Papers by Jun Zhou

A Deep Learning-Based System for PharmaCoNER (D19-57)

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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Ying Xiong, Yedan Shen, Yuanhang Huang, Shuai Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, Yi Zhou

Information Retrieval

Challenge:	Efficient access to mentions of clinical entities is very important for using clinical text.
Approach:	They developed a pipeline system based on deep learning methods for this shared task . it achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average LSTM score of 0.8391 on track 2 .
Outcome:	The proposed system achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average score of 0.8391 on track 2.

Unified Thinker: A General Reasoning Core for Image Generation (2026.acl-long)

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Sashuai Zhou, Qiang Zhou, Jijin Hu, Hanqing Yang, Yue Cao, Junpeng Ma, Yinchao Ma, Jun Song, Tiezheng Ge, Cheng Yu, Bo Zheng, Zhou Zhao

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

Challenge:	generative models struggle with logic-intensive instruction following, exposing a persistent reasoning–execution gap.
Approach:	They propose a task-agnostic reasoning architecture for general image generation . they propose pixel-level feedback to ground the Thinker's policy in pixel feedback .
Outcome:	The proposed system significantly improves image reasoning and generation quality.

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

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

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

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

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

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

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

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

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

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

Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation (2025.emnlp-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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Chaojun Nie, Jun Zhou, Guanxiang Wang, Shisong Wu, Zichen Wang

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

Challenge:	Existing approaches to embed knowledge into large language models have some limitations . static nature of training data and lack of knowledge in domains create knowledge gaps .
Approach:	They propose a method that iteratively cycles between sampling generations and optimizing the model through calculated rewards.
Outcome:	The proposed method outperforms baseline approaches on medical, legal, astronomy, and current events 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

Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation (2024.lrec-main)

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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Zhigang Chen, Benjia Zhou, Jun Li, Jun Wan, Zhen Lei, Ning Jiang, Quan Lu, Guoqing Zhao

Emotion Classification by Jointly Learning to Lexiconize and Classify (2020.coling-main)

Challenge:	Previous Sign Language Translation methods have relied on gloss annotations to improve performance, but labeling high-quality glosses is labor-intensive and inefficient.
Approach:	They propose to integrate Large Language Model (LLM) into SLT by factorizing learning into two stages to improve the learning curve.
Outcome:	The proposed approach improves on three SLT datasets conducted under the gloss-free setting.

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Deyu Zhou, Shuangzhi Wu, Qing Wang, Jun Xie, Zhaopeng Tu, Mu Li

Text Classification

Challenge:	Existing approaches to identify emotions in short text are limited and lack coverage and inaccuracies when applied to informal short text.
Approach:	They propose a novel emotional network to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context.
Outcome:	The proposed model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.

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

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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

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-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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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou

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

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

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

Knowledge Representation

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.

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

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

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

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

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

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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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

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

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

Information Retrieval

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

Robust Preference Optimization via Dynamic Target Margins (2025.findings-acl)

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Jie Sun, Junkang Wu, Jiancan Wu, Zhibo Zhu, Xingyu Lu, Jun Zhou, Lintao Ma, Xiang Wang

Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm (2024.findings-acl)

Challenge:	Direct Preference Optimization (DPO) is an efficient method for ensuring safety and reliability in practical applications.
Approach:	They propose a dynamic target margin preference optimization algorithm that adjusts reward margins at the pairwise level.
Outcome:	The proposed method achieves an average 4.4% improvement over baselines, setting new benchmarks for state-of-the-art performance.

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Qiang Gao, Zixiang Meng, Bobo Li, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

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

Challenge:	Document-level event extraction aims to extract structured information from unstructured text.
Approach:	They propose a cross-document event extraction pipeline that integrates event information from multiple documents and provides a comprehensive perspective on events.
Outcome:	The proposed pipeline achieves about 72% F1 in end-to-end cross-document event extraction, setting up a benchmark for future research.

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

TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack (2022.emnlp-main)

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

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Yu Cao, Dianqi Li, Meng Fang, Tianyi Zhou, Jun Gao, Yibing Zhan, Dacheng Tao

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

Challenge:	Existing adversarial models rely on keyword matching and ignore relevant contextual relations for answer prediction.
Approach:	They propose to use keyword matching to attack model with two biases that rely on a perturbed answer sentence and a distracting answer sentence to misguide model.
Outcome:	The proposed method produces fluent and grammatical adversarial contexts while maintaining gold answers.

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

Sentiment Analysis

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

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

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

ToolDreamer: Instilling LLM Reasoning Into Tool Retrievers (2026.eacl-long)

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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Saptarshi Sengupta, Zhengyu Zhou, Jun Araki, Xingbo Wang, Bingqing Wang, Suhang Wang, Zhe Feng

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

Challenge:	Existing retrieval models rank tools based on similarity between query and tool description (TD) Existing tools are not conditioned to learn tool-to-tool relationships (middle).
Approach:	They propose a framework that conditions retrieval models to fetch tools based on hypothetical (synthetic) TD generated using an LLM.
Outcome:	The proposed framework improves the performance of sparse and dense retrievers with and without training, showcasing its flexibility.

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

DelucionQA: Detecting Hallucinations in Domain-specific Question Answering (2023.findings-emnlp)

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

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Mobashir Sadat, Zhengyu Zhou, Lukas Lange, Jun Araki, Arsalan Gundroo, Bingqing Wang, Rakesh Menon, Md Parvez, Zhe Feng

Video Dialog via Progressive Inference and Cross-Transformer (D19-1)

Challenge:	Hallucination is a well-known phenomenon in text generated by large language models . state-of-the-art LLMs still have a number of weaknesses, including the tendency to generate hallucinatory statements without considering the factuality .
Approach:	They propose a dataset that captures hallucinations made by retrieval-augmented LLMs . they propose to use these methods to help detect hallucinosity in QA tasks .
Outcome:	The proposed method captures hallucinations made by retrieval-augmented LLMs for QA tasks.

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Weike Jin, Zhou Zhao, Mao Gu, Jun Xiao, Furu Wei, Yueting Zhuang

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

Challenge:	Existing visual dialog methods use RNN to encode the dialog history as a vector representation . a new method for video dialog is proposed, which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous.
Approach:	They propose a method which progressively updates query information based on dialog history and video content until the agent thinks it is sufficient and unambiguous.
Outcome:	The proposed method can be used to infer video dialog answers on large-scale datasets.

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

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

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

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Minghui Qiu, Liu Yang, Feng Ji, Wei Zhou, Jun Huang, Haiqing Chen, Bruce Croft, Wei Lin

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

Challenge:	Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems.
Approach:	They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance.
Outcome:	The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist.

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

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

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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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

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

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

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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 .

LongHeads: Multi-Head Attention is Secretly a Long Context Processor (2024.findings-emnlp)

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Yi Lu, Xin Zhou, Wei He, Jun Zhao, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

NarGINA: Towards Accurate and Interpretable Children’s Narrative Ability Assessment via Narrative Graphs (2025.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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Jun Zhong, Longwei Xu, Li Kong, Xianzhuo Li, Dandan Liang, Junsheng Zhou

Training-Free Adaptive Speculative Decoding via Linguistic Priors (2026.findings-acl)

Challenge:	Existing methods for assessing children's narrative ability are limited to evaluating completeness of narrative content and the coherence of expression, as well as interpretability of assessment results.
Approach:	They propose a computational framework for assessing narrative ability using a narrative graph to provide a concise and structured summary representation of narrative text.
Outcome:	The proposed framework achieves significant performance improvement over baselines while possessing good interpretability.

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Jingyi Wang, Jiaqi Huang, Zunnan Xu, Jun Zhou, Kehong Yuan, Xiang Qian

Syntactic Text Processing

Challenge:	Speculative decoding (SPD) is a promising technique to accelerate Large Language Models (LLMs). current approaches neglect the inherent heterogeneity of natural language and fail to distinguish between semantically-rich content and structurally-predictable syntax.
Approach:	They propose a training-free framework that leverages linguistic priors to enable adaptive drafting and verification.
Outcome:	The proposed framework significantly accelerates inference without additional training.

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

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

Machine Translation

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.

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

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

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

Understanding Conflicts in Multi-Objective Alignment through Reward Consistency (2026.findings-acl)

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Zhihao Xu, Yongqi Tong, Xin Zhang, Jun Zhou, Xiting Wang

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

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

Information Retrieval

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

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

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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.

RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library (2026.findings-eacl)

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Jiapeng Wang, Jinhao Jiang, Zhiqiang Zhang, Jun Zhou, Xin Zhao

CoRE: A Fine-Grained Code Reasoning Benchmark Beyond Output Prediction (2026.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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Jun Gao, Yun Peng, Qian Qiao, Changhai Zhou, Yuhua Zhou, Shiyang Zhang, Shichao Weng, Zhenchang Xing, Xiaoxue Ren

“I See What You Did There”: Can Large Vision-Language Models Understand Multimodal Puns? (2026.acl-long)

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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Naen Xu, Jiayi Sheng, Changjiang Li, Chunyi Zhou, Yuyuan Li, Tianyu Du, Jun Wang, Zhihui Fu, Jinbao Li, Shouling Ji

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

Challenge:	Puns are a common form of rhetorical wordplay that exploits polysemy and phonetic similarity to create humor.
Approach:	They propose a multimodal pun generation pipeline and a model to evaluate their understanding of puns.
Outcome:	The proposed benchmark improves the understanding of multimodal puns by 16.5% in the F1 test.

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

Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models (2025.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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Liyang He, Chenglong Liu, Rui Li, Zhenya Huang, Shulan Ruan, Jun Zhou, Enhong Chen

Understanding and Mitigating Overrefusal in LLMs from an Unveiling Perspective of Safety Decision Boundary (2025.emnlp-main)

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

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Licheng Pan, Yongqi Tong, Xin Zhang, Xiaolu Zhang, Jun Zhou, Zhixuan Chu

UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining (2026.findings-acl)

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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Changhao Wang, null Yunfeiyu, Xinhao Yao, Jiaolong Yang, Lu Yu, Junpeng Fang, Chaobo Li, Riccardo Cantoro, Qing Cui, Jun Zhou

Challenge:	Large Language Models (LLMs) scaling is limited by data quality and domain mixing and instance selection are two separate problems.
Approach:	They propose a framework that unifies mixing and selection without training proxy models or relying on external reference datasets.
Outcome:	The proposed framework achieves 2.0 data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization.

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

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

Ask Question First for Enhancing Lifelong Language Learning (2022.coling-1)

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

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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.

Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompts (2023.findings-emnlp)

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Gangwei Jiang, Caigao Jiang, Siqiao Xue, James Zhang, Jun Zhou, Defu Lian, Ying Wei

RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution (2025.emnlp-main)

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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Jiahui Li, Lin Li, Tai-Wei Chang, Kun Kuang, Long Chen, Jun Zhou, Cheng Yang

KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)

Challenge:	Experimental results demonstrate the superiority of our approach to aligning large language models with human preferences.
Approach:	They propose a method that evaluates and assigns specific credit to each token using an off-the-shelf reward model.
Outcome:	The proposed method evaluates and assigns specific credit to each token using an off-the-shelf reward model.

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Jun Zhang, Yan Yang, Chencai Chen, Liang He, Zhou Yu

Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)

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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Linyu Fan, Wu Wu Yiheng, Jun Xie, Junhui Li, Fang Kong, Guodong Zhou

Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)

Challenge:	Recent studies on AMR parsing often regard this task as a seq2seq translation problem.
Approach:	They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding.
Outcome:	The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0.

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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

LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models (2026.acl-long)

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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Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Zhou, Yankai Lin, Ji-Rong Wen, Chongxuan Li

CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph (2024.acl-demos)

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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Tong Zhou, Yubo Chen, Kang Liu, Jun Zhao

Augmenting Reasoning Capabilities of LLMs with Graph Structures in Knowledge Base Question Answering (2024.findings-emnlp)

Challenge:	Large language models (LLMs) are susceptible to generating hallucinated content and often encompass factually inaccurate information.
Approach:	They propose a framework that leverages knowledge graphs to address the limitations of Large Language Models (LLMs) they identify and decompose required knowledge triples that are not present in the KG, enriching them and aligning updates with real-world demands.
Outcome:	The proposed framework reduces hallucinations and increases factual accuracy in QA scenarios while retaining the same quality of knowledge.

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Hao Wang, Jun Yang, Jing Xu, Ruanmin Cao, HaoYu Wang

World to Code: Multi-modal Data Generation via Self-Instructed Compositional Captioning and Filtering (2024.emnlp-main)

Challenge:	Recent work uses Large Language Models (LLMs) for semantic parsing to address Knowledge Base Question Answering tasks.
Approach:	They propose a framework that augments reasoning capabilities of LLMs with Graph Structures in Knowledge Base Question Answering to retrieve question-related graph structures.
Outcome:	The proposed framework outperforms existing methods on GrailQA and WebQSP under the few-shot setting.

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Jiacong Wang, Bohong Wu, Haiyong Jiang, Zhou Xun, Xin Xiao, Haoyuan Guo, Jun Xiao

Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)

Challenge:	Recent advances in Vision-Language Models and the scarcity of high-quality multi-modal alignment data have inspired numerous researches on synthetic VLM data generation.
Approach:	They propose a multi-modal data construction pipeline that organizes the final output into a Python code format.
Outcome:	The proposed pipeline improves visual question answering and visual grounding benchmarks across different VLMs.

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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen

SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities (2023.findings-emnlp)

Challenge:	Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks.
Approach:	They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs.
Outcome:	The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs.

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Dong Zhang, Shimin Li, Xin Zhang, Jun Zhan, Pengyu Wang, Yaqian Zhou, Xipeng Qiu

KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model (2023.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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Lei Geng, Xu Yan, Ziqiang Cao, Juntao Li, Wenjie Li, Sujian Li, Xinjie Zhou, Yang Yang, Jun Zhang

Multilinguality

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.

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment (2026.acl-long)

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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

LSSF: Safety Alignment for Large Language Models through Low-Rank Safety Subspace Fusion (2025.acl-long)

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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Guanghao Zhou, Panjia Qiu, Cen Chen, Hongyu Li, Jason Chu, Xin Zhang, Jun Zhou

Optimizing Language Models with Fair and Stable Reward Composition in Reinforcement Learning (2024.emnlp-main)

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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Jiahui Li, Hanlin Zhang, Fengda Zhang, Tai-Wei Chang, Kun Kuang, Long Chen, Jun Zhou

Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information (2024.lrec-main)

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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Qiang Gao, Bobo Li, Zixiang Meng, Yunlong Li, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators (N18-2)

Challenge:	Existing cross-document event coreference resolution models lack the ability to capture long-distance dependencies.
Approach:	They propose to construct document-level Rhetorical Structure Theory trees and cross-document Lexical Chains to model structural and semantic information of documents.
Outcome:	The proposed model outperforms baseline models on English and Chinese datasets by large margins.

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Cen Chen, Yinfei Yang, Jun Zhou, Xiaolong Li, Forrest Sheng Bao

Challenge:	Recent studies on review helpfulness prediction require labeled samples for each domain/category of interest.
Approach:	They propose a convolutional neural network based model which leverages word-level and character-based representations to transfer knowledge between domains.
Outcome:	The proposed model outperforms the state-of-the-art on the Amazon product review dataset.

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

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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 Autoformalization Using Direct Dependency Retrieval (2026.acl-long)

Challenge:	Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question.
Approach:	They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance.
Outcome:	Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance.

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Shaoqi Wang, Lu Yu, Siwei Lou, Feng Yan, Chunjie Yang, Qing Cui, Jun Zhou

OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs (2024.findings-emnlp)

Challenge:	Existing methods for hallucinate formal dependencies lack scalability and precision to leverage ever-growing public datasets.
Approach:	They propose a retrieval-augmented framework based on Direct Dependency Retrieval to generate formal dependencies from natural-language mathematical descriptions and verify their existence via an efficient Suffix Array Check (SAC).
Outcome:	The proposed framework outperforms state-of-the-art methods in retrieval precision and recall and can be used to validate formal representations in a public dataset.

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Jintian Zhang, Cheng Peng, Mengshu Sun, Xiang Chen, Lei Liang, Zhiqiang Zhang, Jun Zhou, Huajun Chen, Ningyu Zhang

COF: Adaptive Chain of Feedback for Comparative Opinion Quintuple Extraction (2025.coling-main)

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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Qingting Xu, Kaisong Song, Chaoqun Liu, Yangyang Kang, Xiabing Zhou, Jun Lin, Yu Hong

Low-Resource NLP

Challenge:	Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text.
Approach:	They propose a model-unaware adaptive chain-of-feedback method to extract quintuples from product review text.
Outcome:	The proposed method improves performance on three benchmarks.

Chain-of-Quizzes: Pedagogy-inspired Example Selection in In-Context-Learning (2024.findings-acl)

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Yiquan Wu, Anlai Zhou, Yuhang Liu, Yifei Liu, Adam Jatowt, Weiming Lu, Jun Xiao, Kun Kuang

Low-Resource NLP

Challenge:	In-context learning (ICL) is a powerful tool for enhancing large language models (LLMs) by mimicking the human learning process.
Approach:	They propose a Chain-of-Quizzes framework that uses LLMs to answer a quiz to sift 'good' examples, combine them iteratively with the increasing complexity, and utilize a final exam to gauge the combined example chains.
Outcome:	The proposed framework outperforms baseline models on diverse reasoning datasets and shows that it is scalable and can be used in future research.

Deputy: Accelerating Large Language Model Inference with Dynamic Low-Rank Substitution (2026.findings-acl)

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Yuhua Zhou, Shichao Weng, Changhai Zhou, Yuhan Wu, Qian Qiao, Jun Gao, Fei Yang, Aimin Pan

Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors (2026.acl-long)

Challenge:	Existing dynamic schemes such as early-exit and layer-drop reduce FLOPs but break batch processing or introduce KV-cache inconsistency.
Approach:	They propose a dynamic low-rank substitution framework that employs a lightweight decision module at each layer to dynamically determine the execution branch for different tokens.
Outcome:	The proposed model reduces computation by approximately 40% compared to the original dense model while outperforming existing baseline methods.

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Rui Yin, Tianxu Han, Naen Xu, Changjiang Li, Ping He, Chunyi Zhou, Jun Wang, Zhihui Fu, Tianyu Du, Jinbao Li, Shouling Ji

Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism (2021.acl-long)

Challenge:	Existing methods to inject safety-aligned large language models rely on token-level mappings, which do not guarantee sustained harmful output.
Approach:	They propose a method that directly modifies model weights to map a trigger to an attacker-specified response.
Outcome:	The proposed method achieves high triggered attack success while maintaining non-triggered safety and general utility.

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Tong Zhou, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Kun Niu, Weifeng Chong, Shengping Liu

Latent Learningscape Guided In-context Learning (2024.findings-acl)

Challenge:	Existing methods for ICD coding ignore the long-tail of code frequency or noisy clinical notes.
Approach:	They propose to use an interactive shared representation network to model code co-occurrences while focusing on the clinical note's noteworthy part and extract valuable information through a self-distillation learning mechanism to solve the long-tail problem.
Outcome:	The proposed model reduces the long-tail of code frequency and noise in clinical notes and extracts valuable information through a self-distillation learning mechanism.

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Anlai Zhou, Sunshine Jiang, Yifei Liu, Yiquan Wu, Kun Kuang, Jun Xiao

Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing (2026.acl-long)

Challenge:	Existing methods to select demonstrations based on surface-level semantic similarities fall short of identifying the most fitting ones.
Approach:	They propose a method that characterizes latent learningscape features of demonstrations and uses them to create more effective prompts.
Outcome:	The proposed method outperforms leading models in arithmetic, commonsense, and symbolic reasoning tasks showing an average increase in scores by 7.4 percentage points.

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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.

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization (2026.findings-acl)

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Chenxi Zhou, Pengfei Cao, Jiang Li, Bohan Yu, Jinyu Ye, Jun Zhao, Kang Liu

Challenge:	Existing research on PTQ spans three primary directions.
Approach:	They conduct a systematic analysis of post-training quantization failures using PTQ . they show that targeted repair can mitigate Signal Degradation but remains ineffective for Computation Collapse .
Outcome:	The proposed method mitigates Signal Degradation but remains ineffective for Computation Collapse.

Towards Explainable Diagnosis: A Self-learned Explanatory Knowledge Base Approach (2026.acl-long)

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Dongqi Huang, Tong Zhou, Zhuoran Jin, Shenghui Shi, null Maoyujiao, Kang Liu, Jun Zhao, Yubo Chen

Explainability & Interpretability in NLP

Challenge:	Large language models (LLMs) have great potential to facilitate explainable diagnosis, but their effectiveness is often constrained by insufficient diagnostic expertise.
Approach:	They propose a unified LLM-based framework for faithful and explainable diagnosis that builds a high-quality diagnostic knowledge base through a record-driven explanation learning paradigm.
Outcome:	The proposed framework outperforms baselines on the DiReCT and JAMA benchmarks and improves the explanation completeness metric from 64.5% to 76.9% over the best existing methods.

A Discrete CVAE for Response Generation on Short-Text Conversation (D19-1)

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Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Guodong Zhou, Shuming Shi

Dialogue Systems & Conversational Agents

Challenge:	Neural conversation models are easy to generate bland and generic responses . however, their improvement of generating high-quality responses is still unsatisfactory .
Approach:	They propose to use a discrete latent variable with an explicit semantic meaning to improve the conditional variational autoencoder on short-text conversation.
Outcome:	The proposed model outperforms various kinds of generation models under automatic and human evaluations and generates more diverse and informative responses.

Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models (2026.findings-acl)

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Chenxi Zhou, Pengfei Cao, Jiang Li, Bohan Yu, Jinyu Ye, Jun Zhao, Kang Liu

A Relation-Oriented Clustering Method for Open Relation Extraction (2021.emnlp-main)

Challenge:	Existing scaling laws focus on general performance, overlooking crucial fine-grained factors and how quantization differentially impacts diverse knowledge capabilities.
Approach:	They propose a framework that unifies model size, bit-width, and fine-grained factors into memorization, application, and reasoning.
Outcome:	The proposed framework shows strong fit and cross-architecture consistency on 293 different PTQ configurations.

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Jun Zhao, Tao Gui, Qi Zhang, Yaqian Zhou

CoAug: Combining Augmentation of Labels and Labelling Rules (2023.findings-acl)

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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Rakesh R. Menon, Bingqing Wang, Jun Araki, Zhengyu Zhou, Zhe Feng, Liu Ren

ShieldHead: Decoding-time Safeguard for Large Language Models (2025.findings-acl)

Challenge:	Named Entity Recognition (NER) tasks require large labeled datasets to perform well.
Approach:	They propose a co-augmentation framework that bootstraps predictions from each model to improve few-shot models and rule-augmentation models by bootstrapping them.
Outcome:	The proposed model outperforms strong weak-supervision-based models by 6.5 F1 points . the proposed model can learn from limited labeled data and perform better on small datasets .

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Zitao Xuan, Xiaofeng Mao, Da Chen, Xin Zhang, Yuhan Dong, Jun Zhou

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

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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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

Lightweight Haar Wavelet Subband Pruning for LLMs (2026.findings-acl)

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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Jiang Li, Pengfei Cao, Chenxi Zhou, Tian Lan, Xiangdong Su, Kang Liu, Jun Zhao, Guanglai Gao

Challenge:	Large language models (LLMs) have impressive performance but require computational and memory resources.
Approach:	They propose a post-training framework that uses a Haar wavelet transform to prune weights.
Outcome:	The proposed pruning framework reduces pruning time and computational costs by removing less important weights while preserving model architecture.

ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution (2026.acl-industry)

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Jihong Wang, Jiamu Zhou, Weiming Zhang, Teng Wang, Weiwen Liu, Zhuosheng Zhang, Xingyu Lou, Weinan Zhang, Huarong Deng, Jun Wang

M2Edit: Locate and Edit Multi-Granularity Knowledge in Multimodal Large Language Model (2025.emnlp-main)

Challenge:	Xue et al., 2025): deploying autonomous web agents in production remains difficult due to site heterogeneity and long-horizon instability.
Approach:	They propose a knowledge-evolving agent that can be used to automate web workflows . they use human-in-the-loop knowledge adaptation and knowledge-aligned progressive summarization .
Outcome:	Experiments on WebArena, WebChoreAren and industrial deployment show it outperforms baselines.

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Yang Zhou, Pengfei Cao, Yubo Chen, Qingbin Liu, Dianbo Sui, Xi Chen, Kang Liu, Jun Zhao

What Factors Influence LLMs’ Judgments? A Case Study on Question Answering (2024.lrec-main)

Challenge:	Existing knowledge editing methods for MLLMs lack multi-granularity knowledge . existing knowledge editing approaches lack multimodality knowledge and generalize to multimodal data.
Approach:	They propose a multimodal knowledge editing method which integrates key knowledge layers within MLLMs and collaboratively edits them.
Outcome:	The proposed method improves visual generality performance on knowledge data of different granularities.

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Lei Chen, Bobo Li, Li Zheng, Haining Wang, Zixiang Meng, Runfeng Shi, Hao Fei, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

Improving Entity Linking through Semantic Reinforced Entity Embeddings (2020.acl-main)

Challenge:	Existing studies indicate that Large Language Models perform at a level comparable to humans with advantages of speed and cost-effectiveness in different fields.
Approach:	They propose to introduce four unexplored factors and a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies.
Outcome:	The proposed dimensions of question difficulty and answer quantity provide valuable insights into optimizing LLMs’ performance as judges.

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Feng Hou, Ruili Wang, Jun He, Yi Zhou

SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026.findings-acl)

Challenge:	Existing entity embeddings are effective, but too distinctive for linking models to learn contextual commonality.
Approach:	They propose a method to inject fine-grained semantic information into entity embeddings . they use word embedds of type words to generate semantic embeddngs based on existing embeddables a sample of semantic information is injected into the embedded entities .
Outcome:	The proposed method reduces the distinctiveness of existing embeddings and improves performance.

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Jie Sun, Yu Liu, Lu Han, Qiwen Deng, Xiang Shu, Yang Xiao, Lintao Ma, Xingyu Lu, Jun Zhou, Pengfei Liu, Jiancan Wu, Xiang Wang

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

Challenge:	Existing large-scale large-context models suffer from performance degradation when processing long numerical sequences.
Approach:	They propose a framework to mitigate attention dispersion by strategically inserting separator tokens into the model to recalibrat attention to local segments while preserving global context.
Outcome:	The proposed framework improves accuracy and reduces inference token consumption by 16.4% on 9 widely-adopted LLMs.

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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

Safety Alignment via Constrained Knowledge Unlearning (2025.acl-long)

Challenge:	Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction.
Approach:	They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack.
Outcome:	The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses.

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Zesheng Shi, Yucheng Zhou, Jing Li, Yuxin Jin, Yu Li, Daojing He, Fangming Liu, Saleh Alharbi, Jun Yu, Min Zhang

ChatUIE: Exploring Chat-based Unified Information Extraction Using Large Language Models (2024.lrec-main)

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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Jun Xu, Mengshu Sun, Zhiqiang Zhang, Jun Zhou

CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering (2025.emnlp-main)

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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Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, Luan Zhang