Papers by Shen Zhou

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

Challenge:	Current reranking models are optimized on static human annotations in isolation, decoupled from the downstream generation process.
Approach:	They propose a reinforcement learning framework that directly aligns reranking with LLM's generation quality.
Outcome:	Experiments on knowledge-intensive benchmarks show that RRPO outperforms strong baselines.

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

GCNet: Global-and-Context Collaborative Learning for Aspect-Based Sentiment Analysis (2024.lrec-main)

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

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Ting Zhou, Ying Shen, Yinghui Li

Sentiment Analysis

Challenge:	Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence.
Approach:	They propose a GCNet that explicitly leverages global semantic information to guide context encoding.
Outcome:	The proposed model outperforms state-of-the-art methods on three public datasets.

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

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

Corpus-Steered Query Expansion with Large Language Models (2024.eacl-short)

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.

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Yibin Lei, Yu Cao, Tianyi Zhou, Tao Shen, Andrew Yates

Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022.naacl-main)

Challenge:	Recent studies show query expansions generate hypothetical documents that answer queries as expansions.
Approach:	They propose a corpus-steered query expansion to promote incorporation of knowledge embedded within the corpus.
Outcome:	et al. analyzed corpus-based Query Expansion (CSQE) using LLMs to generate hypothetical documents that answer the query.

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Yu Li, Baolin Peng, Yelong Shen, Yi Mao, Lars Liden, Zhou Yu, Jianfeng Gao

Negative Focus Detection via Contextual Attention Mechanism (D19-1)

Challenge:	Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data.
Approach:	They propose a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks.
Outcome:	The proposed language model generalizes well across knowledge-grounded dialogue tasks.

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Longxiang Shen, Bowei Zou, Yu Hong, Guodong Zhou, Qiaoming Zhu, AiTi Aw

Challenge:	Negation is a universal but complicated linguistic phenomenon that reverses the polarity of a statement or its property into opposite.
Approach:	They propose a framework which consists of a Bidirectional Long Short-Term Memory neural network and a Conditional Random Fields layer to capture contextual information.
Outcome:	The proposed framework improves on the SEM’12 shared task corpus, yielding an absolute improvement of 2.11% over the state-of-the-art.

Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters (2024.acl-long)

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Yinghui Li, Zishan Xu, Shaoshen Chen, Haojing Huang, Yangning Li, Shirong Ma, Yong Jiang, Zhongli Li, Qingyu Zhou, Hai-Tao Zheng, Ying Shen

Visual Data in NLP

Challenge:	Existing studies focus on misspelled characters, ignoring faked characters which are more common and difficult to correct.
Approach:	They propose to use Chinese character checking to identify and correct wrong characters in texts by human annotation.
Outcome:	The proposed dataset is the first real-world visual and the largest human-crafted dataset for the Chinese character checking scenario.

Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)

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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

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

Challenge:	Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation.
Approach:	They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved.
Outcome:	The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment.

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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

Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents (2026.findings-acl)

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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Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao, Zhixu Li

Visual In-Context Learning for Large Vision-Language Models (2024.findings-acl)

Challenge:	Existing LLMs struggle to identify errors in financial documents, a study shows . 18% of financial practitioners make errors daily, one-third make errors several times weekly, and 59% make errors multiple times monthly.
Approach:	They introduce FinED-Bench, a publicly available Benchmark for financial error detection . it covers nine real-world financial scenarios and includes over 900 documents in 2025 . supervised fine-tuning can significantly improve the performance of weaker LLMs, they show .
Outcome:	The proposed benchmark covers nine real-world financial scenarios and includes over 900 documents reported in 2025 that are unseen by existing language models.

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Yucheng Zhou, Xiang Li, Qianning Wang, Jianbing Shen

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

Challenge:	Existing approaches to improve the performance of Large Visual Language Models (LVLMs) are limited by cross-modal interactions and representation disparities.
Approach:	They propose a Visual In-Context Learning method that retrieves images via a 'Retrieval & Rerank' paradigm and summarises images with task intent and task-specific visual parsing to compose language-based demonstrations that reduce token count.
Outcome:	The proposed method reduces token count and alleviates cross-modal interaction problem on visual reasoning 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

CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning (2020.findings-emnlp)

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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Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, Xiang Ren

CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction (2023.emnlp-main)

Challenge:	Recent studies show that pre-trained language models perform well on commonsense-reasoning benchmark datasets, but building machines with commonsence to compose plausible sentences remains challenging.
Approach:	They propose a constrained text generation task for generative commonsense reasoning that generates a coherent sentence using common concepts.
Outcome:	The proposed task generates a coherent sentence describing an everyday scenario using common concepts over 35k concept-sets.

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Jingheng Ye, Yinghui Li, Qingyu Zhou, Yangning Li, Shirong Ma, Hai-Tao Zheng, Ying Shen

Robustness in NLP

Challenge:	Evaluating the performance of Grammatical Error Correction systems is a challenging task due to its subjectivity.
Approach:	They propose a method to evaluate GEC systems in multi-reference evaluation setting . they use consistent edit boundaries to eliminate bias caused by inconsistent edit boundaries .
Outcome:	The proposed evaluation metric eliminates bias caused by inconsistent edit boundaries on six English reference sets.

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

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

DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models (2025.findings-acl)

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

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Ying Zhou, Xinyao Wang, Yulei Niu, Yaojie Shen, Lexin Tang, Fan Chen, Ben He, Le Sun, Longyin Wen

A Systematic Survey of Automatic Prompt Optimization Techniques (2025.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for high-quality data synthesis.
Approach:	They propose a controllable data synthesis framework based on variational autoencoder which leverages diffusion models to reserve more information of original distribution and format structure in the learned latent distribution.
Outcome:	The proposed framework generates high-quality data with performance exceeding that of real data by 2%–7% on seven real-world datasets.

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Kiran Ramnath, Kang Zhou, Sheng Guan, Soumya Smruti Mishra, Xuan Qi, Zhengyuan Shen, Shuai Wang, Sangmin Woo, Sullam Jeoung, Yawei Wang, Haozhu Wang, Han Ding, Yuzhe Lu, Zhichao Xu, Yun Zhou, Balasubramaniam Srinivasan, Qiaojing Yan, Yueyan Chen, Haibo Ding, Panpan Xu, Lin Lee Cheong

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

Challenge:	Recent advances in prompt engineering have created impediments for end users to adopt . however, prompt engineering remains an impedance due to rapid advances in models, tasks, and associated best practices.
Approach:	They propose to define APO as a 5-part unifying framework and categorize all relevant works based on their salient features.
Outcome:	The proposed framework aims to improve the performance of large language models on various tasks.

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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.

MReD: A Meta-Review Dataset for Structure-Controllable Text Generation (2022.findings-acl)

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Chenhui Shen, Liying Cheng, Ran Zhou, Lidong Bing, Yang You, Luo Si

Text Generation

Challenge:	a new text generation dataset is needed to controllable text summarization, but it lacks the domain knowledge.
Approach:	They propose to use existing text generation datasets to leverage input and control signals . they propose to annotate each meta-review sentence manually with a control signal .
Outcome:	The proposed method can be used to control the structure of a text generation dataset . it can be applied to a variety of tasks, including a task with a large number of meta-review sentences .

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

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

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

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

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

Transparentize the Internal and External Knowledge Utilization in LLMs with Trustworthy Citation (2025.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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Jiajun Shen, Tong Zhou, Yubo Chen, Delai Qiu, Shengping Liu, Kang Liu, Jun Zhao

Responsible & Trustworthy NLP

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 .

Leveraging Web-Crawled Data for High-Quality Fine-Tuning (2024.findings-emnlp)

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Jing Zhou, Chenglin Jiang, Wei Shen, Xiao Zhou, Xiaonan He

Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents (2026.acl-long)

Challenge:	Currently, large language models are fine-tuned using expensive human-annotated data or GPT-4 generated data.
Approach:	They propose to use web-crawled data to train a language model on a smaller set of data . their results show that the model can convert web data with irregular formats into high-quality ones .
Outcome:	The proposed model outperforms open-source models larger than 32B and outperformed open-sourced models such as GPT-3.5.

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Yiting Shen, Kun Li, Wei Zhou, Songlin Hu

Challenge:	Existing benchmarks focus on direct queries for a factual answer, but fail to evaluate the more crucial capability of actively applying memory to execute tasks.
Approach:	They propose a benchmark to evaluate whether agents can proactively leverage long-term memory to execute tool-based actions by selecting appropriate tools and grounding their parameters.
Outcome:	The proposed benchmarks show that 91.3% of tasks are memory-dependent . the benchmarks simulate persistent assistant usage, where users mention the same topic across long, interrupted interactions and expect previously established preferences and task states to be implicitly applied.

Focusing Condition: Inference-Time Self-Contrastive Steering Elicits Better Conditional Text Embeddings in LLMs (2026.acl-long)

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Zifeng Cheng, Lingyun Qian, Zhiwei Jiang, Cong Wang, Yafeng Yin, Fei Shen, Ao Zhou, Qing Gu

Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue (2024.findings-naacl)

Challenge:	Existing methods for extracting conditional text embeddings from large language models (LLMs) relying on prompts often fails to produce high-quality conditional embeddables, resulting in degradation of quality.
Approach:	They propose a plug-and-play method that constructs unconditional general text embeddings and uses them to refine conditional text embeds.
Outcome:	The proposed method improves performance of prompt-based methods on clustering, Semantic Textual Similarity, and triplet alignment datasets.

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Junkai Zhou, Liang Pang, Huawei Shen, Xueqi Cheng

An Ensemble-of-Experts Framework for Rehearsal-free Continual Relation Extraction (2024.findings-acl)

Challenge:	emergence of large language models (LLMs) improves capabilities of dialogue systems . but they lack communication skills, which make them more like information seeking tools .
Approach:	They propose to empower LLMs with communication skills through inner monologues . they use a benchmark to evaluate the dialogue generation ability of the model .
Outcome:	The proposed model outperforms the baselines in the evaluation of communication skills.

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Shen Zhou, Yongqi Li, Xin Miao, Tieyun Qian

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

Challenge:	Existing methods for continual relation extraction (CRE) are rehearsal-based and need to store samples and thus may encounter privacy and security issues.
Approach:	They propose an Ensemble-of-Experts framework for rehearsal-free continual relation extraction that discriminates between experts and augments analogous relations across tasks.
Outcome:	The proposed method outperforms existing rehearsal-free methods and is even better than existing methods.

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

Episodic Memory Retrieval from LLMs: A Neuromorphic Mechanism to Generate Commonsense Counterfactuals for Relation Extraction (2024.findings-acl)

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

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Xin Miao, Yongqi Li, Shen Zhou, Tieyun Qian

CrowdSelect: SyntheticInstruction Data Selection with Multi-LLM Wisdom (2026.findings-eacl)

Challenge:	Large language models (LLMs) have achieved satisfactory performance in counterfactual generation, however, there are misalignments between LLMs and humans which hinder LLM from handling complex tasks like relation extraction.
Approach:	They propose to mimic the episodic memory retrieval mechanism of human hippocampus to align LLMs’ generation process with that of humans.
Outcome:	The proposed framework improves over existing methods in terms of quality of counterfactuals.

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Yisen Li, Lingfeng Yang, Wenxuan Shen, Pan Zhou, Yao Wan, Weiwei Lin, Dongping Chen

Machine Translation

Challenge:	Existing methods for capturing instruction-following complexity rely on single-dimensional signals, but they fail to capture complexity across diverse fields.
Approach:	They propose three foundational metrics that leverage Multi-LLMs wisdom to capture instruction-response pair characteristics and propose CrowdSelect, an integrated metric incorporating a clustering-based approach to maintain response diversity.
Outcome:	The proposed metrics outperform existing models on MT-bench and Arena-hard and show improvements of 4.81% on full and LoRA fine-tuning.

Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (2023.emnlp-main)

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Shwai He, Run-Ze Fan, Liang Ding, Li Shen, Tianyi Zhou, Dacheng Tao

Green & Sustainable NLP

Challenge:	Extensive experiments show that MEO significantly improves computational efficiency . compared to dense networks, sparsely activated networks only employ a few parameters for each input .
Approach:	They propose a method that merges multiple experts into one to reduce computation costs . they demonstrate that a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters for each input .
Outcome:	The proposed approach reduces the computational cost to that of a single expert by 83.3% compared to 82.6% in vanilla MoE.

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

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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

Responsible & Trustworthy NLP

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.

Compatibility-Aware Dynamic Fine-Tuning for Large Language Models (2026.acl-long)

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Yucheng Zhou, Junwei Sheng, Qianning Wang, Jianbing Shen

IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)

Challenge:	Recent work attributes optimization instability to the low probability of demonstrations being incompatible with the sample level.
Approach:	They propose a Dynamic Fine-Tuning extension of DFT that controls sample-level optimization variance.
Outcome:	The proposed model can generalize token-level stabilization to the sample level while remaining fully supervised and free of reward modeling.

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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

Low-Resource NLP

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.

RepEval: Effective Text Evaluation with LLM Representation (2024.emnlp-main)

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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.

Evidence-Augmented Policy Optimization with Reward Co-Evolution for Long-Context Reasoning (2026.acl-long)

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Xin Guan, Zijian Li, Shen Huang, Pengjun Xie, Jingren Zhou, Jiuxin Cao

ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification (2022.acl-long)

Challenge:	Evidence-Augmented Policy Optimization (EAPO) improves long-context reasoning performance . Xu et al., 2025): large language models are a critical part of NLP .
Approach:	They propose an Evidence-Augmented Reasoning paradigm that uses a group-relative reward to improve evidence quality.
Outcome:	EAPO significantly improves long-context reasoning performance compared to baselines.

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Yucheng Zhou, Tao Shen, Xiubo Geng, Guodong Long, Daxin Jiang

Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation (2021.findings-emnlp)

Challenge:	Existing work on event-centric reasoning fails to model event-level correlations . Existing studies limit their scope to specific scenarios or overlook event- level correlations.
Approach:	They propose to pre-train a general Correlation-aware context-to-Event Transformer for event-centric reasoning by highlighting event-level correlations with effective training.
Outcome:	The proposed model is applicable to a wide range of event-centric reasoning scenarios, considering its versatility of event correlation types, application formulations, and reasoning types.

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Lei Shen, Jinchao Zhang, Jiao Ou, Xiaofang Zhao, Jie Zhou

Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation (2024.acl-long)

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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Shicheng Xu, Liang Pang, Mo Yu, Fandong Meng, Huawei Shen, Xueqi Cheng, Jie Zhou

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

Challenge:	Existing studies show that LLMs face challenges in effectively using retrieved information . authors propose a method that considers LLM as "Information Refiner"
Approach:	They propose a method that considers LLMs as "Information Refiners" they propose INFO-RAG, which is low-cost and general across various tasks .
Outcome:	The proposed method improves performance of LLaMA2 by 9.39% relative points . it is low-cost and general across various tasks, and is robust and in-context learning is possible .

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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.

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

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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

DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis (2022.coling-1)

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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Xian Wu, Shuxin Yang, Zhaopeng Qiu, Shen Ge, Yangtian Yan, Xingwang Wu, Yefeng Zheng, S. Kevin Zhou, Li Xiao

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts (2026.acl-long)

Challenge:	X-ray and CT are the gold standard for COVID-19 diagnosis and treatment . however, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists.
Approach:	They propose to use X-ray and CT to generate medical reports automatically . they evaluate DeltaNet on a COVID-19 dataset, where it outperforms state-of-the-art approaches .
Outcome:	The proposed system outperforms state-of-the-art methods on a COVID-19 dataset.

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Haolei Xu, Haiwen Hong, Hongxing Li, Rui Zhou, Yang Zhang, Longtao Huang, Hui Xue, Yongliang Shen, Weiming Lu, Yueting Zhuang

A Survey on Training-free Alignment of Large Language Models (2025.findings-emnlp)

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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Birong Pan, Yongqi Li, Weiyu Zhang, Wenpeng Lu, Mayi Xu, Shen Zhou, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

Self-Rewarding Large Vision-Language Models for Optimizing Prompts in Text-to-Image Generation (2025.findings-acl)

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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Hongji Yang, Yucheng Zhou, Wencheng Han, Jianbing Shen

MovieChats: Chat like Humans in a Closed Domain (2020.emnlp-main)

Challenge:	Existing methods for rewriting text-to-image models require specialized vocabulary . a new approach uses large vision language models to optimize text-based models .
Approach:	They propose a prompt optimization framework that rephrases a user prompt into a text-to-image model by using large vision language models as solver and reward model.
Outcome:	The proposed model outperforms existing models on two popular datasets.

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Hui Su, Xiaoyu Shen, Zhou Xiao, Zheng Zhang, Ernie Chang, Cheng Zhang, Cheng Niu, Jie Zhou

Prompting Large Language Models for Counterfactual Generation: An Empirical Study (2024.lrec-main)

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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Yongqi Li, Mayi Xu, Xin Miao, Shen Zhou, Tieyun Qian

Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication (2025.emnlp-main)

Challenge:	Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks, but their ability to generate counterfactuals has not been examined systematically.
Approach:	They propose a framework to evaluate LLMs' ability to generate counterfactuals based on key factors including intrinsic properties and prompt design.
Outcome:	The proposed framework examines the strengths and weaknesses of large language models (LLMs) and identifies factors that influence their ability to generate counterfactuals.

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Jocelyn J Shen, Akhila Yerukola, Xuhui Zhou, Cynthia Breazeal, Maarten Sap, Hae Won Park

RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining (2022.acl-long)

Challenge:	a recent study examines the role of personalization in enabling LLMs to serve as effective mediators in human communication for authentic connection.
Approach:	They leverage nonviolent communication theory to evaluate LLMs in detecting conversational breakdowns . they annotate a subset of dialogues and obtain fine-grained labels of communication breakdown types .
Outcome:	The proposed dataset analyzes human interactions and relationships in a human context.

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Hui Su, Weiwei Shi, Xiaoyu Shen, Zhou Xiao, Tuo Ji, Jiarui Fang, Jie Zhou

Diversifying Dialogue Generation with Non-Conversational Text (2020.acl-main)

Challenge:	Large-scale pretrained language models have achieved SOTA results on NLP tasks but are vulnerable to adversarial attacks especially for logographic languages like Chinese.
Approach:	They propose a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation, synonyms, typos, etc.
Outcome:	The proposed model outperforms baselines on 5 Chinese NLU tasks without sacrificing performance on clean testsets.

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Hui Su, Xiaoyu Shen, Sanqiang Zhao, Zhou Xiao, Pengwei Hu, Randy Zhong, Cheng Niu, Jie Zhou

GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond (2024.findings-naacl)

Challenge:	Neural network-based sequence-to-sequence models suffer from low diversity in open-domain dialogue generation.
Approach:	They propose a way to diversify dialogue generation by leveraging non-conversational text . they collect large-scale corpus from forum comments, idioms and book snippets .
Outcome:	The proposed model produces significantly more diverse responses without sacrificing relevance with context.

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Shen Zheng, Yuyu Zhang, Yijie Zhu, Chenguang Xi, Pengyang Gao, Zhou Xun, Kevin Chang

Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network (2020.emnlp-main)

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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Sihan Wang, Kaijie Zhou, Kunfeng Lai, Jianping Shen

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

Challenge:	Existing models of reinforcement learning use background planning and may suffer from low-quality simulated experiences.
Approach:	They propose a Monte Carlo Tree Search with Double-q Dueling network framework for task-completion dialogue policy learning.
Outcome:	The proposed method outperforms the previous model-based reinforcement learning methods and is robust to simulation errors.

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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

Reward Modeling Requires Automatic Adjustment Based on Data Quality (2024.findings-emnlp)

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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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.

SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval (2022.emnlp-industry)

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Kun Zhou, Yeyun Gong, Xiao Liu, Wayne Xin Zhao, Yelong Shen, Anlei Dong, Jingwen Lu, Rangan Majumder, Ji-rong Wen, Nan Duan

You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations (2026.findings-acl)

Challenge:	Existing methods for sapping negatives from large document pool suffer from the uninformative or false negative problem.
Approach:	They propose a method to sample negatives from a large document pool using a new sampling probability distribution.
Outcome:	The proposed method can be used to sample more ambiguous negatives on four public and one industry datasets.

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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

Unsupervised Dependency Graph Network (2022.acl-long)

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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Yikang Shen, Shawn Tan, Alessandro Sordoni, Peng Li, Jie Zhou, Aaron Courville

ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models (2023.emnlp-demo)

Challenge:	Recent work has identified properties of pretrained self-attention models that mirror those of dependency parse structures.
Approach:	They propose a model that encourages attention heads to model different dependency relations from raw corpora and a masked language modeling task.
Outcome:	The proposed model can induce dependency structures from raw corpora and the masked language modeling task without gold POS tags and any external information.

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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

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)

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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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

Text Generation

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.

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

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

Meta-Task Prompting Elicits Embeddings from Large Language Models (2024.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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Yibin Lei, Di Wu, Tianyi Zhou, Tao Shen, Yu Cao, Chongyang Tao, Andrew Yates

LawBench: Benchmarking Legal Knowledge of Large Language Models (2024.emnlp-main)

Challenge:	Existing methods for large language modeling are based on task-related instructions or prompts.
Approach:	They propose a method for generating high-quality sentence embeddings from Large Language Models (LLMs) using meta-task prompts.
Outcome:	The proposed method produces high-quality sentences without fine-tuning . it excels on STS benchmarks and in downstream tasks, surpassing models with similar prompts .

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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

Learning from LLM Agents: In-Context Generative Models for Text Casing in E-Commerce Ads (2025.emnlp-industry)

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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Yingxue Zhou, Tan Zhu, Tao Zeng, Zigeng Wang, Wei Shen

Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention (2020.coling-main)

Challenge:	Existing NER-based transformer models are expensive and lack contextual dependencies, making them less reliable when handling unseen or ad-specific terms, e.g., brand names.
Approach:	They propose a two-stage approach to casing correction in e-commerce ad content that leverages Chain-of-Actions to enforce content policies while accurately handling ads-specific terms.
Outcome:	The proposed model outperforms existing NER-based models and achieves near-LLM performance at a fraction of the cost.

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Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang

Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning (2021.findings-acl)

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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Yucheng Zhou, Xiubo Geng, Tao Shen, Jian Pei, Wenqiang Zhang, Daxin Jiang

Knowledge Representation

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.

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

Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together (N19-1)

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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Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

ToW: Thoughts of Words Improve Reasoning in Large Language Models (2025.naacl-long)

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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Zhikun Xu, Ming Shen, Jacob Dineen, Zhaonan Li, Xiao Ye, Shijie Lu, Aswin Rrv, Chitta Baral, Ben Zhou

PEGRL: Improving Machine Translation by Post-Editing Guided Reinforcement Learning (2026.findings-acl)

Challenge:	Unlike other data augmentation methods, thoughts of words (TOW) views next-word prediction as a core reasoning task and injects fine-grained thoughts into pre-training texts.
Approach:	They propose a training-time data-augmentation method called thoughts of words (TOW) that injects fine-grained thoughts directly into a next-word prediction task and teaches the model to understand how the observed next word is related to previous contexts.
Outcome:	The proposed method reduces model hallucination by 10% and improves reasoning performance by 7% to 9% on average.

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Yunzhi Shen, Hao Zhou, Xin Huang, Xue Han, Junlan Feng, Shujian Huang

Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback (2025.acl-long)

Challenge:	Reinforcement learning (RL) has shown strong promise for LLM-based machine translation . however, translation-oriented RL remains challenged by high-variance policy gradients induced by Monte Carlo baselines and large trajectory space that favors global exploration over fine-grained local optimization.
Approach:	They propose a two-stage RL framework that uses post-editing as an auxiliary task to stabilize training and guide overall optimization.
Outcome:	The proposed framework supports global exploration and fine-grained optimization while supporting global exploration.

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Yucheng Zhou, Lingran Song, Jianbing Shen

SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation (2023.acl-long)

Challenge:	Existing Medical Large Vision-Language Models (Med-LVLMs) lack visual localization in medical images, which is crucial for abnormality detection and interpretation.
Approach:	They propose a medical abnormalities unveiling method based on a Medical Abnormalities Unveiler dataset and propose 'abnormal-aware instruction tuning' and 'abbnormal-Aware Reward' method generates diagnoses based upon identified abnormal areas in medical images.
Outcome:	The proposed method outperforms existing medical large vision-language models in identifying and understanding medical abnormalities and improves generalization capability.

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Junkai Zhou, Liang Pang, Huawei Shen, Xueqi Cheng

Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction (2022.findings-emnlp)

Challenge:	Existing work on large-scale corpora-based language models is limited and hard to generalize to all types of pre-trained language models.
Approach:	They propose a two-stage SimOAP strategy that over-samples and post-evaluates large-scale responses from existing models and selects a good response based on multiple evaluation metrics.
Outcome:	The proposed strategy outperforms baseline and automatic evaluation strategies in both automatic and human evaluations.

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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

Syntactic Text Processing

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.

ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model (2025.emnlp-main)

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Zhongyi Zhou, Yichen Zhu, Minjie Zhu, Junjie Wen, Ning Liu, Zhiyuan Xu, Weibin Meng, Yaxin Peng, Chaomin Shen, Feifei Feng, Yi Xu

A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences (2025.findings-acl)

Challenge:	Recent advances in vision-language-action models prioritize robotic action mastery . however, models trained on visual-text pairs struggle to interpret multimodal data .
Approach:	They propose a framework that integrates multimodal data after initial control mastery and a Mixture-of-Experts architecture to minimize task interference.
Outcome:	The proposed framework surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks and achieves six times higher performance on visual question-answering datasets.

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Jiaxin Shen, Jinan Xu, Huiqi Hu, Luyi Lin, Guoyang Ma, Fei Zheng, Fandong Meng, Jie Zhou, Wenjuan Han

GiFT: Gibbs Fine-Tuning for Code Generation (2025.acl-long)

Challenge:	a recent study focuses on generating impartial and interpretable judicial judgments based on established criminal fact.
Approach:	They propose a law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience that enables public scrutiny and preventing bias.
Outcome:	The proposed schema enables public scrutiny and prevents bias in the "Intelligent Court" it employs a suite of legal analysis tools to address the challenge task.

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Haochen Li, Wanjin Feng, Xin Zhou, Zhiqi Shen

Programming Languages in NLP

Challenge:	Training Large Language Models (LLMs) with synthetic data is a prevalent practice in code generation.
Approach:	They propose a method to fine-tune large language models with code drawn from a conditional distribution, conditioned on a specific seed description.
Outcome:	The proposed method improves performance on four datasets and shows that it can be used to fine-tune LLMs with code derived from the marginal distribution.

Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever (2024.findings-acl)

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Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Yibin Lei, Tianyi Zhou, Michael Blumenstein, Daxin Jiang

New Compendium of a Myriad of Plants: A New Dataset Describing Ancient Chinese Plants (2026.findings-acl)

Challenge:	Large-scale retrieval is indispensable in information-seeking tasks such as open-domain question answering and knowledgegrounded dialogue.
Approach:	They propose to use a large language model (LLM) to augment a query with its potential answers by prompting LLMs with a composition of the query and the query’s in-domain candidates.
Outcome:	The proposed method breaks brute-force combinations of retrievers with LLMs and lifts the performance of zero-shot retrieval to be very competitive on benchmark datasets.

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Xiaobin Shen, Zhongqing Wang, Shichen Li, Chu-Ren Huang, Guodong Zhou

GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level (D19-1)

Challenge:	Existing approaches to digitize ancient Chinese texts and extract information from them are shallow and inconsistent with modern realities.
Approach:	They propose to expand ancient Chinese datasets using large language model . they focus on Great Compendium of Myriad Flowers, an ancient plants dataset .
Outcome:	The proposed model can extract plant-related information from classical Chinese poetry and prose.

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Zixian Huang, Yulin Shen, Xiao Li, Yu’ang Wei, Gong Cheng, Lin Zhou, Xinyu Dai, Yuzhong Qu

Question Answering

Challenge:	SQA is an emerging application of NLP in the medical, geography, and legal domains.
Approach:	They propose a dataset of 1,981 scenarios and 4,110 multiple-choice questions in geography domain at high school level.
Outcome:	The proposed dataset consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level.

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

Event Causality Identification via Derivative Prompt Joint Learning (2022.coling-1)

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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Shirong Shen, Heng Zhou, Tongtong Wu, Guilin Qi

Low-Resource NLP

Challenge:	Existing methods for event causality identification lack annotated data, and they lack the ability to identify explicit and implicit causality.
Approach:	They propose a derivative prompt joint learning model which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem.
Outcome:	The proposed model can identify explicit and implicit causality on two benchmark datasets and it has great advantages over previous methods.

Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)

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Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

Recovering Gold from Black Sand: Multilingual Dense Passage Retrieval with Hard and False Negative Samples (2022.emnlp-main)

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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Tianhao Shen, Mingtong Liu, Ming Zhou, Deyi Xiong

Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction (2021.findings-emnlp)

Challenge:	Existing approaches to recover and utilize negative samples have not been effective on all languages.
Approach:	They propose a multilingual dense passage retrieval framework to recover and utilize negative samples.
Outcome:	The proposed framework outperforms strong sparse, dense and hybrid baselines on a high-quality dataset covering eleven typologically diverse languages.

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Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Yi Chang

Sentiment Analysis

Challenge:	Aspect-level sentiment classification (ALSC) is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect.
Approach:	They propose a span-based anti-bias aspect representation learning framework that eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects’ prior sentiment.
Outcome:	The proposed framework achieves state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction.

Noise Robust Named Entity Understanding for Voice Assistants (2021.naacl-industry)

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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

Robustness in NLP

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 .

Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering over Knowledge Graph (2021.naacl-main)

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Yucheng Zhou, Xiubo Geng, Tao Shen, Wenqiang Zhang, Daxin Jiang

Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search (2024.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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Haochen Li, Xin Zhou, Zhiqi Shen

Mixture of Attention Heads: Selecting Attention Heads Per Token (2022.emnlp-main)

Challenge:	Experimental results show that ReCo significantly boosts retrieval accuracy across sparse, zero-shot dense and fine-tuned dense search settings.
Approach:	They propose a generation-augmented retrieval framework that additionally Rewrites the Code (ReCo) within the codebase for style normalization.
Outcome:	The proposed method significantly boosts retrieval accuracy across sparse, zero-shot dense, and fine-tuned dense retrieval settings in diverse search scenarios.

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Xiaofeng Zhang, Yikang Shen, Zeyu Huang, Jie Zhou, Wenge Rong, Zhang Xiong

Responsible & Trustworthy NLP

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.

Towards Robust Ranker for Text Retrieval (2023.findings-acl)

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Yucheng Zhou, Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Guodong Long, Binxing Jiao, Daxin Jiang

QA‐LIGN: Aligning LLMs through Constitutionally Decomposed QA (2025.findings-emnlp)

Challenge:	Existing methods for text retrieval are based on a 'retrieval & rerank' pipeline, which uses a fast retriever to fetch a set of top document candidates, while a robust ranker is based upon a weak negative mining during contrastive learning.
Approach:	They propose a multi-adversarial training strategy that leverages multiple retrievers as generators to challenge a ranker.
Outcome:	The proposed model outperforms the existing de facto ranker training paradigms on the passage retrieval benchmarks using BM25-reranking, full-ranking and retriever distillation.

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Jacob Dineen, Aswin Rrv, Qin Liu, Zhikun Xu, Xiao Ye, Ming Shen, Zhaonan Li, Shijie Lu, Chitta Baral, Muhao Chen, Ben Zhou

Explainability & Interpretability in NLP

Challenge:	QA-LIGN decomposes monolithic rewards into interpretable principle-specific evaluations . scalar rewards obscure which objectives drive the training signal .
Approach:	a new method decomposes monolithic rewards into interpretable principle-specific evaluations . QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate .
Outcome:	QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . the results outperform DPO and GRPO with state-of-the-art reward models given equivalent training .

Plot Retrieval as an Assessment of Abstract Semantic Association (2024.acl-srw)

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Shicheng Xu, Liang Pang, Jiangnan Li, Mo Yu, Fandong Meng, Huawei Shen, Xueqi Cheng, Jie Zhou

DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs (2025.findings-emnlp)

Challenge:	Existing information retrieval datasets cannot capture abstract semantic associations well.
Approach:	They propose a task that retrieves relevant plots from the book for a query using a labeled dataset.
Outcome:	The proposed task can be used to evaluate the performance of IR models on the novel task Plot Retrieval.

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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%)

Improving Contextual Query Rewrite for Conversational AI Agents through User-preference Feedback Learning (2023.emnlp-industry)

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Zhongkai Sun, Yingxue Zhou, Jie Hao, Xing Fan, Yanbin Lu, Chengyuan Ma, Wei Shen, Chenlei Guo

Multimodal Large Language Models for Multi-Subject In-Context Image Generation (2026.acl-long)

Challenge:	Contextual query rewriting (CQR) is a crucial component in Conversational AI agents, leveraging contextual information from previous user-agent conversations to improve comprehension of current user intent.
Approach:	They propose a framework to enhance the CQR model's capability in generating user preference-aligned rewrites.
Outcome:	The proposed framework improves the CQR model's ability to generate user preference-aligned rewrites.

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Yucheng Zhou, Dubing Chen, Huan Zheng, Jianbing Shen

On the Strength of Sequence Labeling and Generative Models for Aspect Sentiment Triplet Extraction (2023.findings-acl)

Challenge:	Recent advances in text-to-image (T2I) generation have enabled visually coherent image synthesis from descriptions, but generating images containing multiple given subjects remains challenging.
Approach:	They propose a model that enables automatic and scalable data generation without manual annotations to overcome the data scarcity.
Outcome:	The proposed model overcomes the data scarcity and lacks manual annotations.

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Shen Zhou, Tieyun Qian

Sentiment Analysis

Challenge:	Existing generative models ignore the mutual informative clues between aspect and opinion terms and may generate false paired triplets.
Approach:	They propose a generative model that encodes aspect and opinion into two bidirectional templates and introduces a marker-oriented sequence labeling module to improve models’ ability of tackling complex structures.
Outcome:	The proposed model captures the boundary information of aspect/opinion spans and provides hints to decode multiple triplets with the shared marker.

CondenseLM: LLMs-driven Text Dataset Condensation via Reward Matching (2025.emnlp-main)

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Cheng Shen, Yew-Soon Ong, Joey Tianyi Zhou

Green & Sustainable NLP

Challenge:	Existing methods for dataset condensation struggle to compress more information into samples . current methods struggle to extract enough training data for effective learning .
Approach:	They propose a paradigm for dataset condensation that uses an LLMs-driven approach to generate more informative and less biased samples.
Outcome:	The proposed method outperforms coreset selection and existing condensation methods by large margins while significantly reducing the computational cost.

MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration (2025.findings-acl)

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Yucheng Zhou, Lingran Song, Jianbing Shen

Wav2SQL: Direct Generalizable Speech-To-SQL Parsing (2024.findings-acl)

Challenge:	Recent advances in medical Large Language Models have demonstrated powerful reasoning and diagnostic capabilities.
Approach:	They propose a modular multi-agent framework for multi-modal medical diagnosis . they decompose the medical diagnostic process into specialized roles .
Outcome:	The framework decomposes the medical diagnostic process into specialized roles . it achieves significant performance improvements ranging from 18% to 365% compared to baseline models.

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Huadai Liu, Rongjie Huang, Jinzheng He, Gang Sun, Ran Shen, Xize Cheng, Zhou Zhao