Papers by Shan Liu

A Dual Contrastive Learning Framework for Enhanced Multimodal Conversational Emotion Recognition (2025.coling-main)

Challenge:	Existing studies on temporal reasoning models neglect the explainable reasoning processes underlying the results.
Approach:	They propose a structure-aware generative framework that integrates Graph structures with text for Explainable TEmporal Reasoning.
Outcome:	The proposed framework achieves state-of-the-art performance while also demonstrating robust generalization capabilities.

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Yunhe Xie, Chengjie Sun, Ziyi Cao, Bingquan Liu, Zhenzhou Ji, Yuanchao Liu, Lili Shan

FAME: Towards Factual Multi-Task Model Editing (2024.emnlp-main)

Challenge:	Existing methods struggle to capture emotion shifts due to label replication and fail to preserve positive independent modality contributions during fusion.
Approach:	They propose a Dual Contrastive Learning Framework that enhances existing MERC models without additional data.
Outcome:	The proposed framework outperforms existing models on two MERC benchmark datasets and shows that it reduces label dependence and enhances emotion-sensitive independent modality features.

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Li Zeng, Yingyu Shan, Zeming Liu, Jiashu Yao, Yuhang Guo

Time-for-Accuracy: Formalizing Chain-of-Thought as an Expansion of Logical Depth (2026.findings-acl)

Challenge:	Large language models embed extensive knowledge and perform exceptionally well across tasks. outdated knowledge or factual errors within LLMs can lead to misleading or incorrect responses.
Approach:	They propose to use a dataset to enhance the practicality of model editing to correct inaccurate information within LLMs.
Outcome:	The proposed method performs excellently across tasks and scenarios, confirming its practicality.

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Yue Wang, Zhi Zhang, Wang Xi, Chengjie Sun, Lili Shan, Bingquan Liu

Challenge:	Chain-of-thought (CoT) prompting can improve multi-step reasoning, but it is unclear what kind of additional sequential computation longer traces actually enable.
Approach:	They propose a deletion-based measure of step necessity under a specified inference interface to operationalize realized depth beyond raw length.
Outcome:	The proposed method combines effective logical depth with Bennett's logical depth to show that it is more efficient than a linear model.

Task-adaptive Label Dependency Transfer for Few-shot Named Entity Recognition (2023.findings-acl)

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Shan Zhang, Bin Cao, Tianming Zhang, Yuqi Liu, Jing Fan

Re3Syn: A Dependency-Based Data Synthesis Framework for Long-Context Post-training (2025.acl-long)

Challenge:	Existing methods for Named Entity Recognition (NER) ignore label dependency, resulting in suboptimal performance.
Approach:	They propose a meta-learning method to make label dependency transferable by learning general initialization and individual parameter update rule for label dependency.
Outcome:	The proposed method improves existing methods by learning general initialization and individual parameter update rule for label dependency.

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Zhiyang Zhang, Ziqiang Liu, Huiming Wang, Renke Shan, Li Kuang, Lu Wang, De Wen Soh

Information Retrieval

Challenge:	Existing methods for constructing long-context data by concatenating short documents have overlooked a crucial characteristic of long-constituency data quality, semantic dependency.
Approach:	They propose a framework called Retrieval, Dependency Recognition, and Reorder for data synthesis which leverages semantic similarity to retrieve relevant documents and form several batches.
Outcome:	The proposed framework leverages semantic similarity to retrieve relevant documents and form several batches.

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)

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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

Multimodality

Challenge:	Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures.
Approach:	They propose a toolkit that supports pre-training models of different modalities.
Outcome:	The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks.

ThinkAnswer Loss: Balancing Semantic Similarity and Exact Matching for LLM Reasoning Enhancement (2025.findings-emnlp)

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Shan Yang, Kun Wu, Zeju Li, Linlin Zhang, Xiangyu Pei, Leike An, Yu Liu

Reasoning

Challenge:	Existing methods for knowledge distillation use Chain-of-Thought (CoT) and answer pairs, but they lack appropriate supervision signals.
Approach:	They propose a framework that decouples CoT and answer supervision . the framework applies semantic similarity constraints while maintaining strict literal matching for the answer .
Outcome:	The proposed framework decouples CoT and answer supervision while maintaining strict literal matching for the answer.

CLaSp: In-Context Layer Skip for Self-Speculative Decoding (2025.acl-long)

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Longze Chen, Renke Shan, Huiming Wang, Lu Wang, Ziqiang Liu, Run Luo, Jiawei Wang, Hamid Alinejad-Rokny, Min Yang

Challenge:	Existing methods for drafting Large Language Models require additional modules to be trained, which can be challenging to implement and ensure compatibility across various LLMs.
Approach:	They propose an in-context layer-skipping strategy for self-speculative decoding that uses a plug-and-play mechanism to skip intermediate layers of the verify model to construct a compressed draft model.
Outcome:	The proposed method achieves a speedup of 1.3 1.7 on LLaMA3 series models without altering the original distribution of the generated text.

PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes (2026.findings-acl)

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Yingyu Shan, Zeming Liu, Silin Li, Boao Qian, Jiashu Yao, Yuhang Guo, Haifeng Wang

RaaS: Reasoning-Aware Attention Sparsity for Efficient LLM Reasoning (2025.findings-acl)

Challenge:	Existing home assistants struggle to interpret elliptical commands based on ellipine expressions . current assistants overlook the progressive omission that occurs in human dialogue as context accumulates - limiting their effectiveness in real-world applications .
Approach:	They propose a simulated home dataset specifically designed for interpreting progressively elliptical commands in smart homes.
Outcome:	The proposed dataset shows that existing home assistants struggle to execute user-intended operations based solely on elliptical commands.

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Junhao Hu, Wenrui Huang, Weidong Wang, Zhenwen Li, Tiancheng Hu, Zhixia Liu, Xusheng Chen, Tao Xie, Yizhou Shan

Reasoning

Challenge:	Large Language Models (LLMs) have demonstrated strong capabilities across various domains, but their large-scale deployment faces a major obstacle: the high computational cost of long-sequence inference.
Approach:	They propose an algorithm that retains key-value vectors until they are no longer needed to solve reasoning tasks.
Outcome:	The proposed algorithm achieves high accuracy with O(L) time but O(N) memory complexities.

On the Perception Bottleneck of VLMs for Chart Understanding (2025.findings-emnlp)

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Junteng Liu, Weihao Zeng, Xiwen Zhang, Yijun Wang, Zifei Shan, Junxian He

Visual Data in NLP

Challenge:	a perception bottleneck in large vision-language models is critical for chart understanding . instruction tuning improves the extraction capability of LVLMs, but the vision encoder remains a bottleneck .
Approach:	They propose to decompose the perception bottleneck into two components . the vision encoder bottleneck is where visual representation fails to encapsulate the correct information .
Outcome:	The proposed approach significantly mitigates the vision encoder bottleneck and improves the ability of LVLMs to comprehend charts.

Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations (2025.findings-naacl)

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Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran Yang, Yumeng Zhang, Shan Huang, Yiran Wei, Wai Lam

Dialogue Systems & Conversational Agents

Challenge:	a new paradigm for dialogue systems is being developed to mimic human interactions . the current single-step dialogue paradigm lacks the depth and fluidity of human interactions.
Approach:	They propose a step-by-step dialogue paradigm that mimics human interactions . they use a dataset to fine-tune existing language models .
Outcome:	The proposed system mimics the dynamic nature of human conversations . it is compared with existing paradigms and will be released later this year .

Lexical Diversity-aware Relevance Assessment for Retrieval-Augmented Generation (2025.acl-long)

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Zhange Zhang, Yuqing Ma, Yulong Wang, Shan He, Tianbo Wang, Siqi He, Jiakai Wang, Xianglong Liu

Information Retrieval

Challenge:	Extensive experiments on widely used benchmarks demonstrate the efficacy of our approach, yielding a 10.6% accuracy improvement on HotpotQA.
Approach:	They propose a Lexical Diversity-aware RAG method to address the biases in relevant information retrieval and utilization induced by lexical diversity.
Outcome:	Extensive experiments on widely used benchmarks show the proposed method yields a 10.6% accuracy improvement on HotpotQA.

Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling (2025.emnlp-industry)

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Xiaoyu Liu, Di Liang, Hongyu Shan, Peiyang Liu, Yonghao Liu, Muling Wu, Yuntao Li, Xianjie Wu, Li Miao, Jiangrong Shen, Minlong Peng

MR. Judge: Multimodal Reasoner as a Judge (2025.emnlp-main)

Challenge:	Generative RMs (GRMs) lack contextual and background information during inference, leading to incomplete evaluations.
Approach:	They propose a modular and interpretable framework that integrates side-branch models as auxiliary feature generators.
Outcome:	The proposed framework outperforms scalar and saline reward models in robustness and alignment with human preferences.

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Renjie Pi, Haoping Bai, Qibin Chen, Xiaoming Simon Wang, Jiulong Shan, Xiaojiang Liu, Meng Cao

Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation (2024.acl-long)

Challenge:	Effective reward modeling is especially valuable in reinforcement learning (RLHF) .
Approach:	They propose a paradigm for empowering general-purpose MLLMs judges with strong reasoning capabilities by using multiple-choice problem models instead of directly assigning scores.
Outcome:	The proposed model surpasses GPT-4o on VL-RewardBench and improves performance on MM-Vet by up to 7.7%.

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Aiwei Liu, Haoping Bai, Zhiyun Lu, Xiang Kong, Xiaoming Wang, Jiulong Shan, Meng Cao, Lijie Wen

Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting (2024.findings-emnlp)

Challenge:	Existing methods to evaluate preference data without human annotations are difficult . et al., 2022b) is effective for aligning large language models with human expectations .
Approach:	They propose a method to evaluate the response preference using output probabilities under contrastive prompts.
Outcome:	The proposed method could surpass the RLHF method without human-annotated preference data.

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Siyi Liu, Yang Li, Jiang Li, Shan Yang, Yunshi Lan

Low-Resource NLP

Challenge:	Existing methods for zero-shot Relation Extraction (RE) lack detailed, context-specific prompts for understanding various sentences and relations.
Approach:	They propose a framework that uses a three-stage diversity approach to prompt LLMs by generating multiple synthetic samples that encapsulate specific relations from scratch.
Outcome:	The proposed framework outperforms existing LLM-based zero-shot RE methods on benchmark datasets and shows that it produces high-quality synthetic data that enhances performance.

Characterizing the Impacts of Instances on Robustness (2023.findings-acl)

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Rui Zheng, Zhiheng Xi, Qin Liu, Wenbin Lai, Tao Gui, Qi Zhang, Xuanjing Huang, Jin Ma, Ying Shan, Weifeng Ge

Robustness in NLP

Challenge:	Existing defense approaches focus on developing new model structures or training algorithms, but they do little to tap the potential of training instances.
Approach:	They propose a method that can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.
Outcome:	The proposed method can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.

EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models (2025.acl-long)

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Zekun Wang, MingHua Ma, Zexin Wang, Rongchuan Mu, Liping Shan, Ming Liu, Bing Qin

Mem2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation (2026.acl-long)

Challenge:	Existing methods for accelerating Large Vision-Language Models lack comprehensive evaluation across diverse backbones, benchmarks, and metrics.
Approach:	They propose EffiVLM-BENCH framework for evaluating absolute performance and generalization and loyalty.
Outcome:	The proposed framework offers insights into optimal strategies for accelerating LVLMs.

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Zihao Cheng, Zeming Liu, Yingyu Shan, Xinyi Wang, Xiangrong Zhu, Yunpu Ma, Hongru Wang, Yuhang Guo, Wei Lin, Yunhong Wang

SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)

Challenge:	Existing frameworks that focus on static tools and static assets are ineffective for self-evolving agents.
Approach:	They propose a paradigm of co-evolutionary Capability Expansion and Experience Distillation that leverages accumulated experience to guide dynamic creation of assets.
Outcome:	The proposed framework improves performance in single-task and cross-task settings by 18.53% over standard LLMs, 11.80% over agents evolving solely through experience, and 6.46% over those evolving solelly through asset creation.

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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin

StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback (2024.acl-long)

Challenge:	Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation.
Approach:	They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model.
Outcome:	The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks.

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Shihan Dou, Yan Liu, Haoxiang Jia, Enyu Zhou, Limao Xiong, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang

Programming Languages in NLP

Challenge:	Existing work integrates reinforcement learning with compiler feedback to enhance code generation quality but the long code generated by LLMs makes RL exploration ineffective.
Approach:	They propose a framework that integrates reinforcement learning and compiler feedback to enhance code generation quality.
Outcome:	The proposed framework outperforms state-of-the-art approaches in corresponding benchmarks and integrates reinforcement learning with compiler feedback to improve code generation quality.

GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning (2026.findings-acl)

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Yi Chen, Yuying Ge, Rui Wang, Yixiao Ge, Junhao Cheng, Ying Shan, Xihui Liu

Multimodality

Challenge:	Recent reinforcement learning approaches have advanced reasoning in Large Language Models (LLMs), yet their adaptation to multimodal LLMs remains underexplored.
Approach:	They propose a reinforcement learning framework that eliminates KL penalties and rewards consistency . they propose GRPO-CARE, which outperforms standard GR PO, with a base reward for accuracy and an adaptive bonus for consistency.
Outcome:	The proposed framework outperforms standard GRPO on the most difficult evaluation level and reasoning consistency test benchmarks.

LaERC-S: Improving LLM-based Emotion Recognition in Conversation with Speaker Characteristics (2025.coling-main)

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Yumeng Fu, Junjie Wu, Zhongjie Wang, Meishan Zhang, Lili Shan, Yulin Wu, Bingquan Liu

Sentiment Analysis

Challenge:	Emotion recognition in conversation (ERC) is a task of discerning human emotions for each utterance within a conversation.
Approach:	They propose a framework that uses large language models to analyze speaker characteristics . they use two-stage learning to make the models reason speaker characteristics and track emotion of the speaker .
Outcome:	The proposed framework outperforms existing methods on three benchmark datasets.

A Survey of Large Language Model-Based Search Agents (2026.acl-long)

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Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang

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

Challenge:	Large Language Models (LLMs) have revolutionized web search, but their integration is static and cannot handle complex contexts.
Approach:	They analyze existing research and analyze existing work from the perspectives of architecture, optimization, application, and evaluation.
Outcome:	The proposed models can comprehend user intentions and context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web.

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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang

Robustness in NLP

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

DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization (2023.acl-long)

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SongYang Gao, Shihan Dou, Yan Liu, Xiao Wang, Qi Zhang, Zhongyu Wei, Jin Ma, Ying Shan

Robustness in NLP

Challenge:	Existing adversarial training methods require multi-step gradient ascents or word substitutions to obtain adversarials, which impairs the effectiveness of adversariarial training.
Approach:	They propose a procedure for instead adversarial training with only clean data that estimates the adversarials loss by perturbing the input data’s probability distribution rather than their embeddings.
Outcome:	The proposed procedure reduces time consumption by up to 70% compared to current best-performing adversarial training methods.

CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information (2025.coling-main)

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Yuxin Wang, MingHua Ma, Zekun Wang, Jingchang Chen, Shan Liping, Qing Yang, Dongliang Xu, Ming Liu, Bing Qin

Green & Sustainable NLP

Challenge:	Existing LLM pruning works focus on unstructured pruning, which typically requires special hardware support for a practical speed-up.
Approach:	They propose a network pruning framework that leverages both coarse and fine-grained activation information as an importance criterion to guide pruning.
Outcome:	The proposed framework outperforms existing pruning methods on diverse models across sparsity budgets.

Ambiguous Learning from Retrieval: Towards Zero-shot Semantic Parsing (2023.acl-long)

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Shan Wu, Chunlei Xin, Hongyu Lin, Xianpei Han, Cao Liu, Jiansong Chen, Fan Yang, Guanglu Wan, Le Sun

Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization (2023.findings-emnlp)

Challenge:	Existing neural semantic parsers require a large amount of training data which is expensive and difficult to obtain.
Approach:	They propose a framework for a supervised retrieval system based on pretrained language models . they propose ambiguous supervision to improve the precision and coverage of the task .
Outcome:	The proposed approach outperforms state-of-the-art zero-shot parsing methods in ambiguous supervision.

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Lei Lin, Shuangtao Li, Yafang Zheng, Biao Fu, Shan Liu, Yidong Chen, Xiaodong Shi