Papers by Deng Zhao

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)

Challenge:	Current multimodal large language models (MLLMs) show limited understanding of dental images.
Approach:	They propose a dental-specialized multimodal large language model trained via staged multimodal alignment and reinforcement learning.
Outcome:	The proposed model outperforms state-of-the-art models on disease classification and dental VQA tasks.

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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin

Challenge:	a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities .
Approach:	They present a comparative analysis to identify and distinguish LLM activities from human activities.
Outcome:	The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities.

CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation (2024.acl-long)

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Weixiang Yan, Haitian Liu, Yunkun Wang, Yunzhe Li, Qian Chen, Wen Wang, Tingyu Lin, Weishan Zhao, Li Zhu, Hari Sundaram, Shuiguang Deng

Refusal-Aware Red Teaming: Exposing Inconsistency in Safety Evaluations (2025.emnlp-main)

Challenge:	Existing benchmarks for evaluating the code understanding and generation capacities of Large Language Models are insufficient . existing benchmarks focus on a narrow range of popular programming languages and specific tasks .
Approach:	They propose an execution-based, multilingual, multitask evaluation benchmark for LLMs . they evaluate coding performance from three dimensions: length, difficulty, efficiency .
Outcome:	The proposed benchmark covers 43 programming languages and eight coding tasks.

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Yongkang Chen, Xiaohu Du, Xiaotian Zou, Chongyang Zhao, Huan Deng, Hu Li, Xiaohui Kuang

Challenge:	Large Language Models (LLMs) require rigorous safety evaluations to be effective.
Approach:	They propose a red teaming framework that detects internal model refusals and contrasts them with judgments from an external safety evaluator to generate test cases that expose such discrepancies.
Outcome:	The proposed framework outperforms existing reinforcement learning-based approaches in generating diverse test cases and achieves a substantially higher discovery rate of refusal gaps.

Beware of Your Po! Measuring and Mitigating AI Safety Risks in Role-Play Fine-Tuning of LLMs (2025.acl-long)

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Weixiang Zhao, Yulin Hu, Yang Deng, Jiahe Guo, Xingyu Sui, Xinyang Han, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning (2024.naacl-long)

Challenge:	Existing role-play fine-tuning techniques improve role adaptability but may degrade safety performance, especially for villainous characters.
Approach:	They propose safety-aware Role-Play Fine-Tuning (SaRFT) to balance role-playing capabilities and safety.
Outcome:	The proposed method outperforms state-of-the-art baselines under both LoRA and full-parameter fine-tuning settings.

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Wenting Zhao, Ye Liu, Yao Wan, Yibo Wang, Qingyang Wu, Zhongfen Deng, Jiangshu Du, Shuaiqi Liu, Yunlong Xu, Philip Yu

Mitigating Shortcuts in Language Models with Soft Label Encoding (2024.lrec-main)

Challenge:	Recent advances in task-oriented parsing involve formulating the task as a sequence-to-sequence problem, relying on a wealth of labeled data.
Approach:	They propose a task-oriented parsing framework that integrates nearest-neighbor learning with a nearest-nearest approach.
Outcome:	The proposed model can be used to synthesize computer programs based on a natural-language prompt without additional data or specialized prompts.

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Zirui He, Huiqi Deng, Haiyan Zhao, Ninghao Liu, Mengnan Du

Beyond Fully Random Masking: Attention-Guided Denoising and Optimization for Diffusion Language Models (2026.acl-long)

Challenge:	Recent studies have shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks.
Approach:	They propose a framework for debiasing shortcuts and a dummy class to encode shortcuts into a model and use it to generate soft labels.
Outcome:	The proposed framework significantly improves out-of-distribution generalization while maintaining satisfactory in-district accuracy.

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Jia Deng, Junyi Li, Xin Zhao, Jinpeng Wang, Hongyu Lu, Ji-Rong Wen

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models (2024.acl-long)

Challenge:	Existing methods for full-attention dLLMs rely on random masking strategies that overlook intrinsic token dependencies.
Approach:	They propose an attention-guided denoising and optimization framework that aligns training and optimization with attention-derived dependencies.
Outcome:	The proposed framework outperforms state-of-the-art methods on mathematical and coding benchmarks.

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Damai Dai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang

MIMIR: A Customizable Agent Tuning Platform for Enhanced Scientific Applications (2024.emnlp-demo)

Challenge:	Mixture-of-Experts (MoE) architectures face challenges in ensuring expert specialization . despite the promising performance, scaling language models to an extremely large scale is associated with exceedingly high computational costs.
Approach:	They propose an architecture that allows for ultimate expert specialization by segmenting experts into mN ones and activating mK from them.
Outcome:	The proposed architecture achieves comparable performance with GShard with 2B parameters and computation.

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Xiangru Tang, Chunyuan Deng, Hanminwang Hanminwang, Haoran Wang, Yilun Zhao, Wenqi Shi, Yi Fung, Wangchunshu Zhou, Jiannan Cao, Heng Ji, Arman Cohan, Mark Gerstein

CQIL: Inference Latency Optimization with Concurrent Computation of Quasi-Independent Layers (2024.acl-long)

Challenge:	Large language models (LLMs) have evolved into interactive agents capable of planning, tool use, and task execution across various tasks.
Approach:	They propose a platform that leverages large language models to generate agent-tuning data for fine-tuneing smaller, specialized models.
Outcome:	MIMIR enables large models to simulate various roles and create interaction data, which can then be used to fine-tune open-source models like LLaMA2.

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Longwei Zou, Qingyang Wang, Han Zhao, Jiangangkong Jiangangkong, Yi Yang, Yangdong Deng

A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods to improve inference efficiency target to reduce per-layer latency, but ignore cumulative latency due to number of layers.
Approach:	They propose to identify quasi-independent layers that can be concurrently computed to significantly decrease inference latency.
Outcome:	Empirical results show that the proposed method reduces latency by 48.3% on LLaMA-33B while maintaining close level of performance.

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Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu

To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion (2023.acl-long)

Challenge:	a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences.
Approach:	They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference .
Outcome:	The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks.

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Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie

ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants (2026.acl-long)

Challenge:	Existing methods for embedding knowledge graphs implicitly memorize relation rules to infer missing links, but they are difficult to memorize due to the inherent deficiencies of such implicit memorization strategy.
Approach:	They propose a vertical learning paradigm that allows to explicitly copy target information from related factual triples for more accurate prediction.
Outcome:	The proposed model improves generalization ability and makes distant link prediction significantly easier.

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Pei Wang, Yanan Wu, Xiaoshuai Song, Weixun Wang, Gengru Chen, Zhongwen Li, Kezhong Yan, Qi Liu, Ken Deng, Shuaibing Zhao, Shaopan Xiong, Xuepeng Liu, Xuefeng Chen, Wanxi Deng, Wenbo Su, Bo Zheng

Challenge:	Existing studies on large language model-based agents focus on evaluation benchmarks without training support.
Approach:	They propose a large-scale Chinese shopping simulation environment that uses large language models to train agents.
Outcome:	The proposed model performs poorly in a large-scale and challenging shopping environment in China.

MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms (2026.findings-acl)

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Yibo Wang, Congying Xia, Wenting Zhao, Jiangshu Du, Chunyu Miao, Zhongfen Deng, Philip S. Yu, Chen Xing

Challenge:	Existing evaluation benchmarks for LLM unit test generation focus on function-level code rather than on more practical, challenging multi-file codebases.
Approach:	They propose a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript.
Outcome:	The proposed benchmarks show that most LLMs exhibit moderate performance on MultiFileTest, highlighting the benchmark’s inherent difficulty.

On Safety Risks in Experience-Driven Self-Evolving Agents (2026.findings-acl)

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Weixiang Zhao, Yichen Zhang, Yingshuo Wang, Yang Deng, Yanyan Zhao, Xuda Zhi, Yongbo Huang, Hao He, Wanxiang Che, Bing Qin, Ting Liu

Challenge:	Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduces underexplored safety risks.
Approach:	They investigate how experience accumulation and utilization in self-evolving agents affect safety performance across web-based and embodied environments.
Outcome:	The findings expose inherent limitations of current self-evolving agents and call for more principled strategies to ensure safe and reliable adaptation.

MPO: Multilingual Safety Alignment via Reward Gap Optimization (2025.acl-long)

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Weixiang Zhao, Yulin Hu, Yang Deng, Tongtong Wu, Wenxuan Zhang, Jiahe Guo, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

Multilinguality

Challenge:	Existing preference learning methods for safety alignment are monolingual and struggle with noisy multilingual data.
Approach:	They propose a multilingual reward gaP optimization approach that leverages the well-aligned safety capabilities of the dominant language to improve safety alignment across multiple languages.
Outcome:	Extensive experiments on three LLMs, LLaMA-3.1, Gemma-2 and Qwen2.5, validate MPO’s efficacy in multilingual safety alignment without degrading general multilingual utility.

AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models (2026.findings-acl)

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Yimin Deng, Yejing Wang, Zhenxi Lin, Zichuan Fu, Guoshuai Zhao, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Xian Wu, Li Zhu, Xueming Qian

I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck (2026.acl-long)

Challenge:	Existing methods for temporal reasoning are limited and apply a fixed pipeline to all questions.
Approach:	They propose an adaptive temporal reasoning method that dynamically executes reasoning steps based on context and task requirements.
Outcome:	Experiments on two temporal QA benchmarks show the proposed method works.

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Huilin Deng, Hongchen Luo, Yue Zhu, Long Li, Zhuoyue Chen, Xinghao Zhao, Ming LI, Chuyang Zhao, Jihai Zhang, MengChang Wang, Yang Cao, Yu Kang

Vision-Language Models Mistake Head Orientation for Gaze Direction: Nonverbal Conversation Cues (2026.findings-acl)

Challenge:	Existing methods for large language model reasoning suffer from exploration collapse due to the semantic homogeneity of random rollouts.
Approach:	They propose to use latent policy optimization via iterative information bottleneck to optimize reasoning trajectories by diversifying reasoning .
Outcome:	Empirical results show that the proposed method achieves state-of-the-art performance with margins of up to 5.3% in accuracy and 7.4% in diversity metrics.

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Zory Zhang, Pinyuan Feng, Bingyang Wang, Tianwei Zhao, Suyang Yu, Qingying Gao, Hokin Deng, Ziqiao Ma, Yijiang Li, Dezhi Luo

Natural Language Interfaces

Challenge:	Where someone looks is a nonverbal communication cue that children and adults readily use.
Approach:	They used 1,360 real-world photos to construct evaluation stimuli for Vision-Language Models (VLMs) they found a substantial performance gap between VLMs and humans .
Outcome:	The proposed model outperforms existing models in predicting gaze direction using head orientation rather than eye appearance.

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory (2026.acl-long)

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Derong Xu, Shuochen Liu, Pengfei Luo, Pengyue Jia, Yingyi Zhang, Yi Wen, Yimin Deng, Wenlin Zhang, Enhong Chen, Xiangyu Zhao, Tong Xu

Challenge:	Existing memory systems rely on static, hand-crafted update rules for personalization, but sparse outcome rewards provide weak supervision, resulting in unstable long-horizon optimization.
Approach:	They propose a memory guideline optimization framework that learns how memory should be organized and what information to update.
Outcome:	The proposed framework learns how memory should be organized and what information to update.

Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks (2024.naacl-long)

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Yunqi Zhang, Songda Li, Chunyuan Deng, Luyi Wang, Hui Zhao

Visual Data in NLP

Challenge:	Existing vision-language models focus on salient attributes but ignore contextualized nuances, resulting in gender bias.
Approach:	They propose a task-agnostic generation framework to mitigate gender bias in vision-language models.
Outcome:	The proposed framework can mitigate gender bias in vision-language models . it yields all-sided but gender-obfuscated narratives, which prevents concentration on localized image features, especially gender attributes.

WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild (2024.emnlp-demo)

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Yuntian Deng, Wenting Zhao, Jack Hessel, Xiang Ren, Claire Cardie, Yejin Choi

Dialogue Systems & Conversational Agents

Challenge:	Currently, the volume and complexity of chat logs makes it difficult to analyze individual conversations.
Approach:	They propose a tool that enables fast, versatile, and large-scale conversation analysis by combining search and visualization capabilities with a list of criteria.
Outcome:	The proposed tool can be extended to handle millions of chat logs and other datasets.

Logic-Thinker: Teaching Large Language Models to Think more Logically. (2025.findings-emnlp)

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Chengyao Wen, Qiang Cheng, Shaofei Wang, Zhizhen Liu, Deng Zhao, Lei Liang

ReLearn: Unlearning via Learning for Large Language Models (2025.acl-long)

Challenge:	Recent Large Reasoning Models (LRMs) have demonstrated the ability to generate long chains of thought (LongCoT) LongCoT still faces challenges such as redundancy and logical incoherence.
Approach:	They propose a neural-symbolic reasoning framework that generates chains of thought . they propose Logic-Thinker, which transforms symbolic solvers into chains of thoughts .
Outcome:	The proposed framework outperforms models fine-tuned with ThinkerCoT on logic reasoning tasks.

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Haoming Xu, Ningyuan Zhao, Liming Yang, Sendong Zhao, Shumin Deng, Mengru Wang, Bryan Hooi, Nay Oo, Huajun Chen, Ningyu Zhang

Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution (2026.findings-acl)

Challenge:	Existing methods for unlearning large language models often rely on reverse optimization to reduce target token probabilities.
Approach:	They propose a data augmentation and fine-tuning pipeline for effective unlearning . they propose augmentation, evaluation frameworks to measure contextual forgetting .
Outcome:	The proposed framework achieves targeted forgetting while preserving high-quality outputs.

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Zouying Cao, Jiaji Deng, Li Yu, Weikang Zhou, Zhaoyang Liu, Bolin Ding, Hai Zhao

Green & Sustainable NLP

Challenge:	Existing frameworks treat memory as a static append-only archive . Existing systems focus on passive accumulation, resulting in a 'passive accumulation' of memory.
Approach:	They propose a framework for experience-driven agent evolution that integrates procedural memory with contextual information to create a high-quality experience pool.
Outcome:	Experiments on BFCL-V3 and AppWorld show that ReMe outperforms memoryless Qwen3-8B.

Knowledge Boundary of Large Language Models: A Survey (2025.acl-long)

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Moxin Li, Yong Zhao, Wenxuan Zhang, Shuaiyi Li, Wenya Xie, See-Kiong Ng, Tat-Seng Chua, Yang Deng

Curr-ReFT: Overcoming Training Bottlenecks in Small-scale Vision-Language Models via Curriculum Reinforcement Finetuning (2025.findings-emnlp)

Challenge:	Large language models (LLMs) store vast amount of knowledge in their parameters, but they still have limitations in the memorization and utilization of certain knowledge.
Approach:	They propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types.
Outcome:	The proposed definition of the LLM knowledge boundary and taxonomy categorizes knowledge into four distinct types . aims to offer a comprehensive overview, facilitate access to key issues, and inspire further advancements in LLM research.

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Huilin Deng, Ding Zou, Xinghao Zhao, Rui Ma, Yanming Guo, Yang Cao, Yu Kang

ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction (2022.emnlp-main)

Challenge:	State-of-the-art vision-language models require massive scaling that limits practical deployment.
Approach:	They propose to use supervised fine-tuning to train small-scale vision-language models but face out-of-domain collapse when trained with traditional supervised learning (SFT).
Outcome:	Experiments show that curr-reFT achieves state-of-the-art performance across visual tasks in both in- and out-of domain settings and benchmarks.

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Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, Wai Lam

Information Extraction & Text Mining

Challenge:	Existing CCE methods treat contracts as plain text, creating a barrier to understanding complex contracts.
Approach:	They propose a framework to model implicit relations in legal contracts to improve contract understanding . they propose Term-Definition Relation captures the relation between important terms and their definitions .
Outcome:	The proposed framework improves on two CCE tasks in conventional and zero-shot settings.

YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)

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Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen

ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM (2025.findings-emnlp)

Challenge:	prevailing pre-training approaches for large language models involve several complexities.
Approach:	They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data .
Outcome:	The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data .

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Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo, Bingjing Xu, Weiwei Deng, Qingwei Lin, Dongmei Zhang

C2D2 Dataset: A Resource for the Cognitive Distortion Analysis and Its Impact on Mental Health (2023.findings-emnlp)

Challenge:	In-context learning (ICL) is a common practice to enhance LLM performance on domain-specific tasks.
Approach:	They propose a method that leverages large language models to enhance query-ad relevance labeling . they identify and provide superior demonstrations for ICL, thereby improving labeling performance .
Outcome:	The proposed method improves query-ad relevance labeling performance by providing demonstrations.

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Bichen Wang, Pengfei Deng, Yanyan Zhao, Bing Qin

Responsible & Trustworthy NLP

Challenge:	Cognitive distortions refer to patterns of irrational thinking that can lead to distorted perceptions of reality and mental health problems in individuals.
Approach:	They propose to use the C2D2 dataset to detect cognitive distortions in everyday life scenes to improve existing models of mental health detection.
Outcome:	The proposed dataset contains 7,500 cognitive distortion thoughts in everyday life scenes.

Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference (P18-1)

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Boyuan Pan, Yazheng Yang, Zhou Zhao, Yueting Zhuang, Deng Cai, Xiaofei He

DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)

Challenge:	Existing approaches to natural language inference focus on interaction architectures of sentences . but, we propose to transfer knowledge from discourse markers to augment the model .
Approach:	They propose to transfer knowledge from discourse markers to augment the quality of the NLI model.
Outcome:	The proposed method achieves state-of-the-art performance on large-scale datasets.

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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang

Challenge:	Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability.
Approach:	They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence.
Outcome:	The proposed model outperforms baselines on three real-world datasets.

Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)

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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni

Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent Discovery (2024.findings-emnlp)

Challenge:	MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture.
Approach:	They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content.
Outcome:	The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context.

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Yimin Deng, Yuxia Wu, Guoshuai Zhao, Li Zhu, Xueming Qian

A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs (2025.findings-acl)

Challenge:	Existing methods focus on transferring in-domain (IND) prior knowledge to out-of-domain data through pre-training and clustering.
Approach:	They propose a Pseudo-Label enhanced Prototypical Contrastive Learning model for uniformed intent discovery that integrates supervised and pseudo signals from IND and OOD data.
Outcome:	The proposed method has been proven effective in two different settings of discovering new intents.

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Yimin Deng, Yuxia Wu, Yejing Wang, Guoshuai Zhao, Li Zhu, Qidong Liu, Derong Xu, Zichuan Fu, Xian Wu, Yefeng Zheng, Xiangyu Zhao, Xueming Qian

Knowledge Representation

Challenge:	Existing methods focus on graph structure learning or semantic reasoning, lacking the capability to capture the inherent differences between historical and non-historical events.
Approach:	They propose a temporal knowledge graph reasoning framework that integrates both structural and semantic information to guide the reasoning process for different events.
Outcome:	The proposed framework integrates structural and semantic information to predict future events . it can provide evidence for many downstream tasks, including situation analysis and political decision making .

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency (2026.acl-long)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Haoming Xu, Ningyuan Zhao, Yunzhi Yao, Weihong Xu, Hongru Wang, Xinle Deng, Shumin Deng, Jeff Z. Pan, Huajun Chen, Ningyu Zhang

Challenge:	Existing evaluations rely on point-wise confidence, which can mask brittle belief.
Approach:	They propose a measure of belief robustness that evaluates coherence across a conceptual neighborhood.
Outcome:	The proposed model is more resistant to interference than existing models.

Learning to Paraphrase for Alignment with LLM Preference (2024.findings-emnlp)

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Junbo Fu, Guoshuai Zhao, Yimin Deng, Yunqi Mi, Xueming Qian

Text Generation

Challenge:	Large Language Models (LLMs) exhibit the issue of paraphrase divergence, which means that when a question is phrased in a slightly different but semantically similar way, LLM may output a wrong response . retraining faces challenges in meeting the computational costs and privacy security demands of LLMs.
Approach:	They propose a black-box method that enhances model performance by paraphrasing questions in expressions preferred by the model.
Outcome:	The proposed method improves performance by paraphrasing questions in expressions preferred by the model.

Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter (2025.findings-emnlp)

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Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu

UER: An Open-Source Toolkit for Pre-training Models (D19-3)

Challenge:	Existing supervised fine-tuning (SFT) fails to address these issues, as it trains models on single gold-standard responses without modeling nuanced strategy trade-offs.
Approach:	They propose a two-stage framework that optimizes strategy selection preferences at each dialogue turn.
Outcome:	The proposed framework improves strategy selection preferences at each dialogue turn.

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Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, Xiaoyong Du

Retrieved Sequence Augmentation for Protein Representation Learning (2024.emnlp-main)

Challenge:	Existing work on pre-training models have shown that it is important to use a framework to deploy various pre- training models efficiently.
Approach:	They propose an assemble-on-demand pre-training toolkit that assembles pre-trained models on demand and encapsulates them with rich modules.
Outcome:	The proposed framework can reproduce state-of-the-art models or develop models that remain unexplored.

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Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Sheng Wang, Lingpeng Kong

ESDM: Early Sensing Depression Model in Social Media Streams (2024.lrec-main)

Challenge:	Using multiple sequence alignments (MSA) to extract evolutionary knowledge is limited.
Approach:	They propose to use multiple sequence alignments to augment protein representations . they propose to employ Retrieved Sequence Augmentation to enhance protein representation learning .
Outcome:	The proposed method surpasses MSA Transformer by 5% in structural and property prediction tasks while being 373 times faster.

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Bichen Wang, Yuzhe Zi, Yanyan Zhao, Pengfei Deng, Bing Qin

Information Extraction & Text Mining

Challenge:	Existing approaches to use social media data for depression detection are based on traditional risk detection (TRD) and early risk detection of depression (ERD).
Approach:	They propose a model that uses two modules: classification with partial information module (CPI) and decision for classification moment module (DMC) and an early detection loss function.
Outcome:	The proposed model outperforms benchmarks in both accuracy and accuracy with evolving partial data.

Can LLMs Act as Historians? Evaluating Historical Research Capabilities of LLMs via the Chinese Imperial Examination (2026.acl-long)

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Lirong Gao, Zeqing Wang, Yuyan Cai, Jiayi Deng, Yanmei Gu, Yiming Zhang, Jia Zhou, Yanfei Zhang, Junbo Zhao

Token-level Proximal Policy Optimization for Query Generation (2025.emnlp-main)

Challenge:	Existing benchmarks assess basic knowledge breadth or lexical understanding, failing to capture higher-order skills that are central to historical research.
Approach:	They propose a benchmark anchored in the Chinese Imperial Examination system that assesses historical knowledge and lexical understanding.
Outcome:	The new benchmark aims to assess the ability of LLMs to process historical materials and documents.

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Yichen Ouyang, Lu Wang, Fangkai Yang, Pu Zhao, Chenghua Huang, Jianfeng Liu, Bochen Pang, Yaming Yang, Yuefeng Zhan, Hao Sun, Qingwei Lin, Saravan Rajmohan, Weiwei Deng, Dongmei Zhang, Feng Sun

Text Generation

Challenge:	Large Language Models (LLMs) have improved search engines and recommendation systems through their text understanding capabilities.
Approach:	They propose a token-level proximal policy optimization approach to empower LLMs to perform better in query generation through fine-tuning.
Outcome:	The proposed approach outperforms existing LLMs on an open-source and industrial dataset.

Effidit: An Assistant for Improving Writing Efficiency (2023.acl-demo)

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Shuming Shi, Enbo Zhao, Wei Bi, Deng Cai, Leyang Cui, Xinting Huang, Haiyun Jiang, Duyu Tang, Kaiqiang Song, Longyue Wang, Chenyan Huang, Guoping Huang, Yan Wang, Piji Li

Green & Sustainable NLP

Challenge:	Effidit is a digital writing assistant that provides three modules to help users write faster and more efficiently.
Approach:	They present Effidit, a digital writing assistant that provides three modules to help users write higher-quality text more efficiently.
Outcome:	Effidit expands the capabilities of a typical writing assistant by providing three modules . Effit can help users create their own text faster and more efficiently .

AdaSteer: Your Aligned LLM is Inherently an Adaptive Jailbreak Defender (2025.emnlp-main)

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Weixiang Zhao, Jiahe Guo, Yulin Hu, Yang Deng, An Zhang, Xingyu Sui, Xinyang Han, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

Responsible & Trustworthy NLP

Challenge:	Activation steering offers training-free defense but relies on fixed steering coefficients, resulting in suboptimal protection and increased false rejections of benign inputs.
Approach:	They propose an adaptive activation steering method that dynamically adjusts model behavior based on input characteristics.
Outcome:	The proposed method outperforms baseline methods across multiple jailbreak attacks with minimal impact on utility.

DORA: A Dual-Objective Reinforcement Learning Framework for Effective and Efficient Multimodal Agentic Search (2026.acl-long)

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Guangming Qin, Yuhao Deng, Yukun Zhao, Zhenyang Li, Junfeng Wang, Dawei Yin, Ye Yuan, Guoren Wang, Yizhou Yan, Chengliang Chai, Lei Cao

Responsible & Trustworthy NLP

Challenge:	Existing methods to train large language models overlook quality of intermediate search results . existing methods often invoke search calls during reasoning, making inference inefficient .
Approach:	They propose a dual-objective reinforcement learning framework to improve search strategies of MLLMs . DORA outperforms state-of-the-art methods, achieving up to 8.4% higher accuracy .
Outcome:	The proposed model outperforms state-of-the-art methods while reducing search calls by 9.7%.

WatME: Towards Lossless Watermarking Through Lexical Redundancy (2024.acl-long)

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Liang Chen, Yatao Bian, Yang Deng, Deng Cai, Shuaiyi Li, Peilin Zhao, Kam-Fai Wong

Challenge:	Existing methods for text watermarking rely on arbitrary vocabulary partitioning during decoding, which compromises the availability of suitable tokens and significantly degrades the quality of responses.
Approach:	They propose a method that leverages linguistic prior knowledge of lexical redundancies in LLM vocabularies to seamlessly integrate watermarks.
Outcome:	The proposed approach preserves the expressive power of large language models while preserving watermark detectability.

Don’t Just Say “I don’t know”! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations (2024.emnlp-main)

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Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng, Tat-Seng Chua

MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning (2026.findings-acl)

Challenge:	Existing studies investigate ways to refuse to answer unknown questions . Large Language Models (LLMs) display a significant level of overconfidence when answering questions that they are aware of.
Approach:	They propose a self-alignment method to utilize Large Language Models to enhance its response-ability to unknown questions.
Outcome:	The proposed method is superior to baseline methods on four types of unknown questions.

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Yimin Deng, Zhenxi Lin, Yejing Wang, Guoshuai Zhao, Pengyue Jia, Zichuan Fu, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Li Zhu, Xian Wu, Xueming Qian

Unveiling the Spectrum of Data Contamination in Language Model: A Survey from Detection to Remediation (2024.findings-acl)

Challenge:	Existing approaches focus on diagnostic reasoning based on internal model knowledge or static knowledge bases.
Approach:	They propose a two-stage diagnostic reasoning framework that integrates multi-perspective evidence to generate a diagnostic prediction.
Outcome:	The proposed method generates suspected diagnoses and reasoning traces from web search, SOAP-formatted case, and clinical case database.

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Chunyuan Deng, Yilun Zhao, Yuzhao Heng, Yitong Li, Jiannan Cao, Xiangru Tang, Arman Cohan

AgentPro: Enhancing LLM Agents with Automated Process Supervision (2025.emnlp-main)

Challenge:	Data contamination is a problem in Large language models due to the reliance on extensive internet-derived training corpora.
Approach:	They present a survey on the topic of data contamination in large language models.
Outcome:	The results of the first survey on data contamination in large language models provide a comprehensive guide for NLP researchers seeking a systematic understanding of the issue.

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Yuchen Deng, Shichen Fan, Naibo Wang, Xinkui Zhao, See-Kiong Ng

Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents (2025.acl-long)

Challenge:	Existing frameworks lack explicit supervision during the reasoning process, which may lead to error propagation across reasoning chains.
Approach:	They propose a framework which automates process supervision for large language model agents by automatically generating step-level annotations and developing a process reward model based on these annotations.
Outcome:	The proposed framework outperforms existing agent-based methods on four datasets and achieves a 6.32% increase in accuracy.

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Fanhang Man, Huandong Wang, Jianjie Fang, Zhaoyi Deng, Baining Zhao, Xinlei Chen, Yong Li

Sentiment Analysis

Challenge:	Existing methods to predict sentiments on social media are limited and do not consider reciprocal influences among social media users.
Approach:	They propose a multi-perspective role-playing framework to simulate human response processes to extract sentiment-related features from social media messages.
Outcome:	The proposed model improves sentiment forecasting at microscopic and macroscopic levels.

The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models (2026.acl-long)

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Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao, Shimin Tao, Minggui HE, Chenxin Liu, Zhang Li, null Mahongxia, Jiaxin Guo, Chen Liu, Liqun Deng, Jiansheng Wei, Xiaojun Meng, Fanyi Du, Daimeng Wei, Yanghua Xiao

Investigating Data Contamination in Modern Benchmarks for Large Language Models (2024.naacl-long)

Challenge:	Existing multilingual evaluation benchmarks neglect cultural nuances and lack language coverage in subjective tasks.
Approach:	They propose a framework that categorizes evaluation tasks into three cultural layers and nine cognitive sub-layers.
Outcome:	The proposed framework surpasses prior coverage by up to 111% on 20+ LLMs.

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Chunyuan Deng, Yilun Zhao, Xiangru Tang, Mark Gerstein, Arman Cohan

SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)

Challenge:	Existing evaluation benchmarks for large language models are inflated and inconsistent with actual performance.
Approach:	They propose a retrieval-based system to explore potential overlaps between benchmarks and pretraining corpora and a protocol to investigate testset slot guessing.
Outcome:	The proposed method exploits overlaps between evaluation benchmarks and pretraining corpora and masks a wrong answer in a multiple choice question and prompts the model to fill in the gap.

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Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen

Dual Activation-Weight Sparsity: A Training-Free Framework for Efficient Large Language Model Compression (2026.acl-long)

Challenge:	Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data.
Approach:	They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments.
Outcome:	The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments.

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Luoyang Sun, Guangyan Li, Cheng Deng, Haifeng Zhang, Jian Zhao, Yongqiang Tang, Wensheng Zhang, Jun Wang

M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)

Challenge:	Large language models (LLMs) excel at natural language tasks but face deployment bottlenecks due to computational demands.
Approach:	They propose a training-free framework that exploits activation and weight sparsity . they use a three-tier routing strategy that uses magnitude-based pruning .
Outcome:	Experiments on Llama and Mistral models show that DAWS outperforms activation-weight sparsity pruning methods.

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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng

Programming Languages in NLP

Challenge:	Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities .
Approach:	They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models.
Outcome:	The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree.

FastBERT: a Self-distilling BERT with Adaptive Inference Time (2020.acl-main)

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Weijie Liu, Peng Zhou, Zhiruo Wang, Zhe Zhao, Haotang Deng, Qi Ju