Papers by Zheng Shen

CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction (2025.acl-long)

Challenge:	Large language models (LLMs) demonstrate remarkable performance across diverse tasks, yet their effectiveness often depends on costly commercial APIs or cloud services.
Approach:	They propose a dual-mode compatible approach that fine-tunes models through shortest-response preference optimization and a confidence-aware rejection mechanism.
Outcome:	The proposed approach reduces redundant outputs and response times while reducing computational costs by over 50% and cascade latency by over 80%.

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Jingheng Ye, Zishan Xu, Yinghui Li, Linlin Song, Qingyu Zhou, Hai-Tao Zheng, Ying Shen, Wenhao Jiang, Hong-Gee Kim, Ruitong Liu, Xin Su, Zifei Shan

Syntactic Text Processing

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

Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization (2026.acl-long)

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Tian Xueyun, MingHua Ma, Bingbing Xu, Nuoyan Lyu, Wei Li, Heng Dong, Zheng Chu, Yuanzhuo Wang, Huawei Shen

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

Challenge:	Recent studies show that supervised fine-tuning (SFT) is a common approach for reasoning in large language models.
Approach:	They propose to use supervised fine-tuning (SFT) on chain-of-thought trajectories demonstrations . they find that incorporating negative traxories yields substantial OOD generalization gains .
Outcome:	The proposed scheme yields 5.51% OOD gain over positive-only training.

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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.

TripTailor: A Real-World Benchmark for Personalized Travel Planning (2025.findings-acl)

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Kaimin Wang, Yuanzhe Shen, Changze Lv, Xiaoqing Zheng, Xuanjing Huang

Challenge:	Existing evaluation metrics for travel planning rely on unrealistic simulated data . fewer than 10% of the itineraries generated by the latest state-of-the-art LLMs achieve human-level performance.
Approach:	They propose a benchmark for personalized travel planning in real-world scenarios . they identify several critical challenges in travel planning including feasibility and rationality .
Outcome:	The proposed benchmarks show that fewer than 10% of the itineraries generated by the latest state-of-the-art LLMs achieve human-level performance.

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

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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

Experience-driven Multi-turn Reinforcement Learning for GUI Agents (2026.acl-long)

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

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Zhengxi Lu, Jiabo Ye, Fei Tang, Yongliang Shen, Haiyang Xu, Ziwei Zheng, Weiming Lu, Ming Yan, Fei Huang, Jun Xiao, Yueting Zhuang

Challenge:	GUI agents have demonstrated remarkable progress in automating complex user interface interactions . training such agents for long-horizon tasks remains challenging due to limited rewards and prohibitive costs.
Approach:	They propose a method that leverages expert trajectories as environment experiences for on-policy multi-turn training.
Outcome:	The proposed method achieves significant gains over the base model with 1K public trajectories as RL experiences . it achieves competitive performance against strong baselines such as UI-TARS-7B and GPT-4o .

A Prism Module for Semantic Disentanglement in Name Entity Recognition (P19-1)

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Kun Liu, Shen Li, Daqi Zheng, Zhengdong Lu, Sheng Gao, Si Li

Information Extraction & Text Mining

Challenge:	Xu et al., 2015) proposed a noise reduction mechanism to disentangle semantics of words . hard and soft attention mechanisms are used to reduce noise in NLP tasks .
Approach:	They propose a prism module to disentangle semantic aspects of words and reduce noise . they propose combining prism modules with downstream models to improve model performance .
Outcome:	The proposed method significantly improves the performance of baselines on named entity recognition (NER) tasks.

Governance in Motion: Co-evolution of Constitutions and AI models for Scalable Safety (2025.emnlp-main)

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Chenhao Huang, Ziyu Shen, Yicong Ren, Huiyuan Zheng, Jiazheng Zhang, Mingxu Chai, Ming Zhang, Shihan Dou, Fan Mo, Jie Shi, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Existing approaches to align large language models with human preferences lack flexibility . static alignment preferences lack the ability to correct misaligned behaviors as they emerge .
Approach:	They propose a framework that enables dynamic and continuous alignment of large language models with human preferences.
Outcome:	The proposed framework improves safety and accuracy of a 7B model with human annotations.

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

Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning (2026.acl-long)

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.

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Naixin Zhai, Pengyang Shao, Binbin Zheng, Yonghui Yang, Fei Shen, Long Bai, Xun Yang

Challenge:	Existing approaches to machine unlearning treat all tokens indiscriminately and enforce uncertainty over the entire vocabulary.
Approach:	They propose a framework that targets the prefix in a response and minimizes uncertainty in the critical subspace.
Outcome:	The proposed framework achieves superior forgetting efficacy and utility preservation compared to baselines.

Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning (2024.findings-acl)

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Naibin Gu, Peng Fu, Xiyu Liu, Bowen Shen, Zheng Lin, Weiping Wang

TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities (2024.emnlp-main)

Challenge:	Existing methods for fine-tuning large language models are inefficient and redundant . a light-PEFT framework can be used to prune redundant parameters during training .
Approach:	They propose a parameter-efficient fine-tuning framework that freezes most parameters of the foundation model and finetuns only a small number of parameters.
Outcome:	The proposed framework achieves training and inference speedup, reduces memory usage, and maintains comparable performance and plug-and-play feature of PEFT.

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Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang

Dialogue Systems & Conversational Agents

Challenge:	Current datasets cater to user-led systems and are limited to predefined specific scenarios and slots.
Approach:	They propose to use a Chinese dialogue dataset to train a model that authentically simulates human-computer dialogues in 30 popular life service scenarios.
Outcome:	The proposed model achieves a joint accuracy of 75.09% in out-of-domain evaluations . it also achieves notable abilities in slot filling and questioning .

Automated Fine-Grained Mixture-of-Experts Quantization (2025.findings-acl)

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Zhanhao Xie, Yuexiao Ma, Xiawu Zheng, Fei Chao, Wanchen Sui, Yong Li, Shen Li, Rongrong Ji

Green & Sustainable NLP

Challenge:	specialized quantization framework for Mixture of Experts architectures is inadequate for model compression.
Approach:	They propose a specialized quantization framework for Mixture of Experts architectures . they find that expert networks exhibit distinctive channel-wise outlier distributions ."
Outcome:	The proposed framework improves on the Mixtral-8x7b-v0.1 architecture while maintaining minimal computational overhead.

Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge (2023.findings-acl)

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Hua Cai, Xuli Shen, Qing Xu, Weilin Shen, Xiaomei Wang, Weifeng Ge, Xiaoqing Zheng, Xiangyang Xue

Dialogue Systems & Conversational Agents

Challenge:	Existing work on generating empathetic responses by utilizing the speaker's emotion has not been successful.
Approach:	They propose an approach which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker’s situation.
Outcome:	The proposed approach outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses.

Wasserstein Selective Transfer Learning for Cross-domain Text Mining (2021.emnlp-main)

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Lingyun Feng, Minghui Qiu, Yaliang Li, Haitao Zheng, Ying Shen

Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations (2024.findings-acl)

Challenge:	Existing methods to improve the learning of data-scarce target domains have negative transfer due to the data distributions between source and target domain.
Approach:	They propose a method that uses a reinforced selector to select helpful data for transfer learning and a Wasserstein-based discriminator to maximize the distance between the selected data and target data.
Outcome:	The proposed method performs better on three real-world text mining tasks.

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Bowen Shen, Zheng Lin, Daren Zha, Wei Liu, Jian Luan, Bin Wang, Weiping Wang

StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling (2021.acl-long)

Challenge:	Structured pruning is a feasible solution for end-side LLM deployment . however, achieving a high compression ratio for scaled-up LLMs remains a challenge .
Approach:	They propose a task-agnostic structured pruning approach coupled with a compact Transformer architecture to prune LLMs into an intra-module low-rank architecture.
Outcome:	The proposed approach reduces transitional activations inside multi-head attention (MHA) and multi-layer perceptron (MLP) modules while preserving inter-module activations sensitive to perturbations.

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Yikang Shen, Yi Tay, Che Zheng, Dara Bahri, Donald Metzler, Aaron Courville

Logic: Long-form Outline Generation via Imitative and Critical Self-refinement (2025.findings-emnlp)

Challenge:	Existing models that induce grammar structures from data focus on constituency or dependency structures alone.
Approach:	They propose a model that can induce dependency and constituency structure at the same time.
Outcome:	The proposed model can induce both constituency and dependency structures at the same time.

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Hengwei Liu, Yongliang Shen, Zhe Zheng, Haoyuan Ma, Xingyu Wu, Yin Zhang, Weiming Lu

Evaluating Memory Capability in Continuous Lifelog Scenario (2026.findings-acl)

Challenge:	Existing methods for long-form outline generation have low knowledge density and lack detail . retrieval-augmented approaches struggle to maintain logical coherence across retrieved information .
Approach:	They propose a system that mimics human writers' refinement process by mimicking outlines through imitation and critical self-refinement.
Outcome:	The proposed system improves on the FreshWiki and WikiOutline datasets and establishes a coherent planning framework and structured knowledge base.

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Jianjie Zheng, Zhichen Liu, Zhanyu Shen, Jingxiang Qu, Guanhua Chen, Yile Wang, Yang Xu, Yang Liu, Sijie Cheng

Challenge:	Existing benchmarks focus on online one-on-one chatting or human-AI interactions, neglecting real-world scenarios.
Approach:	They propose a framework to curate a lifelog benchmark that combines two subsets of audio data to address temporal leakage in offline settings.
Outcome:	The proposed framework outperforms existing benchmarks on live chats and AI interactions.

AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification (2025.emnlp-main)

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Xuan Zhang, Yongliang Shen, Zhe Zheng, Linjuan Wu, Wenqi Zhang, Yuchen Yan, Qiuying Peng, Jun Wang, Weiming Lu

DP3: Differentially Private Prompt Perturbation for Multi-turn LLM Inference (2026.findings-acl)

Challenge:	Existing tools for ambiguous and incomplete queries are limited by manual construction and lack of error correction mechanisms during multi-turn clarification.
Approach:	They propose a framework that exploits the mapping between queries and their tool invocation solutions by removing key parameters from queries while retaining them as ground truth.
Outcome:	The proposed framework outperforms existing methods while maintaining high accuracy in tool invocation.

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Lele Zheng, Chao Zhang, Feiyang Yuan, Ke Cheng, Tao Zhang, Anxiao Song, Yulong Shen

MKT: A Multi-Stage Knowledge Transfer Framework to Mitigate Catastrophic Forgetting in Multi-Domain Chinese Spelling Correction (2025.emnlp-industry)

Challenge:	Large language models (LLMs) are widely used for text understanding and generation . existing methods that assume single-turn interactions break down in multi-turn settings .
Approach:	They propose a differentially private prompt perturbation framework for multi-turn LLM inference . DP3 constructs a perturbation mapping table to reuse perturbations for recurring tokens .
Outcome:	The proposed framework reduces privacy costs and degrades cross-turn semantic coherence . it also provides a context-aware utility function to maintain semantic consistency across turns .

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Peng Xing, Yinghui Li, Shirong Ma, Xinnian Liang, Haojing Huang, Yangning Li, Shu-Yu Guo, Hai-Tao Zheng, Wenhao Jiang, Ying Shen

Green & Sustainable NLP

Challenge:	Chinese Spelling Correction (CSC) is a model that detects and corrects spelling errors in given sentences.
Approach:	They propose a model-agnostic model with an evolving teacher model and dynamic distillation weights for knowledge transfer in each domain rather than focusing solely on new domain knowledge.
Outcome:	The proposed model-agnostic framework is based on an evolving teacher model and dynamic distillation weights for knowledge transfer in each domain, rather than focusing solely on new domain knowledge.

TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models (2023.emnlp-main)

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Jing Xiong, Jianhao Shen, Ye Yuan, Haiming Wang, Yichun Yin, Zhengying Liu, Lin Li, Zhijiang Guo, Qingxing Cao, Yinya Huang, Chuanyang Zheng, Xiaodan Liang, Ming Zhang, Qun Liu

UNComp: Can Matrix Entropy Uncover Sparsity? — A Compressor Design from an Uncertainty-Aware Perspective (2025.emnlp-main)

Challenge:	Automated theorem proving (ATP) benchmarks focus on symbolic inference but rarely involve understanding complex number combination reasoning.
Approach:	They propose a benchmark that requires a model to reduce a trigonometric expression with step-by-step proof and evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms.
Outcome:	The proposed benchmark evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms.

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Jing Xiong, Jianghan Shen, Fanghua Ye, Chaofan Tao, Zhongwei Wan, Jianqiao Lu, Xun Wu, Chuanyang Zheng, Zhijiang Guo, Min Yang, Lingpeng Kong, Ngai Wong

TailorKV: A Hybrid Framework for Long-Context Inference via Tailored KV Cache Optimization (2025.findings-acl)

Challenge:	Deploying large language models (LLMs) for long-context inference remains challenging due to their substantial memory and computational demands.
Approach:	They propose an uncertainty-aware framework that leverages truncated matrix entropy to identify areas of low information content.
Outcome:	The proposed framework reduces the KV cache size to 4.74% of the original and achieves a 6% speedup.

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Dingyu Yao, Bowen Shen, Zheng Lin, Wei Liu, Jian Luan, Bin Wang, Weiping Wang

Challenge:	Existing work mitigates memory overhead by offloading or compressing the Key-Value cache.
Approach:	They propose a method that integrates quantization and offloading into a generative large language model by using a hybrid compression method.
Outcome:	The proposed method outperforms the state-of-the-art in long-context evaluations.

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

Answer-driven Deep Question Generation based on Reinforcement Learning (2020.coling-main)

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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Liuyin Wang, Zihan Xu, Zibo Lin, Haitao Zheng, Ying Shen

Rethinking Token Reduction for State Space Models (2024.emnlp-main)

Challenge:	Existing methods for deep question generation focus on enhancing document representations, but little attention is paid to the answer information.
Approach:	They propose a deep question generation model that makes better use of the target answer as a guidance to facilitate question generation.
Outcome:	The proposed model outperforms state-of-the-art models in automatic and human evaluations on the hotpotQA dataset.

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Zheng Zhan, Yushu Wu, Zhenglun Kong, Changdi Yang, Yifan Gong, Xuan Shen, Xue Lin, Pu Zhao, Yanzhi Wang

Challenge:	Existing methods for token reduction for SSMs lead to performance drops . a recent study shows that Mamba-2 improves the accuracy of the model by 5.7% to 13.1% .
Approach:	They propose a token reduction method that integrates token importance and similarity into SSMs and takes advantage of pruning and merging.
Outcome:	The proposed method improves accuracy by 5.7% to 13.1% on six benchmarks with Mamba-2 compared to existing methods while reducing computational demands and memory requirements.

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

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

Natural Language Interfaces

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.

Quantifying Association Capabilities of Large Language Models and Its Implications on Privacy Leakage (2024.findings-eacl)

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Hanyin Shao, Jie Huang, Shen Zheng, Kevin Chang

Is the Attention Matrix Really the Key to Self-Attention in Multivariate Long-Term Time Series Forecasting? (2026.acl-long)

Challenge:	a new study examines the association capabilities of large language models . as models scale up, their ability to associate entities/information intensifies . however, there is a performance gap when associating commonsense knowledge versus PII, with the latter showing lower accuracy.
Approach:	They examine the association capabilities of large language models and identify factors that influence their proficiency in associating information.
Outcome:	The proposed models show a performance gap when associating commonsense knowledge versus PII, with the latter showing lower accuracy.

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Xinyu Li, Kexi Chen, Jiajie Shen, Ying Zheng, Hong Lu, Jin Zhao, Xin Wang

Scaling LLMs’ Social Reasoning: Sprinkle Cognitive “Aha Moment” into Fundamental Long-thought Logical Capabilities (2025.findings-acl)

Challenge:	In multivariate long-term time series forecasting, it is widely believed that the effectiveness of self-attention arises from its attention matrix.
Approach:	They propose a multi-branch MLP that isolates the ‘multi-brain mapping with element-wise operation’ structure from the Transformer and shows that it achieves competitive performance.
Outcome:	The proposed model outperforms three classic and three latest Transformer models and shows that it achieves competitive performance.

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Guiyang Hou, Wenqi Zhang, Zhe Zheng, Yongliang Shen, Weiming Lu

STARD: A Chinese Statute Retrieval Dataset Derived from Real-life Queries by Non-professionals (2024.findings-emnlp)

Challenge:	Existing studies have examined how large language models’ social reasoning capabilities evolve during model size scaling or reasoning tokens scaling.
Approach:	They propose to optimize evaluation of Large Language Models from both data and model perspectives and to analyze their reasoning trajectories to identify notable cognitive "Aha Moments"
Outcome:	The proposed model outperforms the o1-preview model by 19.0 points in the evaluation of large language models.

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Weihang Su, Yiran Hu, Anzhe Xie, Qingyao Ai, Quezi Bing, Ning Zheng, Yun Liu, Weixing Shen, Yiqun Liu

DGLF: A Dual Graph-based Learning Framework for Multi-modal Sarcasm Detection (2024.emnlp-main)

Challenge:	Existing statute retrieval benchmarks emphasize formal and professional queries from sources like bar exams and legal case documents . existing retrieval approaches that lack domain-specific knowledge may struggle to capture the meanings of specialized terms accurately.
Approach:	They propose a dataset that captures the complexity and diversity of real queries from the general public.
Outcome:	The proposed dataset captures the complexity and diversity of real queries from the general public.

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Zhihong Zhu, Kefan Shen, Zhaorun Chen, Yunyan Zhang, Yuyan Chen, Xiaoqi Jiao, Zhongwei Wan, Shaorong Xie, Wei Liu, Xian Wu, Yefeng Zheng

Maximum Entropy Loss, the Silver Bullet Targeting Backdoor Attacks in Pre-trained Language Models (2023.findings-acl)

Challenge:	Existing methods for multimodal sarcasm detection neglect high-order relationships and underestimate high-frequency messages.
Approach:	They propose a Dual Graph-based Learning Framework to capture inter-modal inconsistencies . they propose combining a hypergraph and a vanilla graph to achieve enhanced propagation .
Outcome:	The proposed model outperforms existing state-of-the-art methods on two benchmark datasets.

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Zhengxiao Liu, Bowen Shen, Zheng Lin, Fali Wang, Weiping Wang

DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)

Challenge:	Existing backdoor defense paradigms focus on detecting and removing poisoned samples at pre-training or inference time.
Approach:	They propose a new approach where the backdoor attack is directly reversed by incorporating maximum entropy loss into training to neutralize the minimal cross-entropiness loss fine-tuning on poisoned data.
Outcome:	The proposed model significantly lowers the attack success rate on classification tasks and reduces the risk of backdoor attacks on clean data.

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Guanqun Bi, Lei Shen, Yanan Cao, Meng Chen, Yuqiang Xie, Zheng Lin, Xiaodong He

Text Generation

Challenge:	Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context.
Approach:	They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals.
Outcome:	The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness.

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

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

Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition (2020.coling-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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Dongming Sheng, Dong Wang, Ying Shen, Haitao Zheng, Haozhuang Liu

Structured Data in NLP

Challenge:	Existing studies focus on modeling emotion influences with utterance-level features, with little attention paid on phrase-level semantic connection between utterrances.
Approach:	They propose a two-stage Summarization and Aggregation Graph Inference Network which integrates inference for topic-related emotional phrases and local dependency reasoning over neighbouring utterances in a global-to-local fashion.
Outcome:	The proposed model outperforms the state-of-the-art models on three CER benchmark datasets.

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

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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo

Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions (2025.emnlp-main)

Challenge:	Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task.
Approach:	They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5.
Outcome:	The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers.

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Yijun Shen, Delong Chen, Fan Liu, Xingyu Wang, Chuanyi Zhang, Liang Yao, Yuhui Zheng

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

Challenge:	Existing approaches for optimizing human annotation efforts are limited . et al., 2015) suggest that densely annotated image captions improve vision-language alignment .
Approach:	They propose an AI-in-the-loop methodology to maximize the number of annotated samples and improve their comprehensiveness under fixed budget constraints.
Outcome:	The proposed method improves annotation speed and retrieval performance over the parallel method.

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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.

ModSCAN: Measuring Stereotypical Bias in Large Vision-Language Models from Vision and Language Modalities (2024.emnlp-main)

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Yukun Jiang, Zheng Li, Xinyue Shen, Yugeng Liu, Michael Backes, Yang Zhang

Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation (2023.findings-emnlp)

Challenge:	Large vision-language models have been widely used but stereotypical biases are unexplored.
Approach:	They propose a framework to SCAN stereotypical bias within large vision-language models . they examine stereotype biases with respect to gender and race in three scenarios .
Outcome:	The proposed framework can reduce stereotypical biases in large vision-language models . the currently popular models show significant stereotype biase .

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Lei Gao, Xinnan Zhang, Xian Wu, Shen Ge, Yefeng Zheng

Structured Data in NLP

Challenge:	Existing approaches ignore relationships between medical items and statuses in the multi-turn doctor-patient dialogue.
Approach:	They propose a task to extract structured medical information from free text dialogues . they propose 'Dialogue Medical Information Extraction' to model relationships between items .
Outcome:	The proposed model outperforms previous models and achieves state-of-the-art performance on the public benchmark data set.

Grammar-based Decoding for Improved Compositional Generalization in Semantic Parsing (2023.findings-acl)

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Jing Zheng, Jyh-Herng Chow, Zhongnan Shen, Peng Xu

Learning from the Irrecoverable: Error-Localized Policy Optimization for Tool-Integrated LLM Reasoning (2026.acl-long)

Challenge:	Sequence-to-sequence (seq2sequ) models have been successful in semantic parsing tasks but struggle on out-of-distribution data.
Approach:	They propose to use a large-scale dialogue dataset to evaluate compositional generalization of semantic parsing.
Outcome:	The proposed model outperforms BART- and T5-based models on the SMCalflow-CS dataset on the zero-shot learning task.

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Qiao Liang, Yuke Zhu, Chao Ge, Lei Yang, Ying Shen, Bo Zheng, Sheng Guo

Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge (P19-1)

Challenge:	Tool-integrated reasoning (TIR) enables LLM agents to solve tasks through planning, tool use, and iterative revision, but outcome-only reinforcement learning suffers from sparse, delayed rewards and weak step-level credit assignment.
Approach:	They propose a tool-integrated reasoning approach that localizes the first irrecoverable step and leverages it for fine-grained credit assignment.
Outcome:	The proposed algorithm outperforms strong Agentic RL benchmarks in math, science QA, and code execution with additional gains in Pass@K and Major@K scaling, rollout ranking quality, and tool-call efficiency.

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Ziran Li, Ning Ding, Zhiyuan Liu, Haitao Zheng, Ying Shen

Knowledge Representation

Challenge:	Existing methods for Chinese relation extraction suffer from segmentation errors and ambiguity of polysemy.
Approach:	They propose a multi-grained lattice framework for Chinese relation extraction . they incorporate word-level information into character sequence inputs to avoid segmentation errors .
Outcome:	The proposed model outperforms existing models on three real-world datasets in distinct domains.

Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation (2025.findings-acl)

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Che Liu, Zhongwei Wan, Yuqi Wang, Hui Shen, Haozhe Wang, Kangyu Zheng, Mi Zhang, Rossella Arcucci

STAIR: Learning Sparse Text and Image Representation in Grounded Tokens (2023.emnlp-main)

Challenge:	Existing work on 3D radiograph report generation focuses on 2D images, but 3D medical images provide more comprehensive diagnostic information.
Approach:	They propose a comprehensive training recipe for building high-performing VLMs for 3DRRG using a publicly available 3D CT-report dataset.
Outcome:	The proposed model achieves superior performance across different model sizes and input 3D medical image resolutions.

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Chen Chen, Bowen Zhang, Liangliang Cao, Jiguang Shen, Tom Gunter, Albin Jose, Alexander Toshev, Yantao Zheng, Jonathon Shlens, Ruoming Pang, Yinfei Yang

Representation Learning

Challenge:	State-of-the-art contrastive learning models like CLIP and ALIGN are less interpretable and suffer from inferior accuracy than dense representations.
Approach:	They extend CLIP and ALIGN models to build a sparse semantic representation that is interpretable and easy to integrate with existing retrieval systems.
Outcome:	The proposed model outperforms CLIP and ALIGN models on image and text retrieval tasks with a 4.9% and +4.3% improvement on COCO-5k textimage and imagetext retrieval respectively.

BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving (2025.acl-long)

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Ran Xin, Chenguang Xi, Jie Yang, Feng Chen, Hang Wu, Xia Xiao, Yifan Sun, Shen Zheng, Ming Ding

How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities (2026.acl-long)

Challenge:	Existing approaches to theorem proving in large language models rely on value functions and/or Monte Carlo Tree Search (MCTS), but the potential of simpler methods like Best-First Tree Search remains underexplored.
Approach:	They propose a scalable expert iteration framework that implements strategic data filtering at each expert iteration round, excluding problems solvable via beam search node expansion to focus on harder cases.
Outcome:	The proposed framework achieves a state-of-the-art score of 72.95 on the MiniF2F test set and challenges the perceived necessity of complex tree search methods.

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Ziwen Xu, Kewei Xu, Haoming Xu, Haiwen Hong, Longtao Huang, Hui Xue, Ningyu Zhang, Yongliang Shen, Guozhou Zheng, Huajun Chen, Shumin Deng

Traces in the Brain: Neural Evidence for Syntactic Movement in English and Chinese (2026.findings-acl)

Challenge:	Large language models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors pose significant risks.
Approach:	They propose a hierarchical benchmark for evaluating LLM controllability across three domains: language features, sentiment, and personality.
Outcome:	The proposed framework offers a principled and interpretable framework for safe and controllable LLM behavior serving as a foundation for future research.

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Yuhan Huang, Zhengwu Ma, Yuqi Jin, Beth Chan, Zheng Shen, Jackie Yan-Ki Lai, John T. Hale, Jixing Li

Syntactic Text Processing

Challenge:	Syntactic movement is a core concept in generative linguistics to account for word-order variation and long-distance dependencies.
Approach:	They annotated every sentence in the audiobook The Little Prince using X-bar style tree annotations.
Outcome:	The proposed model shows that deep structure significantly predicts neural responses in English but not in Chinese.

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

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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.

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

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

DIVE into MoE: Diversity-Enhanced Reconstruction of Large Language Models from Dense into Mixture-of-Experts (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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Yuchen Feng, Bowen Shen, Naibin Gu, Jiaxuan Zhao, Peng Fu, Zheng Lin, Weiping Wang

COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models (2022.emnlp-main)

Challenge:	Existing methods for reconstruction of large language models overlook diversity among experts, leading to potential redundancy.
Approach:	They propose a pruning-based expert reconstruction method that prunes a specific LLM and retrains it on routers, experts and normalization modules.
Outcome:	The proposed method outperforms pruning and MoE reconstruction methods on Llama-style models with open-source training corpora.

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Bowen Shen, Zheng Lin, Yuanxin Liu, Zhengxiao Liu, Lei Wang, Weiping Wang

Open Schrödinger’s Closed Box: Identifying Retrieval Augmented Generation in API-Accessible Large Language Model Services (2026.acl-long)

Challenge:	Existing statically compressed pre-trained language models lack spatial and temporal efficiency due to their large size and wide width.
Approach:	They propose a spatially and temporally efficient model which retains the major capacity of PLMs.
Outcome:	The proposed model retains the major capacity of pre-trained language models at high compression and acceleration rate with 1/8 parameters and 1/19 FLOPs of BERT.

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Yukun Jiang, Xinyue Shen, Michael Backes, Zheng Li, Yang Zhang

LoReFact: Bridging the Logic Gap in Fact-Checking (2026.findings-acl)

Challenge:	Large language models (LLMs) are powerful at question-answering but prone to hallucinations due to limited domain-specific or up-to-date knowledge.
Approach:	They propose a framework for IDentifying RAG properties in LLM services that integrates LLMs with retrieval systems and adds an external retriever and knowledge database to mitigate hallucinations.
Outcome:	The proposed framework detects RAG-enhanced LLMs with 99.97% accuracy with partial or no optional knowledge and nearly 100% when the LLM and database are known.

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Qiming Xie, Wenjie Zheng, Xiangqing Shen, Rui Xia

A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges (2025.findings-acl)

Challenge:	Existing fact-checking methods focus on verification of individual facts, overlooking logical dependencies . a recent study shows that text containing logical errors may still be misjudged as factual .
Approach:	They propose a content–logic coupled factuality evaluation paradigm that conceptualizes factual dimension along two complementary dimensions: content factualism and logic factuity.
Outcome:	The proposed paradigm bridges the gap between factual verification and content factuality . it incorporates the logical dimension and a logic-aware metric to expose and penalize logical fallacies.

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Yibo Yan, Jiamin Su, Jianxiang He, Fangteng Fu, Xu Zheng, Yuanhuiyi Lyu, Kun Wang, Shen Wang, Qingsong Wen, Xuming Hu

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

Challenge:	This survey provides the first comprehensive analysis of mathematical reasoning in the era of multimodal large language models . integrating large language model with mathematical reasoning tasks is becoming significant as AI advances .
Approach:	They review over 200 studies published since 2021 and examine the state-of-the-art developments in Math-LLMs . they identify five major challenges hindering the realization of AGI in this domain .
Outcome:	The authors examine the state-of-the-art developments in Math-LLMs with a focus on multimodal settings.

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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

Position: LLMs Can be Good Tutors in English Education (2025.emnlp-main)

Challenge:	Named Entity Recognition and Entity Linking are challenging for voice assistants . utterances are relatively short, so there is not much context to help disambiguate .
Approach:	They propose a Named Entity Understanding system that combines NER and EL in a joint reranking module.
Outcome:	The proposed framework improves NER accuracy by up to 3.13% and EL accuracy by 3.6% in F1 score . it also leads to better accuracies in other natural language understanding tasks .

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Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu, Qingsong Wen

Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency (2023.findings-emnlp)

Challenge:	Recent efforts to integrate large language models into English education lack adaptability to language learning.
Approach:	They argue that large language models can be effective tutors in English education . they encourage interdisciplinary research to explore these roles, fostering innovation and risks .
Outcome:	The proposed models can play three critical roles: 1) as data enhancers, 2) as task predictors, 3) as agents, enabling personalized and inclusive education.

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Lingfeng Shen, Weiting Tan, Boyuan Zheng, Daniel Khashabi

Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models (2026.acl-long)

Challenge:	Manually "engineering" prompts for large language models can be laborious and time-intensive.
Approach:	They propose a new metric to quantify the expected utility of a language prompt.
Outcome:	The proposed metric outperforms previous prompt selection metrics with 10% increase in Pearson correlation across 6 classification benchmarks and the prompt selected by the proposed meter gains 5% higher accuracy than previous metrics.

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Shaonan Liu, Guo Yu, Xiaoling Luo, Shiyi Zheng, Jie Liu, Wenting Chen, Linlin Shen

Generative Semantic Hashing Enhanced via Boltzmann Machines (2020.acl-main)

Challenge:	Existing benchmarks fail to evaluate egocentric clinical intent understanding of medical multimodal large language models.
Approach:	They propose a benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation and diagnostic interpretation.
Outcome:	The proposed benchmark addresses challenges of visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical workflows, and implicit adherence to safety protocols.

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Lin Zheng, Qinliang Su, Dinghan Shen, Changyou Chen

Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective (2023.eacl-main)

Challenge:	Existing methods for generative semantic hashing assume a factorized posterior distribution, enforcing independence among the bits of hash codes.
Approach:	They propose to use a Boltzmann machine distribution as the variational posterior to introduce correlations among the bits of hash codes.
Outcome:	The proposed method can achieve significant performance gains by combining two hash codes.

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Zijian Zhang, Chang Shu, Ya Xiao, Yuan Shen, Di Zhu, Youxin Chen, Jing Xiao, Jey Han Lau, Qian Zhang, Zheng Lu

Visual Data in NLP

Challenge:	Recent VSE models combine simple pooling methods with hard triplet loss to improve performance.
Approach:	They propose an adaptive pooling strategy that allows the model to learn how to aggregate features through a combination of simple pooling methods.
Outcome:	The proposed strategy outperforms current state-of-the-art systems on image-to-text and text-toimage retrieval.

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

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Yucheng Zhou, Dubing Chen, Huan Zheng, Jianbing Shen

Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback (2023.findings-emnlp)

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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Wei Shen, Rui Zheng, Wenyu Zhan, Jun Zhao, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (2025.findings-emnlp)

Challenge:	Experimental results prove that language models can learn from human feedback better, irrespective of sequence length . emergence of length bias often induces the model to favor longer outputs .
Approach:	They propose to separate reward modeling from the influence of sequence length by using the Product-of-Experts technique.
Outcome:	The proposed approach shows that language models perform better regardless of sequence length . the main expert is focused on understanding human intents, while the biased expert targets the identification and capture of length bias.

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Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)

Challenge:	Multimodal large language models have demonstrated remarkable performance in visual-language tasks, but their authenticity is often compromised by object hallucinations.
Approach:	They propose a multi-frequency perturbation method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference.
Outcome:	The proposed method significantly mitigates object hallucinations across various model architectures.

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Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang

Causally Modeling the Linguistic and Social Factors that Predict Email Response (2025.naacl-long)

Challenge:	Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored.
Approach:	They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes.
Outcome:	The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks.

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Yinuo Xu, Hong Chen, Sushrita Rakshit, Aparna Ananthasubramaniam, Omkar Yadav, Mingqian Zheng, Michael Jiang, Lechen Zhang, Bowen Yi, Kenan Alkiek, Abraham Israeli, Bangzhao Shu, Hua Shen, Jiaxin Pei, Haotian Zhang, Miriam Schirmer, David Jurgens

Challenge:	a key intent behind many emails is to get a reply from the recipient.
Approach:	They propose to model the intents, expectations, and responsiveness in email exchanges by using a dataset containing 1800 emails annotated with nuanced types of intents and expectations.
Outcome:	The proposed model is based on 1800 emails annotated with nuanced types of intents and expectations . it shows that social status, argumentation, and strength of social connection influence email response rates .

Improving Adversarial Text Generation by Modeling the Distant Future (2020.acl-main)

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Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Dinghan Shen, Guoyin Wang, Zheng Wen, Lawrence Carin

The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts (2024.findings-acl)

Challenge:	Recent work has shown excellent performance on text generation tasks by combining reinforcement learning (RL) and generative models.
Approach:	They propose a model-based imitation-learning approach to improve text generation performance by focusing on a long horizon.
Outcome:	The proposed model improves on a number of text-generation tasks and provides intermediate rewards for generator optimization.

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Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, Daniel Khashabi