xingyue Zhang, Wenqing Zhou, Lei Li

RJE: A Retrieval-Judgment-Exploration Framework for Efficient Knowledge Graph Question Answering with LLMs (2025.emnlp-main)

Challenge:	Despite the remarkable generation capabilities of large language models, the issue of hallucination remains a critical challenge.
Approach:	They propose a contrastive decoding framework based on dynamic pointwise mutual information that disentangles spurious dependencies induced by context priors, lexical co-occurrences, and syntactic structures and prioritizes causal logic.
Outcome:	The proposed framework significantly improves the model’s factuality and reasoning robustness while maintaining high computational efficiency.

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Can Lin, Zhengwang Jiang, Ling Zheng, Qi Zhao, Yuhang Zhang, Qi Song, Wangqiu Zhou

Doolittle: Benchmarks and Corpora for Academic Writing Formalization (2023.emnlp-main)

Challenge:	Knowledge graph question answering (KGQA) aims to answer natural language questions using knowledge graphs.
Approach:	They propose a framework that retrieves refined reasoning paths and evaluates their sufficiency.
Outcome:	The proposed framework outperforms existing baselines while enabling small open-source LLMs to achieve competitive results without fine-tuning LLM.

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Shizhe Diao, Yongyu Lei, Liangming Pan, Tianqing Fang, Wangchunshu Zhou, Sedrick Keh, Min-Yen Kan, Tong Zhang

SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verification (2026.acl-long)

Challenge:	Existing methods of language refinement focus on narrow, specific linguistic features within isolated sentences, such as grammatical errors and improper word use.
Approach:	They propose a task to improve the overall quality of academic writing at paragraph level by integrating automatic feedback into the training process.
Outcome:	The proposed task improves the overall quality of formal academic writing at the paragraph level.

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Tianyang Zhou, Ziyi Zhang, Haowen Lin, Somesh Jha, Mihai Christodorescu, Kirill Levchenko, Varun Chandrasekaran

Incomplete Utterance Rewriting as Semantic Segmentation (2020.emnlp-main)

Challenge:	Large language models (LLMs) have shown promise in producing idiomatic translations, but offer no correctness guarantees.
Approach:	They propose a C-to-Rust translation tool that uses an initial "unidiomatic" translation followed by an "idiomatic refinement" they evaluate SACTOR on 200 programs from two datasets and two more complex scenarios .
Outcome:	The proposed tool delivers high end-to-end correctness and produces safe, idiomatic Rust with up to 7 fewer Clippy warnings.

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Qian Liu, Bei Chen, Jian-Guang Lou, Bin Zhou, Dongmei Zhang

Exploring Dynamic Selection of Branch Expansion Orders for Code Generation (2021.acl-long)

Challenge:	Recent studies focus on the task of incomplete utterance rewriting as a machine translation task.
Approach:	They propose a semantic segmentation task which incorporates edit operations into the problem and predicts a word-level edit matrix.
Outcome:	The proposed approach outperforms existing baselines on several datasets and is four times faster than the standard approach in inference.

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Hui Jiang, Chulun Zhou, Fandong Meng, Biao Zhang, Jie Zhou, Degen Huang, Qingqiang Wu, Jinsong Su

TasTe: Teaching Large Language Models to Translate through Self-Reflection (2024.acl-long)

Challenge:	Existing code generation models model abstract syntax tree (AST) but not suitable for all multi-branch nodes.
Approach:	They propose to equip a Seq2Tree model with a branch selector to determine optimal expansion orders for multi-branch nodes.
Outcome:	The proposed model can determine optimal expansion orders of branches for multi-branch nodes.

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Yutong Wang, Jiali Zeng, Xuebo Liu, Fandong Meng, Jie Zhou, Min Zhang

Joint Optimization of Training Data and Policy in RLHF (2026.findings-acl)

Challenge:	Existing approaches to enhance LLMs' performance in machine translation are unable to fully exploit their instruction-following capabilities.
Approach:	They propose a framework for translating through self-reflection that involves two stages of inference . they propose to use the framework to refine LLMs' preliminary translations .
Outcome:	The proposed framework can produce translation outputs that match the quality of NMT systems.

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Zhuohao Yu, Jiali Zeng, Weizheng Gu, Mengyuan Sun, Yidong Wang, Fandong Meng, Jie Zhou, Shikun Zhang, Wei Ye

Challenge:	JODP optimizes policies on fixed training inputs, limiting the diversity of learning signals.
Approach:	They propose a framework where policy generates improved variants of training problems to enhance its own learning.
Outcome:	The proposed framework improves on safety alignment tasks by allowing 4B models to reach 8B model performance with less than 1% additional computational overhead.

Parrot: A Training Pipeline Enhances Both Program CoT and Natural Language CoT for Reasoning (2025.emnlp-main)

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Senjie Jin, Lu Chen, Zhiheng Xi, Yuhui Wang, Sirui Song, Yuhao Zhou, Xinbo Zhang, Peng Sun, Hong Lu, Tao Gui, Qi Zhang, Xuanjing Huang

TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers’ Guidance (2025.findings-emnlp)

Challenge:	Existing work focuses on enabling models to generate natural language chain-of-thought rationales or leverage executable and verifiable code, such as Python.
Approach:	They propose a novel training pipeline that integrates sequential P-CoT and N-Co T generation and a subtask hybrid training strategy to facilitate natural language transferability.
Outcome:	The proposed training pipeline improves both N-CoT and P-Co T performance over the RL baseline.

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Jingxian Xu, Mengyu Zhou, Weichang Liu, Hanbing Liu, Shi Han, Dongmei Zhang

Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) have made significant strides in problem-solving by incorporating reasoning processes, but this enhanced reasoning capability results in an increased number of output tokens during inference, leading to higher computational costs.
Approach:	They propose a method that internalizes explicit reasoning into the model’s habitual behavior through a Teacher-Guided compression strategy inspired by human cognition.
Outcome:	The proposed method reduces inference-time costs while maintaining high performance while preserving high quality and diversity of the distillation dataset.

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Li Zhou, Kevin Small, Yong Zhang, Sandeep Atluri

Discourse Parsing Enhanced by Discourse Dependence Perception (2022.aacl-main)

Challenge:	a proposed model for question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers is based on suggested question generation in conversational news recommendation systems.
Approach:	They propose a model for generating question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers.
Outcome:	The proposed model captures the central gists of the articles and achieves high answer accuracy.

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Yuqing Xing, Longyin Zhang, Fang Kong, Guodong Zhou

PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference (2025.acl-long)

Challenge:	Top-down neural models still suffer from the top-down error propagation issue . previous studies gradually switch from feature-based machine learning methods to deep neural models .
Approach:	They propose a top-down framework that learns from discourse dependency and constituency parsing through one shared encoder and two independent decoders.
Outcome:	The proposed framework learns from discourse dependency and constituency parsing through one shared encoder and two independent decoders on a Chinese discourse corpus.

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Jiaming Ji, Donghai Hong, Borong Zhang, Boyuan Chen, Josef Dai, Boren Zheng, Tianyi Alex Qiu, Jiayi Zhou, Kaile Wang, Boxun Li, Sirui Han, Yike Guo, Yaodong Yang

Challenge:	Using large-scale annotation data, large language models can generate noise, errors and biases, leading to unexpected behaviours.
Approach:	They propose a dataset to promote safety alignment in large language models . they separate helpfulness and harmlessness annotations for question-answering pairs .
Outcome:	The proposed dataset provides 44.6k prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels, with answers generated by Llama-family models.

GTM: A Generative Triple-wise Model for Conversational Question Generation (2021.acl-long)

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Lei Shen, Fandong Meng, Jinchao Zhang, Yang Feng, Jie Zhou

Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)

Challenge:	Experimental results show that opendomain conversational question generation improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines.
Approach:	They propose a triple-wise model with hierarchical variations for open-domain conversational question generation using a post-question-answer triple and one-to-many semantic mappings.
Outcome:	The proposed model significantly improves the quality of questions in terms of fluency, coherence and diversity over baselines.

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Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

DUB: Discrete Unit Back-translation for Speech Translation (2023.findings-acl)

Challenge:	Existing approaches to grammatical error correction (GEC) are sequence-to-sequence and sequence-edit.
Approach:	They propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally.
Outcome:	The proposed framework outperforms baselines and state-of-the-art methods on English and Chinese datasets.

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Dong Zhang, Rong Ye, Tom Ko, Mingxuan Wang, Yaqian Zhou

Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)

Challenge:	Discrete unit back-translation (DUB) is a back-translated speech-to-text translation (ST) technique that can be applied to ST . a modality gap between speech and text makes it difficult to transfer these techniques to ST due to the modality of the speech-text model.
Approach:	They propose a method to represent speech with discrete units instead of continuous features in direct ST.
Outcome:	The proposed method achieves comparable performance to existing methods that rely on large-scale external data.

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Chiyu Song, Zhanchao Zhou, Jianhao Yan, Yuejiao Fei, Zhenzhong Lan, Yue Zhang

ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models (2023.findings-emnlp)

Challenge:	Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models.
Approach:	They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs.
Outcome:	The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities.

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Zhipeng Chen, Kun Zhou, Beichen Zhang, Zheng Gong, Xin Zhao, Ji-Rong Wen

Gunrock: A Social Bot for Complex and Engaging Long Conversations (D19-3)

Challenge:	Large language models (LLMs) have excellent performance in evaluation benchmarks, but struggle in complex reasoning tasks.
Approach:	They propose a tool-augmented chain-of-thought reasoning framework for chat-based LLMs . they model chain- of-thoughting reasoning as multi-turn conversations to utilize tools .
Outcome:	The proposed framework can outperform state-of-the-art models on complex reasoning tasks.

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Dian Yu, Michelle Cohn, Yi Mang Yang, Chun Yen Chen, Weiming Wen, Jiaping Zhang, Mingyang Zhou, Kevin Jesse, Austin Chau, Antara Bhowmick, Shreenath Iyer, Giritheja Sreenivasulu, Sam Davidson, Ashwin Bhandare, Zhou Yu

Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation (2021.acl-short)

Challenge:	Gunrock is a speech-based social chatbot that can be used to understand complex sentences and have in-depth conversations.
Approach:	They propose a system that allows users to understand complex sentences and have in-depth conversations in open domains.
Outcome:	The proposed system produces longer sentences, which are directly related to user engagement (e.g., ratings, number of turns).

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Yangyifan Xu, Yijin Liu, Fandong Meng, Jiajun Zhang, Jinan Xu, Jie Zhou

LEPO: Latent Reasoning Policy Optimization for Large Language Models (2026.findings-acl)

Challenge:	Existing approaches to token-level adaptive training only use static word frequency information without considering the source language.
Approach:	They propose a bilingual mutual information based adaptive objective that assigns weights to target tokens with higher BMI . they propose to use this approach to improve token-level adaptive training .
Outcome:	The proposed method improves token-level adaptive training on two languages.

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Yuyan Zhou, Jiarui Yu, Hande Dong, Zhezheng Hao, Hong Wang, Jianqing Zhang, Qiang Lin

Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation (D19-1)

Challenge:	Existing latent reasoning methods that use chain of thought (CoT) are limited to selecting one discrete token at each reasoning step, which potentially induces information loss.
Approach:	They propose a framework that injects controllable stochasticity into latent reasoning via Gumbel-Softmax, restoring LLMs' exploratory capacity and enhancing their compatibility with Reinforcement Learning (RL).
Outcome:	The proposed framework preserves richer information for more comprehensive reasoning and is compatible with Reinforcement Learning (RL).

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Zhengxin Yang, Jinchao Zhang, Fandong Meng, Shuhao Gu, Yang Feng, Jie Zhou

Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)

Challenge:	Context modeling is essential to generate coherent and consistent translation for document-level Neural Machine Translations.
Approach:	They propose a query-guided capsule network to cluster context information into different perspectives from which the target translation may concern.
Outcome:	The proposed model outperforms baseline models on multiple datasets of different domains.

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Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li

ESCoT: Towards Interpretable Emotional Support Dialogue Systems (2024.acl-long)

Challenge:	Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored.
Approach:	They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching.
Outcome:	The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions.

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Tenggan Zhang, Xinjie Zhang, Jinming Zhao, Li Zhou, Qin Jin

RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2024.findings-acl)

Challenge:	Emotion-focused and strategy-driven chain-of-thought (ESCoT) is a new paradigm for emotional support dialogues.
Approach:	They propose an emotional support response generation scheme to improve interpretability . they generate a dataset and develop a model to generate dialogue responses with better interpretability.
Outcome:	The proposed scheme can generate dialogue responses with better interpretability.

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Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, Junran Peng

Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions (2022.acl-long)

Challenge:	Large Language Models (LLMs) have paved the way for complex tasks such as role-playing.
Approach:	They propose a framework to benchmark, elicit, and enhance role-playing abilities in Large Language Models.
Outcome:	The proposed framework improves role-playing abilities with 168,093 samples.

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Haitao Lin, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong

TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios (2025.findings-acl)

Challenge:	Existing methods for role-oriented dialogue summarization ignore information from other roles, resulting in omitted information.
Approach:	They propose a novel method that uses cross attention and decoder self-attention interactions to acquire other roles' critical information.
Outcome:	The proposed method significantly outperforms baselines on two public role-oriented dialogue summarization datasets.

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Xiaokang Zhang, Sijia Luo, Bohan Zhang, Zeyao Ma, Jing Zhang, Yang Li, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, Jifan Yu, Shu Zhao, Juanzi Li, Jie Tang

Dunhuang-Bench: How Well Do MLLMs Understand Cultural Heritage? (2026.findings-acl)

Challenge:	TableLLM is a robust large language model capable of handling tabular data manipulation tasks.
Approach:	They propose a distant supervision method for training which includes a reasoning process extension strategy and a cross-way validation strategy.
Outcome:	The proposed model has 8 billion parameters and is capable of handling tabular data tasks.

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Junyi Yuan, Jian Zhang, Tianxiu Yu, Yanlin Zhou, Xiaobo Jin, Qiufeng Wang, Fangyu Wu

BioFEG: Generate Latent Features for Biomedical Entity Linking (2023.emnlp-main)

Challenge:	Recent advances in Multimodal Large Language Models (MLLMs) have led to extensive evaluations on Chinese cultural benchmarks.
Approach:	They construct a large-scale benchmark comprising 486 images and 22,970 QA pairs to evaluate MLLMs' cultural understanding.
Outcome:	The proposed benchmark incorporates three task formats to evaluate MLLMs’ cultural understanding: Question Answering with Text Description, Multi-turn Dialogue, and Question Answers with Choices.

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Xuhui Sui, Ying Zhang, Xiangrui Cai, Kehui Song, Baohang Zhou, Xiaojie Yuan, Wensheng Zhang

A Systematic Study of Performance Disparities in Multilingual Task-Oriented Dialogue Systems (2023.emnlp-main)

Challenge:	Existing approaches to biomedical entity linking suffer from multiple types of errors due to the rarity of many biomedically relevant entities in real-world scenarios.
Approach:	They propose a latent feature generation framework to generate latent semantic features for unseen entities to capture fine-grained coherence information of unseened entities.
Outcome:	The proposed framework is superior to existing models on two benchmark datasets.

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Songbo Hu, Han Zhou, Moy Yuan, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Anna Korhonen, Ivan Vulić

Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation (2024.findings-acl)

Challenge:	Existing systems trained for Arabic or Turkish using annotated data fully parallel to English ToD data still exhibit diminished ToD task performance.
Approach:	They define new quantitative measures of absolute and relative equivalence in system performance, capturing disparities across languages and within individual languages.
Outcome:	The proposed measures capture disparities across languages and within individual languages.

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Zhibin Lan, Liqiang Niu, Fandong Meng, Jie Zhou, Min Zhang, Jinsong Su

Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)

Challenge:	In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image with translations in target language.
Approach:	They propose an end-to-end IIMT model with four modules that translate images . they propose a two-stage training framework to assist the model in learning alignment across languages .
Outcome:	The proposed model outperforms cascaded models with only 70.9% of parameters and is highly accurate.

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Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

UniDataBench: Evaluating Data Analytics Agents Across Structured and Unstructured Data (2026.acl-long)

Challenge:	Existing methods to classify QA text contain rich sentiment information.
Approach:	They propose a task/method to address QA sentiment analysis by annotating QA text pair with annotation guidelines.
Outcome:	The proposed method can learn the matching vectors of each Q-sentence, A-sentent unit.

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Han Weng, Zhou Liu, Yuanfeng Song, Xiaoming Yin, Xing Chen, Wentao Zhang

TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning (2024.findings-emnlp)

Challenge:	Existing benchmarks do not assess agents’ capabilities across data types . Existing tools only evaluate agents' ability to extract reasonable insights across data formats.
Approach:	They propose a multi-source benchmark to evaluate the performance of data analytics agents in handling diverse data sources.
Outcome:	The proposed agent performs end-to-end analysis over diverse data sources by automatically discovering cross-source linkages, decomposing goals, and generating robust, self-correcting code to extract actionable insights.

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Yuan Sui, Jiaru Zou, Mengyu Zhou, Xinyi He, Lun Du, Shi Han, Dongmei Zhang

Calibrating the Confidence of Large Language Models by Eliciting Fidelity (2024.emnlp-main)

Challenge:	Existing solutions for table reasoning tasks are mainly tested on small tables and face scalability issues and struggle with complex queries due to incomplete or dispersed data across different table sections.
Approach:	They propose a table reasoning pre-processor suite that can be used to leverage large language models (LLMs) in table-based tasks.
Outcome:	The proposed method improves LLMs’ reasoning capabilities in various tabular tasks and enhances interaction between LLM and tabular data by employing effective pre-processing.

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Mozhi Zhang, Mianqiu Huang, Rundong Shi, Linsen Guo, Chong Peng, Peng Yan, Yaqian Zhou, Xipeng Qiu

Event Detection without Triggers (N19-1)

Challenge:	Large language models with RLHF and RLAIF have good alignment but exhibit overconfidence post-alignment.
Approach:	They propose a plug-and-play method to estimate the confidence of large language models.
Outcome:	The proposed method has shown good calibration performance on 6 RLHF-LMs on four MCQA datasets.

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Shulin Liu, Yang Li, Feng Zhang, Tao Yang, Xinpeng Zhou

SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting (2023.findings-emnlp)

Challenge:	Existing approaches to event detection require annotated triggers and event types in training data.
Approach:	They propose a framework that encodes the representation of a sentence based on target event types.
Outcome:	The proposed framework achieves competitive performances compared with state-of-the-art methods.

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Xiaoying Zhang, Baolin Peng, Kun Li, Jingyan Zhou, Helen Meng

More than Text: Multi-modal Chinese Word Segmentation (2021.acl-short)

Challenge:	Experimental results show that SGP-TOD provides state-of-the-art zero-shot performance . prevailing approach for creating task bots is to fine-tune pre-trained language models .
Approach:	They propose a Schema-Guided Prompting for building Task-Oriented Dialog systems . they use predefined task schema and dialog policy to instruct fixed LLMs to generate appropriate responses .
Outcome:	The proposed system outperforms few-shot approaches on multiwoz, RADDLE, and STAR datasets.

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Dong Zhang, Zheng Hu, Shoushan Li, Hanqian Wu, Qiaoming Zhu, Guodong Zhou

Ambiguity Awareness Optimization: Towards Semantic Disambiguation for Direct Preference Optimization (2025.emnlp-main)

Challenge:	Currently, word segmentation is performed in many languages without word delimiters.
Approach:	They propose to combine the multi-modality to perform Chinese word segmentation . they propose a time-dependent multi-module interactive model to integrate multi-modality information .
Outcome:	The proposed model integrates multi-modal information for word sequence labeling with Chinese language as target . the proposed model performs well on three training sets on Chinese and other languages without word delimiters.

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Jian Li, Shenglin Yin, Yujia Zhang, Alan Zhao, Xi Chen, Xiaohui Zhou, Pengfei Xu

MT2: Towards a Multi-Task Machine Translation Model with Translation-Specific In-Context Learning (2023.emnlp-main)

Challenge:	Direct Preference Optimization (DPO) is a widely used reinforcement learning from human feedback (RLHF) method across various domains.
Approach:	They propose an approach that automatically re-weights ambiguous content to reduce ambiguities by calculating semantic similarity from preference pairs.
Outcome:	The proposed approach outperforms state-of-the-art approaches in performance across multiple model scales and widely adopted benchmark datasets.

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Chunyou Li, Mingtong Liu, Hongxiao Zhang, Yufeng Chen, Jinan Xu, Ming Zhou

Sharpness-Aware Minimization with Dynamic Reweighting (2022.findings-emnlp)

Challenge:	Sentence-level translation, document-level and terminology constrained translations are important in machine translation.
Approach:	They propose a multi-task machine translation model that integrates translation memory sentences . they propose 'in-context learning' paradigm that allows translation-specific context learning .
Outcome:	The proposed model improves translation memory, document-level translation, and document-constrained translation tasks.

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Wenxuan Zhou, Fangyu Liu, Huan Zhang, Muhao Chen

Disentangling Text Representation With Counter-Template For Unsupervised Opinion Summarization (2023.findings-acl)

Challenge:	Deep neural networks are often overparameterized and can overfit training data.
Approach:	They propose an adversarial weight minimization algorithm that conducts adversarials and finds a common adversaria per-batch.
Outcome:	The proposed algorithm finds a common adversarial weight perturbation per-batch.

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Yanyue Zhang, Deyu Zhou

CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems (2026.acl-long)

Challenge:	Existing approaches for unsupervised opinion summarization are based on reconstruction model, but selection is too coarse as not all information in each input is equally essential for the summary.
Approach:	They propose a framework for unsupervised opinion summarization based on text representation disentanglement with counter-template.
Outcome:	The proposed framework outperforms the state-of-the-art models on quality and stability on two benchmark datasets.

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Yongxuan Wu, Xixun Lin, He Zhang, Nan Sun, Kun Wang, Chuan Zhou, Shirui Pan, Yanan Cao

Making Harmful Behaviors Unlearnable for Large Language Models (2024.findings-acl)

Challenge:	LLM-based multi-agent systems (MAS) have demonstrated remarkable capabilities in solving complex tasks.
Approach:	They propose a communication inference attack that constructs new adversarial queries to induce intermediate agents’ reasoning outputs and models their semantic correlations through the global bias disentanglement and LLM-guided weak supervision.
Outcome:	The proposed attack achieves an average AUC of 0.87 and a peak AUC up to 0.99, revealing the privacy risk in MAS.

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Xin Zhou, Yi Lu, Ruotian Ma, Yujian Wei, Tao Gui, Qi Zhang, Xuanjing Huang

Born a BabyNet with Hierarchical Parental Supervision for End-to-End Text Image Machine Translation (2024.lrec-main)

Challenge:	Large language models (LLMs) are often customized by fine-tuning for the requirements of different domains.
Approach:	They propose a controllable training framework to make undesired behaviors unlearnable during the fine-tuning process.
Outcome:	The proposed framework makes undesired behaviors unlearnable during the fine-tuning process while preserving the ability to learn other information.

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Cong Ma, Yaping Zhang, Zhiyang Zhang, Yupu Liang, Yang Zhao, Yu Zhou, Chengqing Zong

Consistency Regularization Training for Compositional Generalization (2023.acl-long)

Challenge:	Existing research on text image machine translation (TIMT) is divided into two types: Cascade methods combine text image recognition and MT models to recognize source language text images.
Approach:	They propose a method which is optimized with hierarchical parental supervision to improve translation performance.
Outcome:	The proposed method significantly outperforms existing methods on synthetic and real-world tests on both synthetic and realistic images.

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Yongjing Yin, Jiali Zeng, Yafu Li, Fandong Meng, Jie Zhou, Yue Zhang

GA-S3: Comprehensive Social Network Simulation with Group Agents (2025.findings-acl)

Challenge:	Existing neural models have difficulty generalizing to unseen combinations of seen components.
Approach:	They propose to improve the capability of Transformer on compositional generalization by consistency regularization training without modifying model architectures.
Outcome:	The proposed model performs well on semantic parsing and machine translation benchmarks.

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Yunyao Zhang, Zikai Song, Hang Zhou, Wenfeng Ren, Yi-Ping Phoebe Chen, Junqing Yu, Wei Yang

Challenge:	Existing social network simulations focus on discrete events or system dynamics instead of elucidating underlying mechanisms or causal relationships.
Approach:	They propose a Social network simulation system that leverages newly designed Group Agents to make intelligent decisions regarding various online events.
Outcome:	The proposed system can make intelligent decisions regarding online events at a manageable cost.

Hierarchical Reward Modeling for Fault Localization in Large Code Repositories (2025.findings-emnlp)

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Jiwei Zhang, Jianxun Lian, Haiming Qin, Mingyang Zhou, KeZhong Lu, Rui Mao, Hao Liao

Challenge:	Large Language Models (LLMs) have limited fault localization capabilities due to limited context length.
Approach:	They propose a hierarchical localization reward model to evaluate and select the most accurate fault localization candidates from the outputs of LLMs.
Outcome:	The proposed model improves the final line-level localization recall by 12% on the SWE-Bench-Lite dataset.

Improving MLLM’s Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency (2025.findings-acl)

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Yupu Liang, Yaping Zhang, Zhiyang Zhang, Zhiyuan Chen, Yang Zhao, Lu Xiang, Chengqing Zong, Yu Zhou

Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification (2022.coling-1)

Challenge:	Multimodal Large Language Models (MLLMs) have shown strong performance in document image tasks, especially Optical Character Recognition (OCR). However, they struggle with Document Image Machine Translation (DIMT), which requires handling both cross-modal and cross-lingual challenges.
Approach:	They propose a novel fine-tuning paradigm that allows the model to generate OCR text before producing translation text, which allows it to leverage its strong monolingual OCR ability while learning to translate text across languages.
Outcome:	The proposed model can leverage its strong monolingual OCR ability while learning to translate text across languages.

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Xu Zhang, Zejie Liu, Yanzheng Xiang, Deyu Zhou

Multi-modal Multi-label Emotion Detection with Modality and Label Dependence (2020.emnlp-main)

Challenge:	Existing frameworks for text classification employing pre-trained models are constrained by the difficulty of the task.
Approach:	They propose a framework which implements a two-stage training strategy to fully exploit the knowledge in pre-trained models.
Outcome:	The proposed framework outperforms state-of-the-art classification models on six text classification corpora.

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Dong Zhang, Xincheng Ju, Junhui Li, Shoushan Li, Qiaoming Zhu, Guodong Zhou

Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld’s Episode Theory (2025.emnlp-main)

Challenge:	Existing studies on multi-label emotion detection focus on one modality . current studies focus on label dependence, but there is no consensus on the model .
Approach:	They propose a multi-modal sequence-to-set approach to model label dependence and modality dependence in a multiple-modal scenario.
Outcome:	The proposed approach is able to model the label dependence and the modality dependence in a multi-modal scenario.

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Ming Li, Nan Zhang, Chenrui Fan, Hong Jiao, Yanbin Fu, Sydney Peters, Qingshu Xu, Robert Lissitz, Tianyi Zhou

Linguistics & Cognitive NLP

Challenge:	Large Reasoning Models (LRMs) generate extensive chain-of-thought reasoning, but we lack a principled framework for understanding how these thoughts are structured.
Approach:	They propose a method to analyze the reasoning traces of Large Reasoning Models using Schoenfeld’s Episode Theory.
Outcome:	The proposed framework provides a theoretically grounded methodology for interpreting LRM cognition and enables future work on more controllable and transparent reasoning systems.

SENT: Sentence-level Distant Relation Extraction via Negative Training (2021.acl-long)

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Ruotian Ma, Tao Gui, Linyang Li, Qi Zhang, Xuanjing Huang, Yaqian Zhou

SQLAgent: Learning to Explore Before Generating as a Data Engineer (2026.findings-acl)

Challenge:	Existing methods for relation extraction use bag labels, which introduce noise, to train the model.
Approach:	They propose to use negative training to train a model using complementary labels to separate the noisy data from the training data.
Outcome:	The proposed method improves on previous methods on sentence-level evaluation and de-noise effect.

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Wenjia Jiang, Yiwei Wang, Boyan Han, Joey Tianyi Zhou, Chi Zhang

DopplerBAS: Binaural Audio Synthesis Addressing Doppler Effect (2023.findings-acl)

Challenge:	Existing approaches to large language models fail to generalize in complex, real-world settings due to database-specific nature of SQL reasoning.
Approach:	They propose a two-stage LLM-based framework that decouples knowledge acquisition from query generation.
Outcome:	The proposed framework significantly improves accuracy over baselines on large-scale benchmarks.

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Jinglin Liu, Zhenhui Ye, Qian Chen, Siqi Zheng, Wen Wang, Zhang Qinglin, Zhou Zhao

SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition (2022.coling-1)

Challenge:	Existing methods for binaural audio synthesis are limited in phase estimation, which is crucial for spatial hearing.
Approach:	They propose a method to explicitly address the Doppler effect of the moving speaker . it calculates the radial relative velocity of the speaker in spherical coordinates .
Outcome:	The proposed method improves the representative WarpNet and BinauralGrad backbones in phase error metric and reaches a new state of the art (SOTA) it is compared with the current method which is limited in phase estimation .

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Zeng Yang, Linhai Zhang, Deyu Zhou

Preference Curriculum: LLMs Should Always Be Pretrained on Their Preferred Data (2025.findings-acl)

Challenge:	Existing few-shot named entity recognition methods focus on leveraging existing datasets in the rich-resource domains which might fail in training-from-scratch setting.
Approach:	They propose a multi-task learning framework for Few-shot named entity recognition without using source domain data.
Outcome:	The proposed framework outperforms state-of-the-art few-shot named entity recognition methods on a training-from-scratch dataset.

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Xuemiao Zhang, Xu Liangyu, Feiyu Duan, Yongwei Zhou, Sirui Wang, Rongxiang Weng, Jingang Wang, Xunliang Cai

Basic Reading Distillation (2025.acl-long)

Challenge:	Existing methods of uniformly sampling data throughout the pretraining process are suboptimal because they overlook the model's evolving data preferences.
Approach:	They propose a Perplexity Difference (PD) based Preference Curriculum learning framework which perceives and uses the data preferred by LLMs as their capabilities improve . they propose PDPC to complete the arrangement of the dataset offline and ensure continuous training without interruption.
Outcome:	The proposed framework surpasses baselines on 1.3B and 3B models and achieves an increased average accuracy of over 8.1% across MMLU and CMMLU.

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Zhi Zhou, Sirui Miao, Xiangyu Duan, Hao Yang, Min Zhang

DiffPO: Diffusion-styled Preference Optimization for Inference Time Alignment of Large Language Models (2025.acl-long)

Challenge:	Large language models require high computational resources which limits their deployment in real-world applications.
Approach:	They propose to distill large language models into smaller language models by either knowledge distillation or task distillation.
Outcome:	The proposed model outperforms or performs comparable to over 20x bigger LLMs on language inference benchmarks and BIG-bench tasks.

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Ruizhe Chen, Wenhao Chai, Zhifei Yang, Xiaotian Zhang, Ziyang Wang, Tony Quek, Joey Tianyi Zhou, Soujanya Poria, Zuozhu Liu

Evo-Attacker: Memory-Augmented Reinforcement Learning for Long-Horizon Tool Attacks on LLM-MAS (2026.acl-long)

Challenge:	Inference-time alignment approaches still face limitations due to policy-specific value functions and latency during the inference phase.
Approach:	They propose an efficient and policy-agnostic preference optimization method that avoids time latency associated with token generation.
Outcome:	The proposed method achieves a favorable trade-off between alignment quality and inference-time latency.

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Bingyu Yan, Xiaoming Zhang, JinYu Hou, Chaozhuo Li, Ziyi Zhou, Yiming Hei, Litian Zhang

SURf: Teaching Large Vision-Language Models to Selectively Utilize Retrieved Information (2024.emnlp-main)

Challenge:	Existing tool attacks are limited by domain specificity or fixed and static templates.
Approach:	They propose an attack-based memory-augmented reinforcement learning process that constructs a dynamic attack memory and employs deliberative reasoning to retrieve adversarial patterns.
Outcome:	Evo-Attacker outperforms baselines in the long-horizon credit assignment challenge.

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Jiashuo Sun, Jihai Zhang, Yucheng Zhou, Zhaochen Su, Xiaoye Qu, Yu Cheng

Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)

Challenge:	Existing studies focus on the text modality or are limited to specific tasks.
Approach:	They propose a framework to teach Large Vision-Language Models to selectively utilize retrieved information and improve their robustness against irrelevant or misleading references.
Outcome:	The proposed framework improves LVLMs’ ability to utilize retrieved multimodal references and their robustness against irrelevant or misleading information.

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Dingxin Hu, Xuanyu Zhang, Xingyue Zhang, Yiyang Li, Dongsheng Chen, Marina Litvak, Natalia Vanetik, Qing Yang, Dongliang Xu, Yanquan Zhou, Lei Li, Yuze Li, Yingqi Zhu

Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants’ Question-Answering in Asynchronous Learning Environments (2025.findings-emnlp)

Challenge:	Abstractive summarization models suffer from factual inconsistency problem . post-editing methods focus on replacing suspicious entities, failing to modify incorrect content hidden in sentence structures.
Approach:	They propose to use sentence pruning operation to correct possible errors . they propose to apply sentence pruning operations to the syntactic dependency tree .
Outcome:	The proposed method improves factual consistency on the FRANK dataset compared with baselines . it is model-independent and can serve as the final step in ensuring factual consistentness.

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Li Siyan, Zhen Xu, Vethavikashini Chithrra Raghuram, Xuanming Zhang, Renzhe Yu, Zhou Yu

An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis (2021.findings-emnlp)

Challenge:	Existing assessments rely on surface-level metrics and lack sufficient grounding in educational theory . a new framework is proposed to evaluate VTAs in asynchronous learning environments .
Approach:	They propose a pedagogically-oriented evaluation framework tailored to asynchronous forum discussions . they construct classifiers using expert annotations of VTA responses on a diverse set of forum posts .
Outcome:	The proposed evaluation framework is rooted in learning sciences and tailored to asynchronous forum discussions.

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Yunlong Liang, Fandong Meng, Jinchao Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)

Challenge:	Existing approaches to Aspect-based sentiment analysis do not exploit the interactive relations among subtasks and do not utilize document-level labeled domain/sentiment knowledge, which restricts their performance.
Approach:	They propose an iterative multi-knowledge transfer network for end-to-end ABSA that leverages the inter-task interaction between subtasks.
Outcome:	The proposed approach improves on three 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

Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models (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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Zhiwei He, Binglin Zhou, Hongkun Hao, Aiwei Liu, Xing Wang, Zhaopeng Tu, Zhuosheng Zhang, Rui Wang

ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval (2025.emnlp-main)

Challenge:	Existing text watermarking technologies lack consistency when texts are translated into different languages.
Approach:	They propose a cross-lingual watermark removal attack to bypass watermarking by first obtaining a response from an LLM in a pivot language and then translating it into the target language.
Outcome:	The proposed method can remove watermarks without performance loss by obtaining a response from an LLM in a pivot language and then translating it into the target language.

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Zihan Chen, Lei Shi, Weize Wu, Qiji Zhou, Yue Zhang

Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)

Challenge:	Large language models (LLMs) are increasingly used to solve the entity recognition task.
Approach:	They propose a framework to select the most informative and representative samples for LLM in-context learning.
Outcome:	The proposed framework outperforms baselines on three specialized domain datasets.

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Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang

Noise Robust Named Entity Understanding for Voice Assistants (2021.naacl-industry)

Challenge:	Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing.
Approach:	They propose a cross-lingual conversation summarization benchmark that explicitly considers source context.
Outcome:	The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations.

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Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D Williams, Lin Li

Explainable Recommendation with Personalized Review Retrieval and Aspect Learning (2023.acl-long)

Challenge:	Named Entity Recognition and Entity Linking are challenging for voice assistants . utterances are relatively short, so there is not much context to help disambiguate .
Approach:	They propose a Named Entity Understanding system that combines NER and EL in a joint reranking module.
Outcome:	The proposed framework improves NER accuracy by up to 3.13% and EL accuracy by 3.6% in F1 score . it also leads to better accuracies in other natural language understanding tasks .

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Hao Cheng, Shuo Wang, Wensheng Lu, Wei Zhang, Mingyang Zhou, Kezhong Lu, Hao Liao

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)

Challenge:	Recent years have witnessed a growing interest in the development of explainable recommendation models.
Approach:	They propose a model that combines prediction and generation tasks to produce more persuasive explanations by obtaining additional information from the training sets.
Outcome:	The proposed model outperforms state-of-the-art models on three datasets and shows that it is more persuasive than previous models.

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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu

Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language (C18-1)

Challenge:	Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering.
Approach:	They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow.
Outcome:	The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow.

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He Bai, Yu Zhou, Jiajun Zhang, Liang Zhao, Mei-Yuh Hwang, Chengqing Zong

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)

Challenge:	a study aims to develop a language transferring system to avoid the trouble of acquiring and labeling a new big SLU corpus . general-purpose translators cannot handle the lot of semantic labels, not to mention cultural differences . a RL-based language transfer method can be used to adapt the adapted translator to a target language .
Approach:	They propose to use reinforcement learning to adapt a spoken language understanding model to a target language.
Outcome:	The proposed language transferring method improves domain classification accuracy by 22% compared with naive translation . the proposed language transfer method can be used on Chinese to English translators with more proper slot tags .

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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si

LLaMA-Berry: Pairwise Optimization for Olympiad-level Mathematical Reasoning via O1-like Monte Carlo Tree Search (2025.naacl-long)

Challenge:	Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment.
Approach:	They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives.
Outcome:	The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering.

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Di Zhang, Jianbo Wu, Jingdi Lei, Tong Che, Jiatong Li, Tong Xie, Xiaoshui Huang, Shufei Zhang, Marco Pavone, Yuqiang Li, Wanli Ouyang, Dongzhan Zhou

ConFit v2: Improving Resume-Job Matching using Hypothetical Resume Embedding and Runner-Up Hard-Negative Mining (2025.findings-acl)

Challenge:	LLaMA-Berry is an advanced mathematical reasoning framework to enhance the problem-solving ability of large language models (LLMs).
Approach:	They propose a Monte Carlo Tree Search and Self-Refine framework to optimize reasoning paths and a pairwise reward model to evaluate different paths globally.
Outcome:	The proposed framework overcomes inefficiencies and limitations of step-wise and greedy search algorithms, enabling more efficient exploration of solution spaces.

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Xiao Yu, Ruize Xu, Chengyuan Xue, Jinzhong Zhang, Xu Ma, Zhou Yu

GUI Agents: A Survey (2025.findings-acl)

Challenge:	Existing methods to model resume-job fit are sparse since job seekers apply to only a few jobs.
Approach:	They propose two techniques to enhance the encoder’s contrastive training process by augmenting job data with hypothetical reference resume generated by a large language model and creating high-quality hard negatives from unlabeled resume/job pairs using a novel hard-negative mining strategy.
Outcome:	The proposed method outperforms ConFit and prior methods on two real-world datasets and achieves an average improvement of 13.8% in recall and 17.5% in nDCG across job-ranking and resume-ranker tasks.

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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.

Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing (2026.acl-long)

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Lingkun Long, Yushi Huang, Shihao Bai, Ruihao Gong, Jun Zhang, Ao Zhou, Jianlei Yang

DTELS: Towards Dynamic Granularity of Timeline Summarization (2025.naacl-long)

Challenge:	Existing methods for estimating attention importance for tokens are ineffective . dLLMs require bidirectional attention, which limits inference efficiency .
Approach:	They propose a training-free attention sparsification framework for efficient long-context inference . they propose 'sink-aware pruning strategy' to accurately estimate and remove redundant computation .
Outcome:	The proposed approach offers 29 lossless speedup under 32K context length.

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Chenlong Zhang, Tong Zhou, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao

Summarization

Challenge:	Existing timeline summarizations lack flexibility to meet diverse granularity needs . a fine-grained timeline showing the technical details is preferred for news topics .
Approach:	They propose a new paradigm to construct adaptive timelines based on user instructions or requirements.
Outcome:	The proposed timelines are informative and granularly consistent, but they struggle to generate consistent timelines.

SMR: State Memory Replay for Long Sequence Modeling (2024.findings-acl)

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Biqing Qi, Junqi Gao, Kaiyan Zhang, Dong Li, Jianxing Liu, Ligang Wu, Bowen Zhou

Challenge:	Existing state space models (SSMs) address non-uniform sampling, but their recursive structures impede efficient SSM computation via convolution.
Approach:	They propose a plug-and-play mechanism to solve the Non-Stable State problem by adjusting input sequences with early memories.
Outcome:	The proposed method overcomes the non-uniform sample processing problem . it can achieve Sampling Step Adaptation (SSA) by adjusting input sequences with early memories.

L-CiteEval: A Suite for Evaluating Fidelity of Long-context Models (2025.acl-long)

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Zecheng Tang, Keyan Zhou, Juntao Li, Baibei Ji, Jianye Hou, Min Zhang

Challenge:	Long-context models (LCMs) have seen remarkable advancements in recent years, facilitating tasks like long-document QA.
Approach:	They propose an out-of-the-box suite that can assess both generation quality and fidelity in long-context understanding tasks.
Outcome:	The proposed suite can assess both generation quality and fidelity in long-context understanding tasks.

A Corpus for Reasoning about Natural Language Grounded in Photographs (P19-1)

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Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, Yoav Artzi

Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling (2025.acl-long)

Challenge:	a dataset for visual reasoning with natural language and images is available.
Approach:	They propose a dataset for joint reasoning about natural language and images . they crowdsource 107,292 examples of English sentences paired with web photographs .
Outcome:	The proposed dataset combines 107,292 examples of English sentences with web photographs . Qualitative analysis shows the data requires compositional joint reasoning .

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Yang Yuguang, Yu Pan, Jixun Yao, Xiang Zhang, Jianhao Ye, Hongbin Zhou, Lei Xie, Lei Ma, Jianjun Zhao

Towards Building More Robust NER datasets: An Empirical Study on NER Dataset Bias from a Dataset Difficulty View (2023.emnlp-main)

Challenge:	Expressive zero-shot voice conversion (VC) aims to modify source timbre to match unseen speaker . existing zero- shot VC systems struggle to reproduce paralinguistic information in highly expressive speech .
Approach:	They propose a framework for expressive zero-shot voice conversion that uses hybrid content encoding and memory-augmented context-aware timbre modeling.
Outcome:	The proposed framework surpasses state-of-the-art VC systems in speech naturalness, speaker similarity, and speaker similarness.

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Ruotian Ma, Xiaolei Wang, Xin Zhou, Qi Zhang, Xuanjing Huang

StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback (2024.acl-long)

Challenge:	Named Entity Recognition (NER) models rely on superficial entity patterns for predictions, without considering evidence from the context.
Approach:	They propose to de-bias NER datasets by altering entity-context distribution . they also validate the feasibility of the proposed de-bianking techniques .
Outcome:	The proposed methods can be applied to different models and improve existing models.

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Shihan Dou, Yan Liu, Haoxiang Jia, Enyu Zhou, Limao Xiong, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang

Unlocking Exploration in RLVR: Uncertainty-aware Advantage Shaping for Deeper Reasoning (2026.findings-acl)

Challenge:	Existing work integrates reinforcement learning with compiler feedback to enhance code generation quality but the long code generated by LLMs makes RL exploration ineffective.
Approach:	They propose a framework that integrates reinforcement learning and compiler feedback to enhance code generation quality.
Outcome:	The proposed framework outperforms state-of-the-art approaches in corresponding benchmarks and integrates reinforcement learning with compiler feedback to improve code generation quality.

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Can Xie, Ruotong Pan, Xiangyu Wu, Zhang Yunfei, Jiayi Fu, Tingting Gao, Guorui Zhou

LogiCoT: Logical Chain-of-Thought Instruction Tuning (2023.findings-emnlp)

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) has shown significant promise for enhancing the reasoning capabilities of large language models (LLMs).
Approach:	They propose a model-free method that refines credit assignment by leveraging the model's internal uncertainty signals.
Outcome:	Extensive experiments on five mathematical reasoning benchmarks show that the proposed method outperforms strong RLVR baselines on multiple model scales, including 1.5B and 7B.

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Hanmeng Liu, Zhiyang Teng, Leyang Cui, Chaoli Zhang, Qiji Zhou, Yue Zhang

Aligning Large Language Models for Controllable Recommendations (2024.acl-long)

Challenge:	Recent work on self-instruction tuning has focused on enhancing the general proficiency of models.
Approach:	They propose a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4 that harvests instructions for prompting GPT to generate chain-of thought rationales.
Outcome:	The proposed dataset enables the model to generate chain-of-thought rationales with GPT-4.

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Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, Xing Xie

Multi-modal Action Chain Abductive Reasoning (2023.acl-long)

Challenge:	Existing literature focuses on integrating domain-specific knowledge into LLMs to enhance accuracy using a fixed task template.
Approach:	They propose a collection of supervised learning tasks augmented with labels derived from a conventional recommender model to improve LLMs’ proficiency in adhering to recommendation-specific instructions.
Outcome:	The proposed approach significantly improves the capability of LLMs to respond to instructions within recommender systems, reducing formatting errors while maintaining a high level of accuracy.

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Mengze Li, Tianbao Wang, Jiahe Xu, Kairong Han, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Shiliang Pu, Fei Wu

Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling (2025.acl-long)

Challenge:	Existing models for Abductive Reasoning are limited in their ability to infer the most plausible explanation of incomplete known phenomena.
Approach:	They propose a vision-language task that aims to imagine the most plausible event by spatio-temporal grounding in past video and infer the hypothesis of subsequent action chain layer by layer.
Outcome:	The proposed model outperforms existing video-language models in terms of effectiveness on the proposed dataset.

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Jiayi Zeng, Yizhe Feng, Mengliang He, Wenhui Lei, Wei Zhang, Zeming Liu, Xiaoming Shi, Aimin Zhou

CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)

Challenge:	Current error-handling works are performed in a passive manner, with explicit error- handling instructions.
Approach:	They propose a new benchmark to analyze LLMs' performance on a mis-prompt benchmark and a dataset to promote further research.
Outcome:	The proposed benchmark shows that current LLMs show poor performance on proactive error handling, and that SFT improves on error handling instances.

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Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang

Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering over Knowledge Graph (2021.naacl-main)

Challenge:	Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions.
Approach:	They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges.
Outcome:	Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4.

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Yucheng Zhou, Xiubo Geng, Tao Shen, Wenqiang Zhang, Daxin Jiang

Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (2021.acl-long)

Challenge:	Existing approaches to solve multilingual question answering over knowledge graph (KGQA) use of bilingual lexicon induction to map training questions into those in target language circumvents language inconsistency .
Approach:	They propose to use bilingual lexicon induction to map training questions in source and target languages as augmented training data to minimize syntax-disorder.
Outcome:	The proposed model narrows the gap in zero-shot cross-lingual transfer between source and target languages.

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Linmei Hu, Tianchi Yang, Luhao Zhang, Wanjun Zhong, Duyu Tang, Chuan Shi, Nan Duan, Ming Zhou

Ethical NLP

Challenge:	Existing methods for fake news detection rely on linguistic and semantic features from news content and do not exploit external knowledge.
Approach:	They propose a graph neural model which compares news to knowledge base through entities for fake news detection.
Outcome:	The proposed model significantly outperforms state-of-the-art methods on two benchmark datasets.

AMR Parsing with Latent Structural Information (2020.acl-main)

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Qiji Zhou, Yue Zhang, Donghong Ji, Hao Tang

Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models (2025.findings-acl)

Challenge:	Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences.
Approach:	They investigate parsing AMR with explicit dependency structures and interpretable latent structures.
Outcome:	The proposed model achieves best results on both AMR 2.0 and AMR 1.0 . the proposed model has been adopted in downstream NLP tasks, including text summarization and question answering.

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Yanyue Zhang, Yulan He, Deyu Zhou

Summarization

Challenge:	Recent studies show that large language models can achieve stateof-the-art performance on standard summarization benchmarks without the need for large-scale training data.
Approach:	They propose a personalized opinion summarization framework via LLM-based role-playing to better understand the user's personalized needs.
Outcome:	The proposed framework can improve the level of personalization in large model-generated summaries by taking into account user characteristics and interests while summarizing multiple product reviews.

A Relation-Oriented Clustering Method for Open Relation Extraction (2021.emnlp-main)

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Jun Zhao, Tao Gui, Qi Zhang, Yaqian Zhou

Logic2Text: High-Fidelity Natural Language Generation from Logical Forms (2020.findings-emnlp)

Challenge:	Existing methods for open relation extraction (OpenRE) are designed for predefined relations, which cannot deal with new emerging relations in the real world.
Approach:	They propose a relation-oriented clustering model that leverages readily available labeled data to learn a relationship-oriented representation.
Outcome:	The proposed model reduces error rate by 29.2% and 15.7% on two datasets compared with current SOTA methods.

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Zhiyu Chen, Wenhu Chen, Hanwen Zha, Xiyou Zhou, Yunkai Zhang, Sairam Sundaresan, William Yang Wang

CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model (2023.emnlp-main)

Challenge:	Recent studies on Natural Language Generation (NLG) from structured data focus on surface descriptions of simple record sequences, for example, attribute-value pairs of fixed or very limited schema.
Approach:	They propose to use a large-scale dataset to generate NLG from logical forms to obtain controllable and faithful generations from structured data.
Outcome:	The proposed model can describe interesting facts from logical inferences across records, but it is difficult to produce such fidelity.

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Kaiyan Zhang, Ning Ding, Biqing Qi, Xuekai Zhu, Xinwei Long, Bowen Zhou

Mitigating Hallucinations in LM-Based TTS Models via Distribution Alignment Using GFlowNets (2025.emnlp-main)

Challenge:	Instruction tuning is an effective way of aligning large language models with private instruction data.
Approach:	They propose a training-free strategy to derive improved emulators from LLMs by using Offsite-Tuning (OFT) they propose CRaSh, which transfers transformer blocks between centralized LLM and downstream emulators .
Outcome:	The proposed technique boosts performance of large language models with billions of parameters.

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Chenlin Liu, Minghui Fang, Patrick Zhang, Wei Zhou, Jie Gao, Jiqing Han

A Critical Analysis of Document Out-of-Distribution Detection (2023.findings-emnlp)

Challenge:	Existing mitigation strategies for Text-to-Speech systems require excessive training resources or inference latency.
Approach:	They propose a GFlOwNet-guided distribution AlignmenT framework that mitigates hallucinations without relying on massive resources or inference latency.
Outcome:	The proposed framework reduces over 50% character error rates and lowers uncertainty by up to 58% on challenging test cases.

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Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova

Unlocking Speech Instruction Data Potential with Query Rewriting (2025.findings-acl)

Challenge:	Existing document understanding models focus on single-modal inputs such as images or texts.
Approach:	They propose to use a spatial-aware adapter to adapt transformer-based language models to document domain to exploit multi-modal information.
Outcome:	The proposed model significantly improves the OOD detection performance compared to using a standard language model and to competitive baselines.

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Yonghua Hei, Yibo Yan, Shuliang Liu, Huiyu Zhou, Linfeng Zhang, Xuming Hu

A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge (2022.findings-acl)

Challenge:	Existing LLMs lack datasets and biased training tasks to follow speech instructions.
Approach:	They propose a query rewriting framework that uses multiple agents to annotate and validate the synthesized speech.
Outcome:	The proposed framework can transform text instructions into distributions more suitable for TTS models for speech synthesis without human annotation.

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Tao Wang, Linhai Zhang, Chenchen Ye, Junxi Liu, Deyu Zhou

Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments (2022.coling-1)

Challenge:	Existing methods to predict medical codes from clinical notes lack interpretability due to lengthy and noisy clinical notes.
Approach:	They propose a framework based on medical concept driven attention to integrate external knowledge for explainable medical code prediction from clinical notes.
Outcome:	The proposed framework outperforms state-of-the-art methods on a benchmark dataset showing that it is more accurate than existing methods.

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Yu Zhang, Qingrong Xia, Shilin Zhou, Yong Jiang, Guohong Fu, Min Zhang

The Curse of Verbalization: How Presentation Order Constrains LLM Reasoning (2026.findings-eacl)

Challenge:	Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based.
Approach:	They propose to regard flat argument spans as latent subtrees, thus reducing SRL to a tree parsing task.
Outcome:	The proposed model performs better than previous syntax-agnostic models on CoNLL05 and CoNll12 benchmarks.

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Yue Zhou, Henry Peng Zou, Barbara Di Eugenio, Yang Zhang

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)

Challenge:	Xu et al., 2025) found that LLMs struggle when programs execute in an unaligned order.
Approach:	They propose to use esoteric programming languages to evaluate LLMs' reasoning abilities.
Outcome:	The proposed model improves reasoning performance across state-of-the-art models by restructuring problems to align the presentation order with the order of utilization.

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Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei

Mapping Natural Language Instructions to Mobile UI Action Sequences (2020.acl-main)

Challenge:	Existing work shows that pre-trained models can improve in various natural language processing tasks.
Approach:	They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data.
Outcome:	The proposed framework is superior to existing models on speech-to-text processing tasks.

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Yang Li, Jiacong He, Xin Zhou, Yuan Zhang, Jason Baldridge

Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers (2026.findings-acl)

Challenge:	a new problem of grounding natural language instructions to mobile UI actions is emerging . we use a Transformer to extract action phrase tuples from long-range natural language instruction .
Approach:	They propose a dataset that pairs English instructions with actions performed by people on a mobile UI emulator.
Outcome:	The proposed model achieves 70.59% accuracy on predicting complete ground-truth action sequences in PixelHelp.

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Zhiyang Zhang, Junda She, Kuo Cai, Bo Chen, Shiyao Wang, Xinchen Luo, Qiang Luo, Ruiming Tang, Han Li, Kun Gai, Guorui Zhou

Learning to Collaborate for Question Answering and Asking (N18-1)

Challenge:	Existing methods for constructing item identifiers face bottlenecks due to their large output space and expensive vocabulary expansion and alignment training.
Approach:	They propose to use Large Language Models to develop general-purpose, semantically-aware recommender systems that can be generalized and reusable.
Outcome:	Experiments on real-world datasets show that GRAM outperforms baselines and significantly outperformed baselines.

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Duyu Tang, Nan Duan, Zhao Yan, Zhirui Zhang, Yibo Sun, Shujie Liu, Yuanhua Lv, Ming Zhou

RealBehavior: A Framework for Faithfully Characterizing Foundation Models’ Human-like Behavior Mechanisms (2023.findings-emnlp)

Challenge:	Question answering (QA) and question generation (QG) are closely related tasks.
Approach:	They propose a training algorithm that generalizes both Generative Adversarial Network and Generating Domain-Adaptive Nets under the question answering scenario.
Outcome:	The proposed training algorithm generalizes both Generative Adversarial Network (GAN) and Generating Domain-Adaptive Nets (GDAN) under the question answering scenario.

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Enyu Zhou, Rui Zheng, Zhiheng Xi, Songyang Gao, Xiaoran Fan, Zichu Fei, Jingting Ye, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Existing studies on human-like behaviors in foundation models do not verify their faithfulness . a simple application of psychological tools cannot faithfully characterize all human-type behaviors .
Approach:	They propose a framework to characterize humanoid behaviors in foundation models . they argue that a simple application of psychological tools cannot faithfully characterize all human-like behaviors .
Outcome:	The proposed framework assesses the faithfulness of results based on reproducibility, internal consistency, and generalizability.

UrbanGeoEval: A City-Scale Benchmark for Evaluating Large Language Models in Geospatial Reasoning (2026.acl-long)

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Mutian Bao, Qiuyi Qi, Tian Liang, Jinjian Zhang, Wei Zhou, Ming Kong, Linjian Mo, Qiang Zhu

Understanding Translationese in Cross-Lingual Summarization (2023.findings-emnlp)

Challenge:	Extensive experiments on 18 widely used LLMs uncover critical insights: (1) models exhibit severe geographic biases and resolution gaps; (2) failures in complex multi-hop tasks stem from brittle foundational spatial skills rather than high-level logic deficits.
Approach:	They propose a dual-module framework that disentangles factual recall and spatial logic from the model's real capabilities in urban environments.
Outcome:	Extensive tests on 18 widely used LLMs reveal that models exhibit severe geographic biases and resolution gaps, and failures in complex multi-hop tasks often stem from brittle foundational spatial skills rather than high-level logic deficits.

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Jiaan Wang, Fandong Meng, Yunlong Liang, Tingyi Zhang, Jiarong Xu, Zhixu Li, Jie Zhou

MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification (2025.acl-long)

Challenge:	Existing datasets involve translation, but translationese is distinguished from original text . previous studies have shown that translationeses in CLS are not a problem in training sets .
Approach:	They propose to use cross-lingual summarization to generate a concise summary in a target language from a document in . existing datasets typically involve translation in their creation, but the translated text is distinguished from the original written in that language.
Outcome:	The proposed method systematically investigates how translationese affects CLS model evaluation and performance when it appears in source documents or target summaries.

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Linzhuang Sun, Hao Liang, Jingxuan Wei, Bihui Yu, Tianpeng Li, Fan Yang, Zenan Zhou, Wentao Zhang

BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents (2026.findings-acl)

Challenge:	MM-Verifier and MM Reasoner are a powerful multimodal reasoning model . large language models (LLMs) have demonstrated exceptional performance across tasks spanning myriad domains.
Approach:	They propose a method which combines tree search and verification to generate high-quality chain-of-thought data.
Outcome:	The proposed method outperforms all larger models on the MathCheck, MathVista, and MathVerse benchmarks.

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Litu Ou, Kuan Li, Huifeng Yin, Liwen Zhang, Zhongwang Zhang, Xixi Wu, Rui Ye, Zile Qiao, Yong Jiang, Pengjun Xie, Fei Huang, Jingren Zhou

Challenge:	Existing work on confidence in LLMs is limited.
Approach:	They propose to use confidence scores to determine model answer quality and encourage model to try again until it reaches satisfactory confidence level.
Outcome:	The proposed methods significantly reduce token consumption while demonstrating competitive performance compared to baseline fixed budget methods.

Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models (2021.eacl-main)

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Qingyang Wu, Yichi Zhang, Yu Li, Zhou Yu

LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks.
Approach:	They propose a framework that uses pre-trained language to model each speaker separately . it can be generalized to more challenging, non-collaborative tasks such as persuasion .
Outcome:	The proposed framework outperforms or is on par with state-of-the-art methods on two popular datasets: CamRest676 and MultiWOZ.

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Yilin Xiao, Jin Chen, Qinggang Zhang, Yujing Zhang, Chuang Zhou, Longhao Yang, Lingfei Ren, Xin Yang, Xiao Huang

R-CHAR: A Metacognition-Driven Framework for Role-Playing in Large Language Models (2025.emnlp-main)

Challenge:	Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) however, traditional RAG attacks are difficult to pose an effective threat to GraphRAg systems.
Approach:	They propose a novel attack framework that targets logical reasoning rather than injecting false contents into GraphRAG systems by grounding their responses in structured knowledge graphs.
Outcome:	The proposed framework outperforms state-of-the-art attacks on GraphRAG systems in both effectiveness and stealth.

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Haiming Qin, Jiwei Zhang, Wei Zhang, KeZhong Lu, Mingyang Zhou, Hao Liao, Rui Mao

An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization (2021.acl-long)

Challenge:	Existing role-playing structures lack cognitive consistency in complex scenarios . Existing models excel in math and coding tasks but lack coherent reasoning .
Approach:	They propose a metacognition-driven framework that enhances role-playing performance . experimental results show performance improvements across varying scenario complexities .
Outcome:	The proposed framework outperforms existing models in social intelligence tasks and shows strength in long-context comprehension and group-level social interactions.

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Baohang Zhou, Xiangrui Cai, Ying Zhang, Xiaojie Yuan

A Training-free LLM-based Approach to General Chinese Character Error Correction (2025.acl-long)

Challenge:	Existing models for medical named entity recognition and named entity normalization suffer from error propagation between the two tasks.
Approach:	They propose an end-to-end progressive multi-task learning model for jointly modeling medical named entity recognition and normalization in an effective way.
Outcome:	The proposed model reduces error propagation by exploiting the learnable features for both tasks to improve performance.

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Houquan Zhou, Bo Zhang, Zhenghua Li, Ming Yan, Min Zhang

Learning Query Adaptive Anchor Representation for Inductive Relation Prediction (2023.findings-acl)

Challenge:	Chinese spelling correction (CSC) is a crucial task that aims to correct character errors in text.
Approach:	They propose a task that handles missing and redundant characters and an additional prompt-based large language model to improve performance.
Outcome:	The proposed task is based on a high-quality dataset and a prompt-based large language model.

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Zhiwen Xie, Yi Zhang, Jin Liu, Guangyou Zhou, Jimmy Huang

From Chaotic OCR Words to Coherent Document: A Fine-to-Coarse Zoom-Out Network for Complex-Layout Document Image Translation (2025.coling-main)

Challenge:	Existing methods to infer the missing links between entities are limited to the transductive setting . Query Adaptive Anchor Representation (QAAR) model is based on entity-independent features .
Approach:	They propose a query adaptive anchor representation model which extracts one opening subgraph and performs reasoning by one time for all candidate triples.
Outcome:	The proposed model outperforms state-of-the-art models in relation prediction task.

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Zhiyang Zhang, Yaping Zhang, Yupu Liang, Lu Xiang, Yang Zhao, Yu Zhou, Chengqing Zong

Chinese Court Simulation with LLM-Based Agents System (2026.findings-acl)

Challenge:	Document Image Translation (DIT) aims to translate documents in images from one language to another.
Approach:	They propose a novel end-to-end network called Zoom-out DIT to improve document translation by combining word positioning, sentence recognition and document organization.
Outcome:	The proposed network improves word positioning, sentence recognition and document organization, and improves translation quality.

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Kaiyuan Zhang, Jiaqi Li, Yueyue Wu, Haitao Li, Cheng Luo, Shaokun Zou, Yujia Zhou, Weihang Su, Yiqun Liu, Qingyao Ai

Event-enhanced Retrieval in Real-time Search (2024.lrec-main)

Challenge:	Existing studies have neglected the systematic design and procedure evaluation of court simulations, which are critical to the credibility and usage of court simulators in practice.
Approach:	They propose a court simulation paradigm based on the real-world procedure structure of Chinese courts and a framework that focuses on both legal judgment prediction and court procedure analysis.
Outcome:	The proposed model outperforms judges and lawyers from the real trials in many aspects.

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Yanan Zhang, Xiaoling Bai, Tianhua Zhou

ShieldHead: Decoding-time Safeguard for Large Language Models (2025.findings-acl)

Challenge:	Existing embedding-based retrieval models face the "semantic drift" problem . a low adoption rate of retrieval results is evident in real-time search scenarios .
Approach:	They propose an embedding-based retrieval approach that enhances real-time retrieval performance by adding contrastive learning to the dual-encoder model.
Outcome:	The proposed approach improves the dual-encoder model of traditional EBR.

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Zitao Xuan, Xiaofeng Mao, Da Chen, Xin Zhang, Yuhan Dong, Jun Zhou

VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training (2026.acl-long)

Challenge:	Recent advances in LLM-based moderation methods have demonstrated remarkable promise in identifying safety risks associated with both inputs and outputs in human-AI interactions.
Approach:	They propose to learn a classification head on the last-layer hidden states of a dialogue model and use it to detect harmful content.
Outcome:	The proposed framework is 300 faster (1ms) than previous LLM-based moderation models with 99% less parameters than LlamaGuard.

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Dingwei Zhu, Shihan Dou, Zhiheng Xi, Senjie Jin, Guoqiang Zhang, Jiazheng Zhang, Junjie Ye, Mingxu Chai, Enyu Zhou, Ming Zhang, Yuhui Wang, Caishuang Huang, Chenhao Huang, Yunke Zhang, Yuran Wang, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang

Mixture of Attention Heads: Selecting Attention Heads Per Token (2022.emnlp-main)

Challenge:	Reinforcement Learning (RL) in real-world environments often suffers from ambiguous or incomplete supervision.
Approach:	They propose a framework that enhances value modeling for robust RL in LLM post-training by integrating auxiliary losses guided by entropy and perplexity from a frozen language model and variational information bottleneck.
Outcome:	The proposed framework outperforms baselines on multi-turn dialogue, math reasoning, and science QA with rule-based and model-based rewards.

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Xiaofeng Zhang, Yikang Shen, Zeyu Huang, Jie Zhou, Wenge Rong, Zhang Xiong

A Self-Denoising Model for Robust Few-Shot Relation Extraction (2025.acl-long)

Challenge:	Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing.
Approach:	They propose a new architecture that combines multi-head attention with the MoE mechanism and a sparsely gated architecture that allows for faster computations.
Outcome:	The proposed architecture can scale up the number of attention heads and the number parameters while preserving computational efficiency.

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Liang Zhang, Yang Zhang, Ziyao Lu, Fandong Meng, Jie Zhou, Jinsong Su

TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models (2025.acl-industry)

Challenge:	Existing studies assume that the support set contains only accurately labeled instances, but this assumption is often unrealistic.
Approach:	They propose a self-denoising model for FSRE which can automatically correct noisy labels of support instances.
Outcome:	The proposed model outperforms all baselines on two public datasets showing that it can correct mislabeled support instances.

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Deyin Yi, Yihao Liu, Lang Cao, Mengyu Zhou, Haoyu Dong, Shi Han, Dongmei Zhang

Multi-Grained Knowledge Distillation for Named Entity Recognition (2021.naacl-main)

Challenge:	Tabular data analysis is crucial in many scenarios, yet its complexity and density can make it challenging to determine the most appropriate analysis operations for a new table.
Approach:	They propose a tabular data analysis framework that recommends query-code-result triplets for new tables . they propose Rec-Align, a method to further improve recommendation quality .
Outcome:	The proposed framework achieves 77.0% top-5 recommendation recall on a dataset designed for tabular data analysis recommendation.

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Xuan Zhou, Xiao Zhang, Chenyang Tao, Junya Chen, Bing Xu, Wei Wang, Jing Xiao

RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL (2022.emnlp-main)

Challenge:	Pre-trained big models have delivered top performance in Seq2seq modeling, but their deployments in real-world applications are often hindered by excessive computations and memory demands.
Approach:	They propose a distillation scheme to efficiently transfer knowledge from big models to their cheaper counterparts.
Outcome:	The proposed scheme maximizes the assimilation of knowledge from the teacher model to the student model.

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Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin

OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)

Challenge:	Experimental results show RASAT can leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model.
Approach:	They propose a Transformer seq2seq architecture augmented with relation-aware self-attention that leverages relational structures while inheriting pretrained parameters from the T5 model.
Outcome:	The proposed model can leverage relational structures while inheriting pretrained parameters from the T5 model effectively.

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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu

Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation (2025.coling-main)

Challenge:	a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide.
Approach:	They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions .
Outcome:	The proposed agents are based on operating systems (OS) and operating systems frameworks.

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Caihong Mu, Keyang Zhang, Jialiang Zhou, Yi Liu

Demystifying Uncertainty in LLMs: Active Calibration between Concepts and Human Evaluations (2026.acl-long)

Challenge:	Existing contrastive learning-based methods struggle with data sparsity in real-world recommendations . Graph collaborative filtering incorporates contrastive training as an auxiliary task to improve performance .
Approach:	They propose a perturbation-driven dual auxiliary contrastive learning task for collaborative filtering . structure perturbation and weight perturbation are used to construct two graphs .
Outcome:	The proposed model outperforms benchmark models on multiple public datasets.

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Pengqi Li, Lizhong Ding, Zhehao Zhou, Chunhui Zhang, Jiarun Fu, Hao Li, Ye Yuan, Guoren Wang

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

Challenge:	Existing static strategies for mitigating hallucinations do not explicitly model the information gain from interacting with the external environment.
Approach:	They propose a calibration-driven interactive learning strategy that selects clarification queries by optimizing calibration error.
Outcome:	The proposed method provides theoretical guarantees and empirical gains for reliability.

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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou

CopyNE: Better Contextual ASR by Copying Named Entities (2024.acl-long)

Challenge:	a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say .
Approach:	They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible .
Outcome:	The proposed framework achieves state-of-the-art performance among open-source projects.

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Shilin Zhou, Zhenghua Li, Yu Hong, Min Zhang, Zhefeng Wang, Baoxing Huai

A Survey of Large Language Model-Based Search Agents (2026.acl-long)

Challenge:	Existing approaches to transcribe contextual named entities (NEs) treat entities as tokens and generate them token-by-token, which may result in incomplete transcriptions of entities.
Approach:	They propose a mechanism that can copy entities from the NE dictionary and reduce errors during entity transcription.
Outcome:	The proposed mechanism can copy entities from the NE dictionary, reducing errors during entity transcription, ensuring the completeness of the entity.

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Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang

Efficient Hyper-parameter Search for Knowledge Graph Embedding (2022.acl-long)

Challenge:	Large Language Models (LLMs) have revolutionized web search, but their integration is static and cannot handle complex contexts.
Approach:	They analyze existing research and analyze existing work from the perspectives of architecture, optimization, application, and evaluation.
Outcome:	The proposed models can comprehend user intentions and context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web.

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Yongqi Zhang, Zhanke Zhou, Quanming Yao, Yong Li

Making RALM Robust to Irrelevant Contexts via Layer Knowledge Guided Attention (2025.findings-acl)

Challenge:	Existing methods for learning knowledge graphs do not search hyper-parameters efficiently.
Approach:	They propose an efficient two-stage search algorithm which explores HP configurations on small subgraph and transfers top-performed configurations for fine-tuning on large full graph.
Outcome:	The proposed method finds better HPs than baseline algorithms within the same time budget and achieves 9.1% relative improvement on large-scale knowledge graphs.

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Weijie Shi, Hao Chen, Jiaming Li, Yao Zhao, Yazhong Zhang, Qijin Chen, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, Xiaofang Zhou

AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation (2023.acl-long)

Challenge:	Large language models (LLMs) face factual hallucination and knowledge obsolescence when tackling knowledge-intensive tasks.
Approach:	They propose a layer-knowledge guided attention method which harnesses the layer-wise knowledge of large language models to optimize per-layer attention on useful passages.
Outcome:	The proposed method outperforms existing methods on RALM benchmarks.

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Rongjie Huang, Huadai Liu, Xize Cheng, Yi Ren, Linjun Li, Zhenhui Ye, Jinzheng He, Lichao Zhang, Jinglin Liu, Xiang Yin, Zhou Zhao

Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models (2025.coling-main)

Challenge:	Existing models for speech-to-speech translation suffer from distinct degradation in noisy environments and fail to translate visual speech.
Approach:	They propose a text-based audio-visual speech-to-speech translation model that integrates visual information with audio-only data to improve system robustness.
Outcome:	The proposed model outperforms models trained on audio-only corpus in two languages . it also improves with low-resource audio-visual data, compared with baselines .

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Xinyu Pang, Ruixin Hong, Zhanke Zhou, Fangrui Lv, Xinwei Yang, Zhilong Liang, Bo Han, Changshui Zhang

Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training (2021.findings-acl)

Challenge:	Existing large language models (LLMs) fail due to lack of knowledge or incorrect knowledge application.
Approach:	They propose a knowledge-augmented framework that constructs a formula set to provide explicit physics knowledge and utilizes checklists to guide effective knowledge application.
Outcome:	The proposed framework achieves state-of-the-art performance on SciBench with an average accuracy improvement of 5.8%.

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Yun Hu, Yeshuang Zhu, Jinchao Zhang, Changwen Zheng, Jie Zhou

ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models (2024.findings-acl)

Challenge:	Named Entity Recognition (NER) is a fundamental and widely used task in natural language processing.
Approach:	They propose a decoupled NER model with two-stage training to take advantage of heterogeneous corpus, including dictionaries, distantly supervised instances, and human-annotated instances.
Outcome:	Empirical results show that the proposed model improves against baselines and can be scaled to a large extent.

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Yanan Wu, Jie Liu, Xingyuan Bu, Jiaheng Liu, Zhanhui Zhou, Yuanxing Zhang, Chenchen Zhang, ZhiqiBai ZhiqiBai, Haibin Chen, Tiezheng Ge, Wanli Ouyang, Wenbo Su, Bo Zheng

HyperMem: Hypergraph Memory for Long-Term Conversations (2026.acl-long)

Challenge:	ConceptMath evaluates concept-wise mathematical reasoning of Large Language Models (LLMs) Existing benchmarks that evaluate general mathematical reasoning with an average accuracy fail to probe the fine-grained failure modes of mathematical reasoning on specific datasets.
Approach:	They introduce a bilingual, fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models.
Outcome:	The proposed benchmarks evaluate concept-wise mathematical reasoning of Large Language Models with concept-based accuracies.

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Juwei Yue, Chuanrui Hu, Jiawei Sheng, Zuyi Zhou, Wenyuan Zhang, Tingwen Liu, Li Guo, Yafeng Deng

Defending Jailbreak Prompts via In-Context Adversarial Game (2024.emnlp-main)

Challenge:	Existing approaches to long-term memory management rely on pairwise relations, causing fragmented retrieval.
Approach:	They propose a hypergraph-based hierarchical memory architecture that explicitly models high-order associations using hyperedges.
Outcome:	Experiments show that HyperMem achieves state-of-the-art performance with 92.73% accuracy for long-term conversations.

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Yujun Zhou, Yufei Han, Haomin Zhuang, Kehan Guo, Zhenwen Liang, Hongyan Bao, Xiangliang Zhang

Breaking the Static Graph: Context-Aware Traversal for Graph-Based RAG (2026.findings-acl)

Challenge:	Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications, but concerns regarding their security persist.
Approach:	They propose an adversarial game that leverages agent learning to extend knowledge to defend against jailbreaks.
Outcome:	The proposed game shows that LLMs safeguarded by ICAG exhibit significantly reduced jailbreak success rates across various attack scenarios.

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Kwun Hang Lau, Fangyuan Zhang, Boyu Ruan, Yingli Zhou, Qintian Guo, Ruiyuan Zhang, Xiaofang Zhou

LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models (2024.naacl-long)

Challenge:	Recent advances in RAG focus on capturing multi-hop dependencies, but static Graphs fail to retrieve complete evidence chain.
Approach:	They propose a structure-aware approach to capture multi-hop dependencies using Knowledge Graphs and Personalized PageRank to capture semantic drift.
Outcome:	Experiments show that CatRAG outperforms state-of-the-art approaches . the proposed approach achieves substantial improvements in reasoning completeness .

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Yifan Yang, Jiajun Zhou, Ngai Wong, Zheng Zhang

DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs (2025.findings-emnlp)

Challenge:	Existing methods for parameter-efficient fine-tuning are limited by the growing number of trainable parameters with the rapid deployment of Large Language Models (LLMs).
Approach:	They propose a parameter-efficient framework that reduces trainable parameters through tensor-train decomposition.
Outcome:	The proposed methods achieve comparable or better performance than most widely used methods with up to 100 fewer parameters on the LLaMA-2-7B models.

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Xiabin Zhou, Wenbin Wang, Minyan Zeng, Jiaxian Guo, Xuebo Liu, Li Shen, Min Zhang, Liang Ding

Challenge:	Existing KV cache compression methods enforce a fixed pattern, neglecting task-specific characteristics, which hampers the effective retention of essential information while discarding less important tokens.
Approach:	They propose a Task-Aware KV cache mechanism that dynamically adjusts the KV caching size across different layers based on the characteristics of the tasks.
Outcome:	The proposed method surpasses state-of-the-art methods by 11% on the LongBench dataset even under extreme compression (0.9%)

Token-level Adaptive Training for Neural Machine Translation (2020.emnlp-main)

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Shuhao Gu, Jinchao Zhang, Fandong Meng, Yang Feng, Wanying Xie, Jie Zhou, Dong Yu

ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution (2026.acl-industry)

Challenge:	Existing token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to different learning difficulties for tokens in Neural machine translation (NMT).
Approach:	They propose to assign tokens with different frequencies to target tokens during training to encourage the model to pay more attention to low-frequency tokens.
Outcome:	The proposed model yields consistent improvements on ZH-EN, EN-RO, and EN-DE translation tasks, especially on sentences that contain more low-frequency tokens.

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Jihong Wang, Jiamu Zhou, Weiming Zhang, Teng Wang, Weiwen Liu, Zhuosheng Zhang, Xingyu Lou, Weinan Zhang, Huarong Deng, Jun Wang

Rectifying the Emotional Flow: Aligning Priors and Dynamic Guidance for High-Arousal Text-to-Speech (2026.acl-long)

Challenge:	Xue et al., 2025): deploying autonomous web agents in production remains difficult due to site heterogeneity and long-horizon instability.
Approach:	They propose a knowledge-evolving agent that can be used to automate web workflows . they use human-in-the-loop knowledge adaptation and knowledge-aligned progressive summarization .
Outcome:	Experiments on WebArena, WebChoreAren and industrial deployment show it outperforms baselines.

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Fangming Feng, Dongjie Fu, Zequn Xie, Yu Zhang, Yangyang Wu, Zhou Zhao, Tao Jin

Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues (2025.emnlp-main)

Challenge:	Existing systems suffer from linguistic collapse when pursuing high intensity or fail to meet target emotional levels.
Approach:	They propose an inference framework that introduces a neutral prosody bias and a uniform Classifier-Free Guidance that distorts the acoustic manifold, leading to artifacts.
Outcome:	The proposed framework achieves superior linguistic accuracy and expressiveness without model retraining.

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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

Modality-Aware Integration with Large Language Models for Knowledge-Based Visual Question Answering (2024.acl-long)

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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Junnan Dong, Qinggang Zhang, Huachi Zhou, Daochen Zha, Pai Zheng, Xiao Huang

Robust Singing Voice Transcription Serves Synthesis (2024.acl-long)

Challenge:	Existing methods to integrate multimodal knowledge in a modality-agnostic manner can be sub-optimal.
Approach:	They propose a modality-aware integration with large language models (LLMs) that leverages multimodal knowledge for both image understanding and knowledge reasoning.
Outcome:	The proposed model is able to bridge a tight inter-modal exchange while preserving insightful intra-modal learning.

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Ruiqi Li, Yu Zhang, Yongqi Wang, Zhiqing Hong, Rongjie Huang, Zhou Zhao

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)

Challenge:	Current AST methods struggle with accuracy and robustness when used for practical annotation.
Approach:	They propose a model that converts singing recordings into note sequences for automatic annotation of singing datasets.
Outcome:	The proposed model outperforms baseline models on enlarged, automatically annotated datasets.

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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang

A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation (2021.findings-emnlp)

Challenge:	Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns.
Approach:	They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users.
Outcome:	The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks.

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Linhai Zhang, Deyu Zhou, Chao Lin, Yulan He

Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)

Challenge:	Existing methods treat multi-label learning problem as a single label . Existing approaches focus on measuring semantic similarity of questions and candidate relations .
Approach:	They propose to solve multi-hop relation detection problem by generating sequences of hops and labels.
Outcome:	The proposed method is effective in KBQA, despite the unknown number of labels and hops.

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Huibin Ruan, Yu Hong, Yang Xu, Zhen Huang, Guodong Zhou, Min Zhang

Challenge:	Existing models for discourse relation recognition use self-attention and interactive-attention mechanisms.
Approach:	They develop a propagative attention learning model using a cross-coupled two-channel network.
Outcome:	The proposed model improves on the baseline models on a Penn Discourse Treebank.

UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets (2025.emnlp-main)

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Pengyu Wang, Shaojun Zhou, Chenkun Tan, Xinghao Wang, Wei Huang, Zhen Ye, Zhaowei Li, Botian Jiang, Dong Zhang, Xipeng Qiu

Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning (2026.acl-long)

Challenge:	Existing datasets address understanding and generation in isolation, limiting the performance of unified vision large language models.
Approach:	They propose a dataset that facilitates mutual enhancement between multimodal understanding and generation.
Outcome:	The proposed framework integrates diverse visual and textual inputs and outputs, enabling comprehensive cross-modal reasoning and precise text-to-image alignment.

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Zelin Tan, Hejia Geng, Xiaohang Yu, Mulei Zhang, Guancheng Wan, Yifan Zhou, Qiang He, Xiangyuan Xue, Heng Zhou, Yutao Fan, Zhong-Zhi Li, Zaibin Zhang, Guibin Zhang, Chen Zhang, Zhenfei Yin, Philip Torr, Lei Bai

Template-free Prompt Tuning for Few-shot NER (2022.naacl-main)

Challenge:	elucidating scaling laws for large language models (LLMs) during pre-training remains unexplored.
Approach:	They characterize how model scale, data, and compute interact during pre-training . they find that large models consistently demonstrate superior compute and data efficiency .
Outcome:	The proposed scaling laws offer practical guidance for scaling reasoning capabilities through reinforcement learning post-training.

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Ruotian Ma, Xin Zhou, Tao Gui, Yiding Tan, Linyang Li, Qi Zhang, Xuanjing Huang

SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding (2025.coling-main)

Challenge:	Prompt-based methods have been successfully applied in few-shot learning tasks . however, when applied to token-level labeling tasks, it would be time-consuming to enumerate the template queries over all potential entity spans.
Approach:	They propose a method to reformulate NER tasks as LM problems without templates.
Outcome:	The proposed method is 30.12 times faster than the template-based method under few-shot settings.

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Zhenglin Wang, Jialong Wu, Yilong Lai, Congzhi Zhang, Deyu Zhou

Frame First, Then Extract: A Frame-Semantic Reasoning Pipeline for Zero-Shot Relation Triplet Extraction (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have remarkable emergent abilities across various tasks, yet their performance on complex reasoning and planning tasks remains suboptimal.
Approach:	They propose a tree-search-based reasoning framework that encourages the exploration of intermediate steps and a round-scheduled strategy to manage draft model dispatching.
Outcome:	The proposed framework improves runtime speed and GPU memory management concurrently and handles multiple iterations for thought generation and state evaluation.

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Zehan Li, Fu Zhang, Wenqing Zhang, Jiawei Li, Zhou Li, Jingwei Cheng, Tianyue Peng

TeachMaster: Generative Teaching via Code (2026.acl-industry)

Challenge:	Existing methods to extract triplets for unseen relations rely on costly fine-tuning and lack structured semantic guidance.
Approach:	They propose a framework that adopts a "frame first, then extract" paradigm to extract triplets from unstructured text.
Outcome:	The proposed framework achieves competitive zero-shot performance on multiple benchmarks and can be used to enhance existing extraction methods.

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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

More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge (2021.emnlp-main)

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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Sixing Wu, Ying Li, Minghui Wang, Dawei Zhang, Yang Zhou, Zhonghai Wu

Visually-augmented pretrained language models for NLP tasks without images (2023.acl-long)

Challenge:	Existing knowledge-enhanced methods use a single-source homogeneous knowledge base with limited knowledge coverage.
Approach:	They propose a multi-source heterogeneous knowledge-enhanced dialogue generation model that leverages multiple knowledge sources to improve knowledge coverage.
Outcome:	The proposed model outperforms existing knowledge-enhanced models on a Chinese dataset and shows that it can leverage multiple heterogeneous knowledge sources to improve knowledge coverage.

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Hangyu Guo, Kun Zhou, Wayne Xin Zhao, Qinyu Zhang, Ji-Rong Wen

ELLE: Efficient Lifelong Pre-training for Emerging Data (2022.findings-acl)

Challenge:	Existing approaches to improve pre-trained language models lack visual commonsense and semantics.
Approach:	They propose a visual-augmented approach to fine-tune pre-trained language models by using retrieved or generated images instead of relying on explicit images.
Outcome:	The proposed approach outperforms baselines on ten tasks and consistently outperformed other approaches.

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Yujia Qin, Jiajie Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

SciFlow-Bench: Evaluating Structure-Aware Scientific Diagram Generation via Inverse Parsing (2026.acl-long)

Challenge:	Existing pre-trained language models are typically trained with static data, ignoring that streaming data of various sources may continuously grow.
Approach:	They propose to use function preserved model expansion to expand existing PLM's width and depth to improve efficiency of knowledge acquisition.
Outcome:	The proposed model improves pre-training efficiency and performance over existing models on BERT and GPT.

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Tong Zhang, Honglin Lin, Zhou Liu, Chong Chen, Wentao Zhang

Exploring and Distilling Multi-Dimensional Clues for Interpretable Social Bot Detection (2026.acl-long)

Challenge:	Existing benchmarks for scientific diagram generation rely on image-centric metrics or evaluation of intermediate symbolic representations rather than final rendered images.
Approach:	They propose a structure-first benchmark for evaluating scientific diagram generation from pixel-level outputs.
Outcome:	The proposed benchmark evaluates scientific diagram generation directly from pixel-level outputs.

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Yi Han, Haiqi Lu, Lizi Liao, Shuhan Zhou, Yuanxing Liu, Weinan Zhang, Ting Liu

Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability (2025.findings-acl)

Challenge:	Existing research on social bot detection results directly without corresponding supportive explanations, making it difficult to assess the extent to which such predictions are trustworthy.
Approach:	They propose a four-dimensional clue framework that uses outcome-reward reinforcement learning to train inspectors to generate faithful, grounded clues from user information, semantic features, interactive situation, and behavioral pattern.
Outcome:	The proposed framework outperforms baselines in detection performance and significantly improves the performance of large language models.

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Mengliang He, Jiayi Zeng, Yankai Jiang, Wei Zhang, Zeming Liu, Xiaoming Shi, Aimin Zhou

STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training (2026.acl-long)

Challenge:	Existing code generation benchmarks neglect flowchart-based code generation . existing benchmarks lack flowcharting-based evaluation, limiting the potential of large language models and minimizing human error.
Approach:	They propose to use flowcharts to evaluate existing LLMs' code generation capabilities.
Outcome:	The proposed benchmarks show that the supervised fine-tuning technique contributes greatly to the models’ performance.

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

ProLex: A Benchmark for Language Proficiency-oriented Lexical Substitution (2024.findings-acl)

Challenge:	Prior work has explored step-level supervision using Shannon-entropy-based uncertainty signals, which conflate inherent state complexity with agent confidence.
Approach:	They propose a hierarchical group-based RL framework that leverages normalized entropy to locate outlier steps associated with trajectory neglect and optimizes them via a mechanism of trajectory-aware reward and trajectory-independent penalty.
Outcome:	Experiments on ALFWorld, WebShop, and Search-Augmented QA show that STAPO achieves state-of-the-art performance while substantially alleviating trajectory neglect.

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Xuanming Zhang, Zixun Chen, Zhou Yu

AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning (2025.emnlp-main)

Challenge:	Lexical Substitution fails to consider substitutes of equal or higher proficiency than the target word.
Approach:	They propose a task to find appropriate substitutes for a given word in a context sentence but not those that are of equal or higher proficiency than the target.
Outcome:	The proposed model outperforms ChatGPT by an average of 3.2% in F-score and achieves comparable results with GPT-4 on ProLex.

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Yujie Feng, Jian Li, Xiaoyu Dong, Pengfei Xu, Xiaohui Zhou, Yujia Zhang, Zexin Lu, Yasha Wang, Alan Zhao, Xu Chu, Xiao-Ming Wu

Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information (2022.coling-1)

Challenge:	Recent model merging-based methods struggle to effectively manage the trade-off between learning new knowledge and preventing catastrophic forgetting.
Approach:	They propose a model merging framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status.
Outcome:	The proposed framework achieves significant performance improvements over existing state-of-the-art methods on three CL benchmarks with various model sizes (from 770M to 13B).

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Xuhui Sui, Ying Zhang, Kehui Song, Baohang Zhou, Guoqing Zhao, Xin Wei, Xiaojie Yuan

One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment (2020.coling-main)

Challenge:	Entity linking is a task of assigning entity mentions to referent entities in a knowledge base.
Approach:	They propose to use ultra-fine-grained type information to improve the generalization ability of EL models by utilizing a low-level task to extract ultra-finish entity type information.
Outcome:	The proposed model achieves state-of-the-art in the zero-shot entity linking task .

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Tengfei Huo, Zhiqiang Liu, Jinchao Zhang, Jie Zhou

A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering (2021.findings-emnlp)

Challenge:	Existing methods for automatic comment generation generate common and meaningless comments for music.
Approach:	They propose a multi-perspective strategy to enhance automatic music comment generation by combining different perspectives on a music comment dataset.
Outcome:	The proposed model outperforms state-of-the-art models on two music comment datasets and outperformed existing models by a substantial margin.

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Deyu Zhou, Yanzheng Xiang, Linhai Zhang, Chenchen Ye, Qian-Wen Zhang, Yunbo Cao

Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning (D19-1)

Challenge:	Existing methods for relation detection only detect one path to obtain the answer without considering other correct paths.
Approach:	They propose a divide-and-conquer approach for multi-label multi-hop relation detection . they propose 'path sampling mechanism' to generate diverse relation paths .
Outcome:	The proposed approach outperforms other competitive approaches on the FreebaseQA benchmark dataset.

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Jingjing Wang, Changlong Sun, Shoushan Li, Jiancheng Wang, Luo Si, Min Zhang, Xiaozhong Liu, Guodong Zhou

On-the-fly Denoising for Data Augmentation in Natural Language Understanding (2024.findings-eacl)

Challenge:	Recent neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC) however, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability.
Approach:	They propose a Hierarchical Reinforcement Learning approach to DASC that incorporates clause selection and word selection strategies to tackle the data noise problem.
Outcome:	The proposed approach over the state-of-the-art approaches shows impressive performance over the current baselines.

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Tianqing Fang, Wenxuan Zhou, Fangyu Liu, Hongming Zhang, Yangqiu Song, Muhao Chen

GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation (2024.emnlp-industry)

Challenge:	Existing methods to improve data augmentation performance may introduce noisy data that impairs training.
Approach:	They propose an on-the-fly denoising technique that learns from soft augmented labels provided by an organic teacher model trained on the cleaner original dataset.
Outcome:	The proposed method improves on text classification and question-answering tasks on general augmentation techniques and prevents overfitting on noisy labels.

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Wenjie Zhou, Zhenxin Ding, Xiaodong Zhang, Haibo Shi, Junfeng Wang, Dawei Yin

Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? (2021.emnlp-main)

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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Tianxing He, Jingzhao Zhang, Zhiming Zhou, James Glass

Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning (2026.acl-long)

Challenge:	Exposure bias is a central problem for auto-regressive language models (LM) it is believed that teacher forcing would cause test-time generation to be incrementally distorted due to the training-generation discrepancy.
Approach:	They propose to quantify the impact of exposure bias in quality, diversity, consistency and consistency by using ground-truth data prefixes instead of prefix generated by the model.
Outcome:	The proposed model performs better when the training-generation discrepancy is removed . the model is more robust and self-recovery ability is shown to counter exposure bias.

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Ziqing Zhuang, Linhai Zhang, Jiasheng Si, Deyu Zhou, Yulan He

G-SPEED: General SParse Efficient Editing MoDel (2023.findings-emnlp)

Challenge:	Existing approaches for improving LLM reasoning remain episodic and lack reusable meta-reasoning skills.
Approach:	They propose a framework that consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning.
Outcome:	The proposed framework consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning.

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Haoke Zhang, Yue Wang, Juntao Li, Xiabing Zhou, Min Zhang

OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages.
Approach:	They propose a general SParse Efficient Editing MoDel which can fulfill diverse editing requirements through a single model while maintaining low computational costs.
Outcome:	The proposed model can fulfill diverse editing requirements through a single model while maintaining low computational costs.

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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou

Why Can Distillation Work with Limited Resources? A Systematic Study (2026.findings-acl)

Challenge:	Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs.
Approach:	They propose a framework that integrates dialogue, reasoning, and personalized recommendation.
Outcome:	Experiments across public benchmarks show state-of-the-art performance.

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Xiao Hu, Xingyu Lu, Liyuan Mao, YiFan Zhang, Tianke Zhang, Bin Wen, Fan Yang, Tingting Gao, Guorui Zhou

BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data (2026.eacl-long)

Challenge:	Recent advances in large language models have driven reasoning performance . low-resource distillation can boost models' performance, but a framework is missing .
Approach:	They conduct a controlled experiment to find out why low-resource distillation can boost model performance . they find that distillation enhances the presence of advanced cognitive behaviors .
Outcome:	The proposed model shows more flexible reasoning, the authors show . they show that distillation enhances the presence of advanced cognitive behaviors .

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Jaap Jumelet, Abdellah Fourtassi, Akari Haga, Bastian Bunzeck, Bhargav Shandilya, Diana Galvan-Sosa, Faiz Ghifari Haznitrama, Francesca Padovani, Francois Meyer, Hai Hu, Julen Etxaniz, Laurent Prevot, Linyang He, María Grandury, Mila Marcheva, Negar Foroutan, Nikitas Theodoropoulos, Pouya Sadeghi, Siyuan Song, Suchir Salhan, Susana Zhou, Yurii Paniv, Ziyin Zhang, Arianna Bisazza, Alex Warstadt, Leshem Choshen

Are LLMs Rational Investors? A Study on the Financial Bias in LLMs (2025.findings-acl)

Challenge:	prevailing trend in language modeling research is to prioritize scaling, authors say . from infancy to maturity, English learners acquire language through exposure to less than 100M words .
Approach:	They propose a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language.
Outcome:	The proposed models outperform models trained on a fixed, developmentally plausible English corpus on various benchmarks.

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Yuhang Zhou, Yuchen Ni, Zhiheng Xi, Zhangyue Yin, Yu He, Gan Yunhui, Xiang Liu, Zhang Jian, Sen Liu, Xipeng Qiu, Yixin Cao, Guangnan Ye, Hongfeng Chai

XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation (2021.acl-long)

Challenge:	Existing studies on biases within specific domains, such as finance, remain limited.
Approach:	They propose a framework to detect, detect, analyze and mitigate financial biases in large language models.
Outcome:	The proposed framework reduces bias by 68% for the most biased model, according to key metrics.

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Dongqin Xu, Junhui Li, Muhua Zhu, Min Zhang, Guodong Zhou

Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint (2024.findings-acl)

Challenge:	Abstract Meaning Representation (AMR) research is limited and challenging for languages other than English.
Approach:	They propose a cross-lingual pre-training approach for AMR parsing and text generation . they use an English-to-English parallel dataset and a multi-task learning approach .
Outcome:	The proposed approach outperforms baseline pre-training methods on English parsing and text generation tasks.

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Zhipeng Chen, Kun Zhou, Xin Zhao, Junchen Wan, Fuzheng Zhang, Di Zhang, Ji-Rong Wen

Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing (2021.findings-acl)

Challenge:	Existing reinforcement learning methods do not provide fine-grained supervision for complex reasoning tasks.
Approach:	They propose a reinforcement learning method that incorporates a generative model as the reward model and a token-level supervision model for RL training.
Outcome:	Experiments on 8 tasks show the proposed method is effective .

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Qian Liu, Dejian Yang, Jiahui Zhang, Jiaqi Guo, Bin Zhou, Jian-Guang Lou

SADA: Bridging In-Context Learning and Fine-Tuning via State-Aligned Distillation Adapters (2026.acl-long)

Challenge:	Recent years pretrained language models (PLMs) have shown their power on modeling language . however, few efforts have been made to explore grounding capabilities of PLMs .
Approach:	They propose to use pretrained language models to explore syntactic structures . they propose to combine their approach with an erasingthen-awakening approach . their results show that the approach can awaken latent grounding, which is understandable to humans .
Outcome:	Empirical studies show that the proposed approach can awaken latent grounding . it shows great potential to benefit downstream semantic parsing models, it says .

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Wenhao Gao, Tianlong Wang, Wei Jia, Linhao Zhang, Aiwei Liu, Miao Fan, Zhou Xiao

AutoCAD: Automatically Generate Counterfactuals for Mitigating Shortcut Learning (2022.findings-emnlp)

Challenge:	Prompt-based in-context learning and parameter fine-tuning are dominant paradigms for incorporating external information into large language models, but they incur high inference costs or require expensive retraining.
Approach:	They propose to convert prompts into temporary adapter weights to bridge this gap by converting prompts to temporary adapters.
Outcome:	The proposed model outperforms baselines on long-context language modeling and downstream NLU and summarization benchmarks while significantly reducing memory footprint and latency.

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Jiaxin Wen, Yeshuang Zhu, Jinchao Zhang, Jie Zhou, Minlie Huang

Challenge:	Existing methods for generating counterfactuals rely on human efforts or task-specific designs.
Approach:	They propose to use a fully automatic and task-agnostic CAD generation framework to generate diverse counterfactuals.
Outcome:	The proposed framework outperforms human-in-the-loop and task-specific CAD methods on multiple out-of-domain and challenge benchmarks.

AgentMark: Utility-Preserving Behavioral Watermarking for Agents (2026.acl-long)

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Kaibo Huang, Jin Tan, Yukun Wei, Wanling Li, Zipei Zhang, Hui Tian, Zhongliang Yang, Linna Zhou

Challenge:	Recent advances in large language models (LLMs) have improved text generation and reasoning.
Approach:	They propose a behavioral watermarking framework that embeds multi-bit identifiers into planning decisions while preserving utility.
Outcome:	The proposed framework embeds multi-bit provenance into planning decisions while preserving utility.

Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs (2025.findings-emnlp)

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Kuan Lok Zhou, Jiayi Chen, Siddharth Suresh, Reuben Narad, Timothy T. Rogers, Lalit K Jain, Robert D Nowak, Bob Mankoff, Jifan Zhang

Commonsense Reasoning

Challenge:	Large Language Models (LLMs) have shown significant limitations in understanding creative content, as demonstrated by Hessel et al. (2023)’s influential work on the New Yorker Cartoon Caption Contest.
Approach:	They propose to decompose humor understanding into three components and improve each by enhancing visual understanding through improved annotation and utilizing LLM-generated humor reasoning and explanations.
Outcome:	The proposed approach achieves 82.4% accuracy in caption ranking, significantly better than the previous 67% benchmark and matches the performance of world-renowned human experts in this domain.

GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling (P19-1)

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

Chunking

Challenge:	Existing systems for sequence labeling are limited by shallow connections between consecutive hidden states and insufficient modeling of global information.
Approach:	They propose a global context enhanced deep transition architecture for sequence labeling . they deepen the state transition path at each position in a sentence and assign tokens with global representations .
Outcome:	The proposed architecture outperforms the best reported results on two standard sequence labeling tasks.

Competence-based Curriculum Learning for Multilingual Machine Translation (2021.findings-emnlp)

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Mingliang Zhang, Fandong Meng, Yunhai Tong, Jie Zhou

Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation (2026.acl-long)

Challenge:	Existing multilingual machine translation models face an imbalance problem due to the different learning competencies of different languages.
Approach:	They propose Competence-based Curriculum Learning for Multilingual Machine Translation, named CCL-M, to help schedule the high resource languages and low resource languages.
Outcome:	The proposed approach achieves a steady and significant performance gain compared to the previous state-of-the-art approach on the TED talks dataset.

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Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo, Yiwen Shao, Hao Zhang, Dong Yu

AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists (2026.acl-demo)

Challenge:	Existing methods for learning general-purpose audio representations are limited in scope and coverage of audio attributes.
Approach:	They propose to use a 10.7M caption dataset to compare ALP with captioning . they find that contrastive learning yields competitive, transferable representations .
Outcome:	The proposed model yields competitive, transferable representations, while captioning exhibits better scalability.

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Junshu Pan, Panzhong Lu, Yixuan Weng, QiYao Sun, Fang Guo, Zijie Yang, Qiji Zhou, Yue Zhang

Challenge:	Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs.
Approach:	They propose an AI-driven open-access platform built on open preprints, AI-augmented analysis and review, and reader feedback.
Outcome:	The proposed platform supports human scientists through an interactive UI and AI scientists through Model Context Protocol (MCP)-based interactions.

Verifiable Format Control for Large Language Model Generations (2025.findings-naacl)

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Zhaoyang Wang, Jinqi Jiang, Huichi Zhou, Wenhao Zheng, Xuchao Zhang, Chetan Bansal, Huaxiu Yao

EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning (2023.findings-acl)

Challenge:	Existing methods focus on benchmarking general instruction following while overlooking how to improve specific format following ability for small LLMs.
Approach:	They propose to synthesize massive datasets to improve LLMs' format following abilities by using a verifiable format following feature.
Outcome:	The proposed method improves the format following ability of small LLMs with about 7B parameters.

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Tiannan Wang, Wangchunshu Zhou, Yan Zeng, Xinsong Zhang

Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval (2025.acl-long)

Challenge:	Pre-trained vision-language models have achieved impressive results in a range of vision-linguistic tasks.
Approach:	They propose a distilling then pruning framework to compress large vision-language models into smaller, faster ones.
Outcome:	The proposed framework reduces the size of a pre-trained large vision-language model and improves its performance on vision-linguistic tasks.

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Zheng Liu, Ze Liu, Zhengyang Liang, Junjie Zhou, Shitao Xiao, Chao Gao, Chen Jason Zhang, Defu Lian

Data-Efficiently Learn Large Language Model for Universal 3D Scene Perception (2025.findings-naacl)

Challenge:	Existing multimodal retrieval models are lacking in visual representations of multimodal data.
Approach:	They propose a visualized information retrieval paradigm where multimodal information is represented by a unified visual format called Screenshots for various retrieval applications.
Outcome:	The proposed model is based on a large dataset of screenshots from diverse sources . it is compared with existing models and lays a solid foundation for the new model .

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Zehan Wang, Haifeng Huang, Yang Zhao, Ziang Zhang, Tao Jin, Zhou Zhao

Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation (2021.emnlp-main)

Challenge:	Existing methods for 3D scene understanding are limited to specific downstream tasks, hindering their practicality in real-world applications.
Approach:	They propose a 3D visual perceptual ability and advanced reasoning capabilities for 3D scenes by aligning 3D representations into the feature space of advanced LLMs.
Outcome:	The proposed system achieves a 82.2% relative score compared with state-of-the-art methods with limited data.

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Xin Tan, Longyin Zhang, Guodong Zhou

Exploration-Exploitation Reshaping towards Efficient Reasoning for Large Language Models (2026.findings-acl)

Challenge:	ZP-annotated natural language generation (NLG) corpora are scarce in pro-drop languages . despite efforts to bridge the discrepancy between human and machine, zero pronouns still persist in pro -drop tasks.
Approach:	They propose a highly adaptive two-stage approach to couple context modeling with ZP recovering to mitigate the ZP problem in NLG tasks.
Outcome:	The proposed approach can improve translation, question answering, and summarization tasks.

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Yufeng Shi, Weilin Luo, Yuxiang Zhang, Zongmeng Zhang, Haoyang Liu, Yubing Wang, Bin Wang, Wengang Zhou, Houqiang Li

LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization (2025.coling-main)

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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Yanyue Zhang, Pengfei Li, Yilong Lai, Yulan He, Deyu Zhou

AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment (2025.coling-main)

Challenge:	Existing methods to generate negative summaries are expensive and lack the capacity to generate large data sets.
Approach:	They propose a data augmentation framework based on LArge and Small language models for debiaSing opinion summarization that generates a small number of synthesized negative reviews by rewriting the positive text via a large language model.
Outcome:	The proposed framework can generate large numbers of negative reviews by rewriting the positive text using a large language model and training a disentangle reconstruction model based on the generated data.

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Yilong Lai, Jialong Wu, Congzhi Zhang, Haowen Sun, Deyu Zhou

EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering (2021.acl-long)

Challenge:	Existing methods to address conversational search challenges are limited by one specific retrieval system.
Approach:	They propose a framework to enhance generalizability of information-seeking queries by aligning reformulation models with term-based and semantic retrieval systems.
Outcome:	The proposed framework outperforms existing methods in a more efficient framework.

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Zhibin Duan, Hao Zhang, Chaojie Wang, Zhengjue Wang, Bo Chen, Mingyuan Zhou

From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization (2022.naacl-main)

Challenge:	Existing studies have shown that data diversity affects the performance of LMs if we train a single LM over the entire dataset.
Approach:	They propose an autoencoding topic model with a mixture prior to perform clustering for the data.
Outcome:	The proposed model can learn knowledge from different samples while extracting cluster-specific features.

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Yue Fang, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Bo Long, Yanyan Lan, Yanquan Zhou

Multi-split Reversible Transformers Can Enhance Neural Machine Translation (2021.eacl-main)

Challenge:	Existing models focus more on the structure of summary, not on the personal and logical inconsistency problem.
Approach:	They propose a model to solve the problem of personal and logical inconsistency . they use an utterance rewriter to complete the ellipsis content of dialogue content .
Outcome:	The proposed model outperforms baseline models on both SAMSum and DialSum datasets.

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Yuekai Zhao, Shuchang Zhou, Zhihua Zhang

Exploiting the Shadows: Unveiling Privacy Leaks through Lower-Ranked Tokens in Large Language Models (2025.acl-long)

Challenge:	Large-scale transformers have been shown to improve neural machine translation performance but training these wider and deeper networks could be extremely memory intensive.
Approach:	They propose a multi-split based reversible transformer and a backpropagation algorithm that does not need to store activations for most layers.
Outcome:	The proposed model outperforms the vanilla transformer by at least 1.4 BLEU points in three datasets.

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Yuan Zhou, Zhuo Zhang, Xiangyu Zhang

A Span-based Multimodal Variational Autoencoder for Semi-supervised Multimodal Named Entity Recognition (2022.emnlp-main)

Challenge:	Large language models face vulnerabilities related to the extraction of sensitive information.
Approach:	They propose a method to exploit the model's lower-ranked output tokens to extract private information from retrieved documents or training knowledge.
Outcome:	The proposed method is effective in both the agentic application privacy extraction setting and the direct training data extraction.

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Baohang Zhou, Ying Zhang, Kehui Song, Wenya Guo, Guoqing Zhao, Hongbin Wang, Xiaojie Yuan

A Progressive Framework for Role-Aware Rumor Resolution (2022.coling-1)

Challenge:	Existing methods for named entity recognition on social media are not efficient for semi-supervised MNER because of the mismatch between the posted text and image.
Approach:	They propose a novel method to fuse the text and image features for multimodal named entity recognition under semi-supervised setting by exploiting modal-specific VAEs.
Outcome:	The proposed method outperforms baselines under supervised setting and improves performance with less labeled data than existing semi-supervised methods.

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Lei Chen, Guanying Li, Zhongyu Wei, Yang Yang, Baohua Zhou, Qi Zhang, Xuanjing Huang

Challenge:	Existing methods for rumor resolution ignore intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges.
Approach:	They propose to identify triggering posts and exploit their characteristics to facilitate rumor verification.
Outcome:	The proposed model and scheme exploits rumor diffusion patterns and linguistic features to facilitate verification.

VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)

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Yang Tan, Chen Liu, Jingyuan Gao, Wu Banghao, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou

Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning (2024.findings-acl)

Challenge:	Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges.
Approach:	They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs.
Outcome:	The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs.

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Kung-Hsiang Huang, Mingyang Zhou, Hou Pong Chan, Yi Fung, Zhenhailong Wang, Lingyu Zhang, Shih-Fu Chang, Heng Ji

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

Challenge:	LVLMs are known for producing text that is factually inconsistent with visual input . factuality of generated captions for structured visuals has not been studied as much .
Approach:	They propose a typology of factual errors in captions generated by large vision-language models . they propose CHOCOLATE, a visual entailment model that outperforms current models based on this analysis .
Outcome:	The proposed model outperforms current models in evaluating caption factuality.

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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang

WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)

Challenge:	Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction.
Approach:	They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack.
Outcome:	The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses.

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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.

Text-to-Song: Towards Controllable Music Generation Incorporating Vocal and Accompaniment (2024.acl-long)

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Zhiqing Hong, Rongjie Huang, Xize Cheng, Yongqi Wang, Ruiqi Li, Fuming You, Zhou Zhao, Zhimeng Zhang

DavIR: Data Selection via Implicit Reward for Large Language Models (2025.acl-long)

Challenge:	Existing studies focus on singing voice synthesis and music generation independently.
Approach:	They propose a novel task called Text-to-Song synthesis which incorporates both vocal and accompaniment generation.
Outcome:	The proposed method can synthesize songs with comparable quality and style consistency.

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Haotian Zhou, Tingkai Liu, Qianli Ma, Yufeng Zhang, Jianbo Yuan, Pengfei Liu, Yang You, Hongxia Yang

Privacy-Preserving Reasoning with Knowledge-Distilled Parametric Retrieval Augmented Generation (2026.findings-acl)

Challenge:	6% of Alpaca dataset selected with DavIR can steer both LLaMA and Gemma models to produce superior performance compared to the same models trained on the full 52K dataset.
Approach:	They propose a model-based data selection method for post-training Large Language Models . they generalize Reducible Holdout Loss to core-set selection problem of causal language modeling .
Outcome:	The proposed method can steer both LLaMA and Gemma models to superior performance compared to the same models trained on the full 52K dataset.

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Jinwen Chen, Hainan Zhang, Liang Pang, Yongxin Tong, Haibo Zhou, Wei Lin, Zhiming Zheng

Opinions Are Not Always Positive: Debiasing Opinion Summarization with Model-Specific and Model-Agnostic Methods (2024.lrec-main)

Challenge:	Existing RAG systems require uploading local documents to the cloud, resulting in inference latency and poor generalization on out-of-distribution (OOD) inputs.
Approach:	They propose a generalizable knowledge-distilled parametric RAG model aligned with standard RAG in document structure and parameter activation.
Outcome:	The proposed model outperforms baselines in accuracy and generalizes well on out-of-distribution (OOD) data.

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Yanyue Zhang, Yilong Lai, Zhenglin Wang, Pengfei Li, Deyu Zhou, Yulan He

Context-faithful Prompting for Large Language Models (2023.findings-emnlp)

Challenge:	Existing opinion summarization frameworks are reluctant to generate negative summaries given input of negative opinions.
Approach:	They propose to disentangle input into sentiment-relevant and sentiment-irrelevant components through adversarial loss.
Outcome:	The proposed approaches reduce sentiment bias in the existing opinion summarization dataset . the proposed approaches generate better summaries with a more balanced emotional polarity distribution .

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Wenxuan Zhou, Sheng Zhang, Hoifung Poon, Muhao Chen

TSAM: A Two-Stream Attention Model for Causal Emotion Entailment (2022.coling-1)

Challenge:	Large language models encode parametric knowledge about world facts but overly rely on it can cause incorrect predictions in context-sensitive NLP tasks.
Approach:	They propose to use opinion-based prompts and counterfactual demonstrations to improve LLM faithfulness to contexts.
Outcome:	The proposed methods improve faithfulness to contexts using opinion-based prompts and counterfactual demonstrations.

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Duzhen Zhang, Zhen Yang, Fandong Meng, Xiuyi Chen, Jie Zhou

Evaluating the Effect of Retrieval Augmentation on Social Biases (2026.eacl-long)

Challenge:	Existing studies on EAC focus on Emotion Recognition in Conversations (ERC), i.e., recognizing emotion labels of utterances.
Approach:	They propose a two-stream attention model to capture correlations between utterances in a global view and classify multiple utterrances synchronously to capture emotion and speaker information in parallel.
Outcome:	The proposed model outperforms baselines and achieves new State-Of-The-Art (SOTA) performance.

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Tianhui Zhang, Yi Zhou, Danushka Bollegala

Transferring General Multimodal Pretrained Models to Text Recognition (2023.findings-acl)

Challenge:	RAG is a popular method for injecting up-to-date knowledge into LLMs.
Approach:	They examine how RAG modulates social biases across three languages and four categories . they find that biased documents are amplified even when base LLM has low-level of intrinsic bias .
Outcome:	The proposed method can enhance factual accuracy but its effect on social biases is not well understood.

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Junyang Lin, Xuancheng Ren, Yichang Zhang, Gao Liu, Peng Wang, An Yang, Chang Zhou

A Novel Negative Sample Generation Method for Contrastive Learning in Hierarchical Text Classification (2025.coling-main)

Challenge:	Existing methods for text recognition rely on large-scale pretraining on human-annotated or synthetic data.
Approach:	They propose a method to transfer multimodal pretrained models to text recognition using image captioning.
Outcome:	The proposed method outperforms the baselines and achieves state-of-the-art performance in the Chinese text recognition benchmark.

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Juncheng Zhou, Lijuan Zhang, Yachen He, Rongli Fan, Lei Zhang, Jian Wan

Safety Alignment via Constrained Knowledge Unlearning (2025.acl-long)

Challenge:	Existing methods for hierarchical text classification struggle with fine-grained labels, leading to difficulties in accurate classification.
Approach:	They propose a hierarchical sequence ranking method for generating diverse negative samples using hierarchically structured hierarchic labels.
Outcome:	The proposed method achieves state-of-art (SOTA) on two datasets showing that it can distinguish between fine-grained labels and discriminate.

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Zesheng Shi, Yucheng Zhou, Jing Li, Yuxin Jin, Yu Li, Daojing He, Fangming Liu, Saleh Alharbi, Jun Yu, Min Zhang

I run as fast as a rabbit, can you? A Multilingual Simile Dialogues Datasets (2023.findings-acl)

Challenge:	Existing defense mechanisms have not fully deleted harmful knowledge in large language models (LLMs) Existing methods to address safety alignment have not completely deleted harmful information in LLMs.
Approach:	They propose a safety alignment strategy that uses scoring neurons to identify useful knowledge in LLMs and pruning the gradients of neurons in U to preserve beneficial information.
Outcome:	The proposed method significantly improves model safety while maintaining utility compared to existing methods.

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

MSMO: Multimodal Summarization with Multimodal Output (D18-1)

Challenge:	A simile is a figure of speech that compares two different things via shared properties.
Approach:	They propose a multilingual simile dialogue dataset that can be used to study similes in real-life scenarios.
Outcome:	The proposed dataset is the largest manually annotated simile dataset and contains both English and Chinese data.

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Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang, Chengqing Zong

G3R: A Graph-Guided Generate-and-Rerank Framework for Complex and Cross-domain Text-to-SQL Generation (2023.findings-acl)

Challenge:	Existing studies show that multimodal summarization can improve user satisfaction for informativeness of summaries by using information in visual modality.
Approach:	They propose a task to generate text and select the most relevant image from the multimodal input and a novel multimodal automatic evaluation method to evaluate multimodal outputs.
Outcome:	The proposed method improves user satisfaction by 12.4% compared to the current system .

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Yanzheng Xiang, Qian-Wen Zhang, Xu Zhang, Zejie Liu, Yunbo Cao, Deyu Zhou

VarBench: Robust Language Model Benchmarking Through Dynamic Variable Perturbation (2024.findings-emnlp)

Challenge:	Existing approaches to complex and cross-domain Text-to-SQL generation lack domain knowledge . domain knowledge is not incorporated to enhance their ability to generalise to unseen databases.
Approach:	They propose a framework called G3R for complex and cross-domain Text-to-SQL generation . they propose re-ranking SQL queries based on domain knowledge and a graph-guided SQL generator .
Outcome:	The proposed framework achieves state-of-the-art results on the Spider and Spider-DK benchmarks.

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Kun Qian, Shunji Wan, Claudia Tang, Youzhi Wang, Xuanming Zhang, Maximillian Chen, Zhou Yu

ChatUIE: Exploring Chat-based Unified Information Extraction Using Large Language Models (2024.lrec-main)

Challenge:	Recent benchmarks release only training and validation sets, keeping the test set labels closed-source.
Approach:	They propose to extract variables from each test case and define a value range for each variable.
Outcome:	The proposed method improves the accuracy of the evaluations on four datasets covering mathematical generation and multiple-choice tasks.

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Jun Xu, Mengshu Sun, Zhiqiang Zhang, Jun Zhou

CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models (2026.findings-acl)

Challenge:	Recent advances in large language models have shown impressive performance in general chat, but their domain-specific capabilities have certain limitations.
Approach:	They propose a unified information extraction framework built upon ChatGLM that incorporates domain-specific modeling to extract structured information from natural language.
Outcome:	The proposed framework significantly improves the performance of information extraction tasks with a slight decrease in chatting ability.

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Zhenhong Zhou, Zherui Li, Jie Zhang, Yuanhe Zhang, Kun Wang, Yang Liu, Qing Guo

CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering (2025.emnlp-main)

Challenge:	Existing security models rely on open-ended communication, but the collaborative process itself can be exploited and disrupted.
Approach:	They propose a new threat class, called Denial-of-Collaboration, which corrupts collaborative structure and transforms communication topology into self-sabotage.
Outcome:	The proposed attacks bypass conventional safety alignments that are not designed to detect behavioral or systemic attacks.

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Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, Luan Zhang

Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans? (2023.emnlp-main)

Challenge:	Existing knowledge base question answering methods struggle with complex queries.
Approach:	They propose a framework that optimizes the process of fine-tuning a LLM for generating logical forms by enabling it to learn relevant sub-tasks like skeleton generation, topic entity generation, and relevant relations generation.
Outcome:	The proposed framework achieves state-of-the-art on two benchmark KBQA datasets, WebQSP and CWQ.

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Yichi Zhang, Jiayi Pan, Yuchen Zhou, Rui Pan, Joyce Chai

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)

Challenge:	Visual illusions are a phenomenon that is often seen in human perception but are not always faithful to the physical world.
Approach:	They build a dataset containing five types of visual illusions and formulate four tasks to examine visual illusion in state-of-the-art VLMs.
Outcome:	The proposed dataset reveals that larger models are closer to human perception and more susceptible to visual illusions.

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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen

Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control (2022.findings-naacl)

Challenge:	Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance.
Approach:	They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents.
Outcome:	The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain.

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Haopeng Zhang, Semih Yavuz, Wojciech Kryscinski, Kazuma Hashimoto, Yingbo Zhou

Summarization

Challenge:	Abstractive summarization systems have been shown to be more prone to unfaithful facts . 30% of summaries generated by pre-trained language models suffer from hallucination .
Approach:	They propose a method to remedy entity-level extrinsic hallucinations with Entity Coverage Control . they first compute entity coverage precision and prepend the corresponding control code . a further fine-tuning is performed to unlock zero-shot summarization .
Outcome:	The proposed method leads to more faithful and salient abstractive summarization in fine-tuning and zero-shot settings.

AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis (2022.coling-1)

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Ziming Li, Yan Zhou, Weibo Zhang, Yaxin Liu, Chuanpeng Yang, Zheng Lian, Songlin Hu

Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)

Challenge:	Existing methods treat three modal features equally, without distinguishing the importance of different modalities. Existing models split the video into frames, leading to missing the global acoustic information.
Approach:	They propose a global Acoustic feature enhanced Modal-Order-Aware network to address these problems.
Outcome:	The proposed model outperforms state-of-the-art models on two public datasets.

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Tong Zhu, Haitao Wang, Junjie Yu, Xiabing Zhou, Wenliang Chen, Wei Zhang, Min Zhang

SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios (2026.acl-long)

Challenge:	Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing.
Approach:	They build a dataset using DS-generated data as training data and hire annotators to label test data.
Outcome:	The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation.

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Junkai Chen, Huihui Huang, Yunbo Lyu, Junwen An, Jieke Shi, Chengran Yang, Ting Zhang, Haoye Tian, Yikun Li, Zhenhao Li, Xin Zhou, Xing Hu, David Lo

Challenge:	Existing benchmarks fail to capture scenarios in which vulnerabilities are introduced by humans . we evaluate 5 popular code agents supported by 5 LLMs on SecureVibeBench .
Approach:	They propose a benchmarking tool that compares 105 C/C++ secure coding tasks . they use real-world open-source vulnerabilities and a comprehensive evaluation tool .
Outcome:	The proposed benchmarks show that code agents struggle to produce correct and secure code . the best performing agent produces merely 23.8% correct and secured solutions .

Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation (2026.findings-acl)

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Hanqi Jiang, Junhao Chen, Yi Pan, Ling Chen, Weihang You, Yifan Zhou, Ruidong Zhang, Yohannes Abate, Tianming Liu

TextObfuscator: Making Pre-trained Language Model a Privacy Protector via Obfuscating Word Representations (2023.findings-acl)

Challenge:	Large Language Models excel at generalized reasoning, but lack the ability to accumulate experiences and maintain narrative coherence over long horizons.
Approach:	They propose a unified memory architecture that transcends static vector similarity.
Outcome:	The proposed model outperforms state-of-the-art methods in temporal and multihop reasoning tasks.

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Xin Zhou, Yi Lu, Ruotian Ma, Tao Gui, Yuran Wang, Yong Ding, Yibo Zhang, Qi Zhang, Xuanjing Huang

Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling (2024.lrec-main)

Challenge:	Existing inference services are plagued by privacy concerns, such as sharing sensitive data with service providers.
Approach:	They propose a framework for protecting inference privacy by applying random perturbations to clustered representations.
Outcome:	The proposed framework protects inference privacy by applying random perturbations to clustered representations.

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Guangmin Zheng, Jin Wang, Xiaobing Zhou, Xuejie Zhang

FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information (2022.emnlp-main)

Challenge:	Chain of thought (CoT) is used for complex reasoning problems, but hallucinations are a problem in multimodal CoT.
Approach:	They propose a method to generate soft negative samples with different semantics to mitigate hallucinations in multimodal CoT.
Outcome:	The proposed method mitigates hallucinations in multimodal CoT by using soft negative sampling.

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Yijia Shao, Mengyu Zhou, Yifan Zhong, Tao Wu, Hongwei Han, Shi Han, Gideon Huang, Dongmei Zhang

Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models (2024.naacl-long)

Challenge:	FormLM is a pre-trained language model for creating semi-structured forms where questions and descriptions are organized by predefined structures.
Approach:	They propose to enhance pre-trained language model with form structural information to model online forms and recommend form creation ideas.
Outcome:	The proposed model outperforms general-purpose language models on all tasks, with an improvement by 4.71 on Question Recommendation and 10.6 on Block Type Suggestion in terms of ROUGE-1 and Macro-F1, respectively.

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Yue Zhou, Yada Zhu, Diego Antognini, Yoon Kim, Yang Zhang

Evaluating Robustness of Large Audio Language Models to Audio Injection: An Empirical Study (2025.emnlp-main)

Challenge:	Despite the impressive performance of large-scale language models, their ability to reason through complex problems remains a bottleneck.
Approach:	They propose a method which diversifies reasoning paths from specific surface forms of the problem to improve mathematical reasoning performance.
Outcome:	The proposed approach improves mathematical reasoning performance over vanilla self-consistency, especially for problems initially deemed unsolvable.

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Guanyu Hou, Jiaming He, Yinhang Zhou, Ji Guo, Yitong Qiao, Rui Zhang, Wenbo Jiang

Chinese Spoken Named Entity Recognition in Real-world Scenarios: Dataset and Approaches (2024.findings-acl)

Challenge:	Large Audio-Language Models (LALMs) are increasingly being deployed in real-world applications, yet their robustness against malicious audio injection remains underexplored.
Approach:	They quantitatively assess their vulnerabilities and resilience using metrics: the Defense Success Rate, Context Robustness Score, and Judgment Robustic Index.
Outcome:	The proposed models demonstrate significant performance disparities across four attack scenarios.

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Shilin Zhou, Zhenghua Li, Chen Gong, Lei Zhang, Yu Hong, Min Zhang

Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models (2024.acl-long)

Challenge:	Current Chinese Spoken NER datasets are laboratory-controlled and are limited in topics.
Approach:	They propose to use Chinese Spoken NER datasets to extract entities from speech to help voice assistants better grasp the intent behind user's questions and instructions.
Outcome:	The proposed methods improve on self-training-asr and mapping then distilling, and even compared with GPT4.0, they achieve better results.

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Yuchong Sun, Che Liu, Kun Zhou, Jinwen Huang, Ruihua Song, Xin Zhao, Fuzheng Zhang, Di Zhang, Kun Gai

DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition (2026.acl-industry)

Challenge:	Existing studies overlook the multi-turn instruction following ability of large language models (LLMs) Extensive experiments show that Parrot improves current LLMs by up to 7.2% in multi- turn instruction following.
Approach:	They propose a method for collecting multi-turn instructions that feature human-like queries, such as anaphora and ellipsis, and a context-aware preference optimization strategy to further enhance LLMs for complex queries.
Outcome:	The proposed method improves existing LLMs by up to 7.2% in multi-turn instruction following.

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Le Zhang, Shengming Zhang, Rui Zha, Wu Yunpeng, Jingbo Zhou, Jizhou Huang

SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation (2026.acl-long)

Challenge:	Accurate Point of Interest (POI) attribute acquisition is essential for location-based services, yet traditional IVR systems suffer from error accumulation and high maintenance overhead.
Approach:	They propose a large language model-based framework for large-scale POI attribute acquisition at Baidu Maps.
Outcome:	The proposed framework outperforms existing IVR systems in 83.9% task success rate while maintaining a low reaction time of 130ms.

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Hui Wang, Jinghua Zhao, Yifan Yang, Shujie Liu, Junyang Chen, Yanzhe Zhang, Shiwan Zhao, Jinyu Li, Jiaming Zhou, Haoqin Sun, Yan Lu, Yong Qin

Dual-Path Dynamic Fusion with Learnable Query for Multimodal Sentiment Analysis (2025.emnlp-main)

Challenge:	Existing methods for evaluating the perceptual quality of synthetic speech are limited due to the complexity of perceptual quality factors and the diversity of speech generation tasks.
Approach:	They propose a new paradigm for enabling large language models to conduct structured speech quality evaluation using a large-scale dataset.
Outcome:	The proposed model performs well across tasks and languages.

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Miao Zhou, Lina Yang, Thomas Wu, Dongnan Yang, Xinru Zhang

Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (2025.findings-acl)

Challenge:	Existing methods for multimodal sentiment analysis struggle with global and fine-grained contributions and over-reliance on text.
Approach:	They propose a multimodal sentiment analysis architecture that processes inputs through two complementary paths: global and local.
Outcome:	The proposed architecture achieves state-of-the-art in fine-grained sentiment prediction on the CMU-MOSI and CMU MOSEI benchmarks.

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Mufan Xu, Gewen Liang, Kehai Chen, Wei Wang, Xun Zhou, Muyun Yang, Tiejun Zhao, Min Zhang

Specializing Large Models for Oracle Bone Script Interpretation via Component-Grounded Multimodal Knowledge Augmentation (2026.acl-long)

Challenge:	Existing methods that confuse tool utilization with knowledge reasoning harm readability and give rise to tool invocation hallucinations.
Approach:	They propose to decouple LLM from tool invocation tasks by establishing a memory module with explicit descriptions of query statements and a query memory module to facilitate the KGQA process.
Outcome:	The proposed method achieves state-of-the-art on WebQSP and CWQ benchmarks.

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Jianing Zhang, Runan Li, Honglin Pang, Ding Xia, Zhou Zhu, Qian Zhang, Chuntao Li, Xi Yang

Prosody-TTS: Improving Prosody with Masked Autoencoder and Conditional Diffusion Model For Expressive Text-to-Speech (2023.findings-acl)

Challenge:	Existing methods for deciphering ancient Chinese Oracle Bone Script (OBS) treat deciphering as a closed-set image recognition problem, which fails to bridge the "interpretation gap" .
Approach:	They propose a vision-language model framework that integrates a VLM and an LLM to automate a reasoning chain of component identification and knowledge retrieval.
Outcome:	The proposed framework yields more detailed and precise decipherments compared to baseline methods.

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Rongjie Huang, Chunlei Zhang, Yi Ren, Zhou Zhao, Dong Yu

Rhombus: Incentivizing Coordination in Parallel Thinking through Reinforcement Learning (2026.findings-acl)

Challenge:	Expressive text-to-speech aims to generate high-quality samples with rich prosody . prosodic attributes in highly dynamic voices are difficult to capture and model without intonation .
Approach:	They propose a pipeline that enhances prosody modeling and sampling by introducing a self-supervised masked autoencoder and a diffusion model to sample diverse prosodic patterns within the latent space.
Outcome:	The proposed pipeline achieves new state-of-the-art in text-to-speech with natural and expressive synthesis.

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Ziyuan Nan, Qi Yi, Di Huang, Yutong Wu, Guanhua Huang, Xue Gong, Kejiao Li, Yuhao Jiang, Chenchen Zhang, Zenan Xu, Xing Hu, Bo Zhou

Event-Centric Query Expansion in Web Search (2023.acl-industry)

Challenge:	Parallel thinking is a promising avenue for scaling test-time compute in Large Language Models . however, coordinating the exploration and aggregation stages remains challenging .
Approach:	They propose a parallel thinking framework that explicitly incentivizes coordination between components via end-to-end reinforcement learning.
Outcome:	The proposed framework improves accuracy by 6.0% over long chain-of-thought baselines while reducing wall-clock latency by 39.4% under matched token budgets.

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Yanan Zhang, Weijie Cui, Yangfan Zhang, Xiaoling Bai, Zhe Zhang, Jin Ma, Xiang Chen, Tianhua Zhou

Just Fine-tune Twice: Selective Differential Privacy for Large Language Models (2022.emnlp-main)

Challenge:	Existing studies rely on long-term search log mining to improve search experience . EQE system is a novel event retrieval framework that can select the best expansion from a significant amount of potential events quickly and accurately.
Approach:	They propose a QE system that uses a four-stage event retrieval framework . they collect news headlines and then refine a dual-tower semantic model to serve as an encoder .
Outcome:	The proposed system can select the best expansion from a significant amount of potential events quickly and accurately.

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Weiyan Shi, Ryan Shea, Si Chen, Chiyuan Zhang, Ruoxi Jia, Zhou Yu

Multi-Task Learning with Language Modeling for Question Generation (D19-1)

Challenge:	Existing approaches to protect language models from privacy leakage suffer from limited user control and low utility . et al., 2018: a novel framework that achieves SDP for state-of-the-art large transformer-based models.
Approach:	They propose a framework that applies differential privacy to large language models . they use redacted in-domain data to fine-tune the model with original in- domain data .
Outcome:	The proposed framework achieves strong utility compared to baselines.

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Wenjie Zhou, Minghua Zhang, Yunfang Wu

Crabs: Consuming Resource via Auto-generation for LLM-DoS Attack under Black-box Settings (2025.findings-acl)

Challenge:	Existing work on answer-aware questions generates a sentence and answer span as input . previous work on QG was mainly tackled by rule-based approach and neural-based one .
Approach:	They propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure.
Outcome:	The proposed model improves on SQuAD and MARCO datasets and human evaluation proves it.

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Yuanhe Zhang, Zhenhong Zhou, Wei Zhang, Xinyue Wang, Xiaojun Jia, Yang Liu, Sen Su