Papers by Yue Zhao

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

Challenge:	Existing datasets often rely on synthetic data or figure-caption pairs, failing to capture the depth and complexity of geoscientific reasoning.
Approach:	They propose a multimodal scientific dataset and benchmark curated from open-access publications.
Outcome:	MSEarth features over 289K figures with captions enriched by contextual discussions and reasoning from original papers.

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

Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)

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

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Bin Liang, Ang Li, Jingqian Zhao, Lin Gui, Min Yang, Yue Yu, Kam-Fai Wong, Ruifeng Xu

HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models (2026.acl-long)

Challenge:	Existing methods for stance detection for pure texts have limited results to multi-modal content.
Approach:	They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities.
Outcome:	The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter .

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Feiyu Zhao, Yiming Chen, Wenhuan Lu, Daipeng Zhang, Xianghu Yue, Jianguo Wei

Reasoning

Challenge:	Existing studies on hallucination focus on text or vision, while few audio-oriented studies are limited in scale, modality coverage, and diagnostic depth.
Approach:	They propose a large-scale benchmark for evaluating hallucinations across speech, sound, and music.
Outcome:	The proposed model improves hallucination rate, yes/no bias, error-type analysis, and refusal rate.

Seeing All Sides: Multi-Perspective In-Context Learning for Subjective NLP (2026.findings-eacl)

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Benedetta Muscato, Yue Li, Gizem Gezici, Zhixue Zhao, Fosca Giannotti

A Survey on LLM-based Conversational User Simulation (2026.eacl-long)

Challenge:	Modern language models excel at factual reasoning but struggle with value diversity, authors say . task-sensitive tasks such as hate speech expose this limitation . human disagreement captures the diversity of plausible human perspectives, authors argue .
Approach:	They evaluate four large language models with human disagreements on five datasets . they find multi-perspective in-context learning outperforms standard prompting .
Outcome:	The proposed approach outperforms standard prompting on English labels while disaggregated soft predictions better align with human judgments in Arabic and Italian datasets.

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Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi

RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization (2025.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have enabled high-fidelity generation of synthetic user conversation.
Approach:	They propose a taxonomy covering user granularity and simulation objectives . they analyze core techniques and evaluation methodologies to help them understand the latest developments .
Outcome:	The proposed model enables high-fidelity generation of synthetic user conversation.

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Tianci Liu, Haoxiang Jiang, Tianze Wang, Ran Xu, Yue Yu, Linjun Zhang, Tuo Zhao, Haoyu Wang

3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection (2025.emnlp-main)

Challenge:	Large language models (LLMs) have impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings.
Approach:	They propose a robust RAG framework for large language models via Margin-aware Preference Optimization to enhance the accuracy and reliability of SLMs.
Outcome:	The proposed framework surpasses state-of-the-art benchmarks on three open-domain question answering tasks.

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Hongxin Ding, Yue Fang, Runchuan Zhu, Xinke Jiang, Jinyang Zhang, Yongxin Xu, Weibin Liao, Xu Chu, Junfeng Zhao, Yasha Wang

CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space (2025.emnlp-main)

Challenge:	Effective domain adaptation typically involves supervised fine-tuning on carefully selected instruction-tuned data.
Approach:	They propose a model-centric data selection framework that aligns data selection with the model’s knowledge distribution to improve model performance.
Outcome:	The proposed framework outperforms existing methods by up to 2.97% accuracy in the healthcare domain.

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Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, Jincai Huang

Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs (2026.findings-acl)

Challenge:	Embodied Question Answering (EQA) tasks are primarily focused on indoor environments, leaving the complexities of urban settings unexplored.
Approach:	They propose a task where an embodied agent answers open-vocabulary questions in dynamic city spaces.
Outcome:	The proposed agent achieves 60.7% of human-level answering accuracy compared to baselines . the proposed agent outperforms existing agents in open-ended city spaces .

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Jinbo Liu, Defu Cao, Yifei Wei, Tianyao Su, Yuan Liang, Yushun Dong, Yan Liu, Yue Zhao, Xiyang Hu

Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA (2024.acl-long)

Challenge:	Multi-agent LLMs are rapidly moving from prototype to real-world use . network topology is a first-order security parameter in multi-aggent systems .
Approach:	They propose a framework for comparing topology-conditioned memory leakage in multi-agent LLM systems.
Outcome:	The proposed framework evaluates topology-conditioned memory leakage in multi-agent LLM systems.

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Yue Fan, Jing Gu, Kaiwen Zhou, Qianqi Yan, Shan Jiang, Ching-Chen Kuo, Yang Zhao, Xinze Guan, Xin Wang

CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models (2026.acl-long)

Challenge:	Multipanel images are a common form of visual representations, and humans can achieve approximately 99% accuracy on these questions.
Approach:	They propose a benchmark that tests multipanel visual reasoning models with 6,600 triplets of questions, answers, and multipanel images.
Outcome:	The proposed benchmark features 6,600 triplets of questions, answers, and multipanel images that challenge state-of-the-art Multimodal Large Language Models (MLLMs) human users can attain approximately 99% accuracy on these questions, compared with previous benchmarks.

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Haibo Tong, Zeyang Yue, Feifei Zhao, Erliang Lin, Lu Jia, Ruolin Chen, Yinqian Sun, Qian Zhang, Yi Zeng

Linguistics & Cognitive NLP

Challenge:	Existing benchmarks for Large Language Models (LLMs) are limited to false belief tasks, highlighting bottlenecks in specific dimensions.
Approach:	They propose a benchmark to evaluate Large Language Models' Theory of Mind capabilities . they evaluate 8000 bilingual instances across 46 paradigms and validated by 49 human annotators .
Outcome:	The proposed benchmark reveals performance heterogeneities and bottlenecks in 22 representative models.

CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor (2025.emnlp-main)

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Zhenhua Xu, Xixiang Zhao, Xubin Yue, Shengwei Tian, Changting Lin, Meng Han

HyKGE: A Hypothesis Knowledge Graph Enhanced RAG Framework for Accurate and Reliable Medical LLMs Responses (2025.acl-long)

Challenge:	Existing methods for fingerprinting model ownership traces are vulnerable to illegal plagiarism and are not reliable.
Approach:	They propose a rule-driven fingerprinting framework that encodes contextual correlations across multiple dialogue turns.
Outcome:	The proposed framework achieves stronger stealth and robustness than previous work.

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Xinke Jiang, Ruizhe Zhang, Yongxin Xu, Rihong Qiu, Yue Fang, Zhiyuan Wang, Jinyi Tang, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang

Knowledge Representation

Challenge:	Recent approaches suffer from insufficient and repetitive knowledge retrieval, tedious and time-consuming query parsing, and monotonous knowledge utilization.
Approach:	They propose a retrieval-augmented generation framework which leverages LLMs’ powerful reasoning capacity to compensate for the incompleteness of user queries.
Outcome:	The proposed framework improves the accuracy and reliability of Large Language Models (LLMs) by combining the rich knowledge of LLMs with Hypothesis Outputs.

FedGUI: Benchmarking Federated GUI Agents across Heterogeneous Platforms, Devices, and Operating Systems (2026.findings-acl)

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WenHao Wang, Haoting Shi, Mengying Yuan, Yiquan Lin, Panrong Tong, Hanzhang Zhou, Guangyi Liu, Pengxiang Zhao, Yue Wang, Siheng Chen

DIDS: Domain Impact-aware Data Sampling for Large Language Model Training (2025.emnlp-main)

Challenge:	a lack of benchmarks capture real-world, cross-platform heterogeneity in GUI training . traditional methods to train GUI agents rely on centralized data collection and manual labeling .
Approach:	They propose a benchmark for developing and evaluating federated GUI agents across mobile, web and desktop platforms.
Outcome:	The proposed benchmarks show that cross-platform collaboration improves performance and identify platform and OS as the most influential factors.

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Weijie Shi, Jipeng Zhang, Yaguang Wu, Jingzhi Fang, Shibo Zhang, Yao Zhao, Hao Chen, Ruiyuan Zhang, Yue Cui, Jia Zhu, Sirui Han, Jiajie Xu, Xiaofang Zhou

From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)

Challenge:	Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact.
Approach:	They propose to use a Fisher-Information Matrix-guided metric to measure domain impact to ensure intra-domain consistency and accuracy.
Outcome:	The proposed model achieves 3.4% higher average performance while maintaining comparable training efficiency.

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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley

Gradient-Guided Multi-Judge Prompt Optimization (2026.acl-long)

Challenge:	Large Language Models (LLMs) have been used for selection and training of data for active learning.
Approach:	They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop.
Outcome:	The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances.

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ChenZhuo Zhao, Xinda Wang, Pu Zhao, Yue Huang, Junting Lu, Ziqian Liu, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

Model Surgery: Modulating LLM’s Behavior Via Simple Parameter Editing (2025.naacl-long)

Challenge:	Existing approaches to prompt optimization trade off signal quality against computational cost.
Approach:	They propose a framework that uses a first-order gradient approximation to score segment importance in a continuous masking direction.
Outcome:	The proposed framework improves efficiency and robustness by using a first-order gradient approximation to score segment importance in a continuous masking direction.

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Huanqian Wang, Yang Yue, Rui Lu, Jingxin Shi, Andrew Zhao, Shenzhi Wang, Shiji Song, Gao Huang

Challenge:	Current approaches for detoxification or preventing jailbreaking involve fine-tuning billions of parameters through gradient descent with substantial computational cost.
Approach:	They propose to use supervised fine-tuning and Reinforcement Learning from human feedback to modify LLMs' behavior by directly editing a small subset of parameters.
Outcome:	Experiments show that editing a small subset of parameters can modulate specific behaviors of LLMs, such as detoxification and resistance to jailbreak, with only inference-level computational resources.

Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching (2026.acl-long)

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Bo Lv, Jingbo Sun, Jianwei Lv, Chen Tang, Shaojie Zhang, Nayu Liu, Guoxin Yu, Zihao Li, Qichao Zhang, Dongbin Zhao, Ping Luo, Yue Yu

Leibniz: Theory-of-Mind Driven Neuro-Symbolic Logical Reasoning via Multi-Agent Collaboration (2026.acl-long)

Challenge:	Existing routing methods rely on direct mapping from queries to models based on surface-level features, leading to poor generalizability on out-of-distribution data.
Approach:	They propose a new routing framework that recasts the routing task as a matching process of sifting similar queries from historical logs.
Outcome:	The proposed framework improves matching accuracy while lowering inference costs . it decouples linguistic surface forms from task-intrinsic requirements .

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Yue Fan, Hu Zhang, Yunxiao Zhao, Guangjun Zhang, Hao Zhan, Ru Li, Hongye Tan, Yuanlong Wang

Reasoning

Challenge:	Existing methods for logical reasoning with large language models suffer from insufficient rule semantic grounding and weak rule application mechanisms.
Approach:	They propose a theory-of-mind driven neuro-symbolic reasoning framework that integrates natural language and symbolic representations throughout the reasoning process.
Outcome:	The proposed model surpasses state-of-the-art models in reasoning accuracy and flexibility.

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

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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

Reasoning

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.

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

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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan

Challenge:	Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach:	They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome:	The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .

Representation Purification for End-to-End Speech Translation (2025.coling-main)

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Chengwei Zhang, Yue Zhou, Rui Zhao, Yidong Chen, Xiaodong Shi

Speech & Audio in NLP

Challenge:	Existing approaches to enhance speech translation focus on enhancing knowledge transfer . factors in speech that are not relevant to translation content, such as timbre and rhythm, often limit the efficiency of knowledge transfer.
Approach:	They propose a framework that excludes content-agnostic perturbations from speech representations to mitigate their negative impact on ST.
Outcome:	The proposed framework significantly improves translation performance across all translation directions in three settings and achieves preeminent performance under a transcript-free setting.

Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference (2026.findings-eacl)

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Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma

Convert Language Model into a Value-based Strategic Planner (2025.acl-industry)

Challenge:	Prior studies have examined the impact of structured output on LLMs’ generation quality, often presenting one-way findings.
Approach:	They propose to derive five potential causal structures characterizing the influence of structured output on LLMs’ generation using one assumed and two guaranteed constraints.
Outcome:	The proposed pipeline can be extended to other modules and is not limited to structured output but can be used in industrial applications.

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Xiaoyu Wang, Yue Zhao, Qingqing Gu, Zhonglin Jiang, Yong Chen, Luo Ji

ZeroStance: Leveraging ChatGPT for Open-Domain Stance Detection via Dataset Generation (2024.findings-acl)

Challenge:	Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations.
Approach:	They propose a framework that bootstraps the planning during ESC and determines the optimal strategy based on long-term returns.
Outcome:	The proposed framework outperforms baseline models on ESC datasets and can be used to guide the LLM to response.

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Chenye Zhao, Yingjie Li, Cornelia Caragea, Yue Zhang

Sentiment Analysis

Challenge:	Until recently, zero-shot stance detection was limited to in-domain tasks.
Approach:	They propose a method for stance detection that trains a model that can generalize well to unseen targets across multiple domains.
Outcome:	The proposed method generalizes well to unseen targets across multiple domains over baselines on most benchmarks.

Suggest-Verify-Revise: A Three-Stage Document-Level Event Causality Identification with Narrative Consistency (2026.acl-long)

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Ya Su, Hu Zhang, Dan Qiao, YuJie Wang, Yunxiao Zhao, Yue Fan, Shike Li, Ru Li, Hongye Tan

Challenge:	Existing methods for document-level Event Causality Identification rely on local semantic similarity for independent event-pair discrimination . Existing approaches ignore the influence of the overall narrative backbone in the propagation of causal dependencies and the role differentiation of events within multi-cause/multi-effect structures.
Approach:	They propose a suggest-verify-revise approach for document-level Event Causality Identification with narrative consistency (SVRECI) they integrate heuristic causal suggestions generated by an LLM with structural suggestions derived from hypergraph modeling .
Outcome:	The proposed approach outperforms existing methods on event-storylines and Causal-TimeBank datasets.

TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph (2022.coling-1)

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Zhitong Yang, Bo Wang, Jinfeng Zhou, Yue Tan, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

Explainable Quantum Program Repair with Verifiable Proof Traces (2026.findings-acl)

Challenge:	Existing target-oriented dialogs take a local and greedy strategy for response generation, where global planning is absent.
Approach:	They propose a global planning method for target-oriented dialog on a commonsense knowledge graph to adjust local response generation towards the global target.
Outcome:	The proposed method can reach the target with a higher success rate, fewer turns, and more coherent responses.

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Tingting Li, Ziming Zhao, Zhaoxuan Li, Jiongchi Yu, Xiaofei Yue, Jianwei Yin

Explainability & Interpretability in NLP

Challenge:	Existing approaches to program repair provide only post-hoc, non-verifiable explanations that are not executable or verifiably.
Approach:	They propose a framework that couples repair generation with machine-checkable executable explanations for quantum programs where correctness hinges on subtle semantic properties such as circuit equivalence and fidelity preservation.
Outcome:	Experiments on QASMBench with mutation-generated quantum program bugs show that the proposed framework improves both semantic precision and explanation faithfulness over baselines that rely on unconstrained or purely natural-language explanations.

AD-LLM: Benchmarking Large Language Models for Anomaly Detection (2025.findings-acl)

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Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao

What Did You Refer to? Evaluating Co-References in Dialogue (2021.findings-acl)

Challenge:	Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring.
Approach:	They propose a benchmark that evaluates how large language models (LLMs) can help with NLP anomaly detection.
Outcome:	The proposed model can perform zero-shot detection without tasks-specific training, data augmentation and model selection, and it can suggest unsupervised AD models.

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Wei-Nan Zhang, Yue Zhang, Hanlin Tang, Zhengyu Zhao, Caihai Zhu, Ting Liu

Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling (2025.findings-emnlp)

Challenge:	Existing neural end-to-end dialogue models have limitations on exactly interpreting the linguistic structures in dialogue history context.
Approach:	They propose to directly measure the capability of neural end-to-end dialogue models on understanding the entity-oriented structures via question answering.
Outcome:	The proposed model can understand large-scale English and Chinese human human dialogues using a large-format dataset.

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Yue Zhao, Xiaoyu Wang, Dan Wang, Zhonglin Jiang, Qingqing Gu, Teng Chen, Ningyuan Xi, Jinxian Qu, Yong Chen, Luo Ji

RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation (2023.emnlp-main)

Challenge:	a framework for constructing dialogue world models for natural language tasks is currently lacking.
Approach:	They propose a framework that can be used to train a dialogue world model.
Outcome:	The proposed framework can predict future utterances and user beliefs . it can achieve state-of-the-art performance on emotion classification and sentiment identification .

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Yue Zhang, Leyang Cui, Enbo Zhao, Wei Bi, Shuming Shi

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

Challenge:	Grammatical Error Correction (GEC) systems perform well in academic benchmarks, but in practical applications they may not correct errors when users perform irrelevant modifications.
Approach:	They propose a benchmark to evaluate the context robustness of Grammatical Error Correction systems.
Outcome:	The proposed method improves the accuracy of errors corrected by human annotations.

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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.

MTGP: Multi-turn Target-oriented Dialogue Guided by Generative Global Path with Flexible Turns (2023.findings-acl)

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Anqi Liu, Bo Wang, Yue Tan, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

QAEval: Mixture of Evaluators for Question-Answering Task Evaluation (2025.acl-long)

Challenge:	Existing approaches focus on global planning, which plans toward the target before the conversation.
Approach:	They propose to generate a global path as a natural language sentence instead of a sequence of nodes.
Outcome:	The proposed method has fewer turns, more coherent semantics, and higher success rate than baselines.

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Tan Yue, Rui Mao, Xuzhao Shi, Shuo Zhan, Zuhao Yang, Dongyan Zhao

SAM3-I: Segment Anything with Instructions (2026.acl-long)

Challenge:	Existing QA evaluation methods struggle with open-ended and unstructured responses.
Approach:	They propose a hybrid framework that combines rule-based reliability with LLM-based adaptability to overcome these challenges.
Outcome:	The proposed framework outperforms existing models like GPT-4o and Claude-3 in accuracy and cost.

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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng

Low-Resource NLP

Challenge:	Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering.
Approach:	They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework.
Outcome:	Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability.

GUI-Bee: Align GUI Action Grounding to Novel Environments via Autonomous Exploration (2025.emnlp-main)

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Yue Fan, Handong Zhao, Ruiyi Zhang, Yu Shen, Xin Eric Wang, Gang Wu

Green & Sustainable NLP

Challenge:	Recent work of GUI action grounding fine-tunes data from pre-trained MLLMs, but data is limited to specific GUI environments.
Approach:	They propose to use a GUI-based agent to collect environment-specific data and fine-tune GUI grounding models with the collected data.
Outcome:	The proposed model can be extended to other GUI environments to improve performance.

GUICourse: From General Vision Language Model to Versatile GUI Agent (2025.acl-long)

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Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun

Visual Data in NLP

Challenge:	Graphical User Interfaces (GUIs) are a pivotal medium for human-computer interaction.
Approach:	They propose a series of datasets for training visual-based GUI agents using general VLMs.
Outcome:	The proposed GUICourse datasets show that even a small-sized GUI agent performs better on GUI tasks.

Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention (P18-2)

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Yue Zhao, Xiaolong Jin, Yuanzhuo Wang, Xueqi Cheng

Representation Learning

Challenge:	Existing methods for event detection use sentence-level contextual information.
Approach:	They propose a document embedding enhanced bi-RNN model to detect events in sentences . they use hierarchical and supervised attention based RNN to learn document embeds .
Outcome:	The proposed model compares with state-of-the-art models on a ACE-2005 dataset.

Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach (2021.naacl-main)

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Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, Chao Zhang

ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation (2025.emnlp-main)

Challenge:	Fine-tuned pre-trained language models (LMs) have enormous success in many natural language processing tasks, but they still require excessive labeled data in the fine-tuning stage.
Approach:	They propose a framework to enable fine-tuning pre-trained language models with weak supervision without any labeled data.
Outcome:	The proposed framework outperforms the strongest baseline and achieves competitive performance with fully-supervised fine-tuning methods.

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Minghua He, Yue Chen, Fangkai Yang, Pu Zhao, Wenjie Yin, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

Read Anywhere Pointed: Layout-aware GUI Screen Reading with Tree-of-Lens Grounding (2024.emnlp-main)

Challenge:	Existing code translation models only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code.
Approach:	They propose an LLM specifically designed for code translation called ExeCoder . it uses executability representations such as functional semantics and syntax structures to enhance LLMs' capabilities.
Outcome:	The proposed model outperforms existing open-source code translation models on two metrics.

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Yue Fan, Lei Ding, Ching-Chen Kuo, Shan Jiang, Yang Zhao, Xinze Guan, Jie Yang, Yi Zhang, Xin Wang

Challenge:	Existing models for GUI understanding ignore a key GUI-referring task: screen reading based on user-indicated points.
Approach:	They propose a Tree-of-Lens agent that constructs a Hierarchical Layout Tree based on user input points and a GUI screenshot.
Outcome:	The proposed agent can interpret the Screen Point-and-Read task on mobile, web, and operating systems.

Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction (2026.findings-eacl)

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Chong Zhang, Yixi Zhao, Yulu Xie, Chenshu Yuan, Yi Tu, Ya Guo, Mingxu Chai, Ziyu Shen, Yue Zhang, Qi Zhang

Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding (2024.emnlp-main)

Challenge:	PTLMs have shown remarkable success in multiple information extraction tasks . however, their performance in real-world scenarios falls short of expectations .
Approach:	They propose to use an entity-centric dataset to evaluate PTLMs' performance . they find that inadequate annotations in benchmark datasets lead to spurious correlations .
Outcome:	The proposed dataset disentangles the falsely-coupled segment and entity annotations that arises from the block-level annotation of FUNSD.

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Chong Zhang, Yi Tu, Yixi Zhao, Chenshu Yuan, Huan Chen, Yue Zhang, Mingxu Chai, Ya Guo, Huijia Zhu, Qi Zhang, Tao Gui

Clues Before Answers: Generation-Enhanced Multiple-Choice QA (2022.naacl-main)

Challenge:	Existing models of layout reading order do not convey the complete reading order information in the layout.
Approach:	They propose to model layout reading order as ordering relations over layout elements . they propose a reading-order-relation-enhancing pipeline to improve model performance .
Outcome:	The proposed model outperforms existing models on a visual-rich document dataset and on eight cross-domain VrD-IE/QA tasks without targeted optimization.

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Zixian Huang, Ao Wu, Jiaying Zhou, Yu Gu, Yue Zhao, Gong Cheng

UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces (2025.acl-long)

Challenge:	Multiple-choice question answering (MCQA) uses text-to-text framework . but, there is an under-utilization of the decoder and knowledge that can be decoded .
Approach:	They propose a generative multiple-choice question answering model which generates a clue from the question and leverages it to enhance a reader for MCQA.
Outcome:	The proposed model outperforms text-to-text models on multiple MCQA datasets.

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Baining Zhao, Jianjie Fang, Zichao Dai, Ziyou Wang, Jirong Zha, Weichen Zhang, Chen Gao, Yue Wang, Jinqiang Cui, Xinlei Chen, Yong Li

STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion (2023.emnlp-main)

Challenge:	Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban aerial spaces remain to be explored.
Approach:	They propose a benchmark to evaluate whether large multimodal models can process continuous first-person visual observations like humans.
Outcome:	The proposed model can process first-person visual observations like humans, enabling recall, perception, reasoning, and navigation.

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Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, Xiaozhong Liu

Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models (2025.acl-long)

Challenge:	Commercial news provides rich semantics and timely information for automated financial risk detection.
Approach:	They propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph to endorse the risk detection enhancement.
Outcome:	The proposed model outperforms existing models in terms of generalization and semantics and annotation.

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Zixiang Xu, Yanbo Wang, Yue Huang, Xiuying Chen, Jieyu Zhao, Meng Jiang, Xiangliang Zhang

LLM Factoscope: Uncovering LLMs’ Factual Discernment through Measuring Inner States (2024.findings-acl)

Challenge:	Large language models have achieved remarkable success in Natural Language Processing, yet their cross-lingual consistency remains a significant challenge.
Approach:	They propose a method to identify cross-lingual weaknesses in Large Language Models . they construct bilingual question pairs that expose performance discrepancies between English and target languages .
Outcome:	The proposed method uncovers over 50% accuracy drops in target languages across models.

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Jinwen He, Yujia Gong, Zijin Lin, Cheng’an Wei, Yue Zhao, Kai Chen

Challenge:	Large Language Models (LLMs) produce outputs that deviate from factual reality, especially in sensitive applications such as medical consultation and legal advice.
Approach:	They propose a Siamese network-based model that leverages LLMs’ inner states for factual detection.
Outcome:	The proposed model achieves over 96% accuracy on a custom-collected factual detection dataset.

NLP-ADBench: NLP Anomaly Detection Benchmark (2025.findings-emnlp)

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Yuangang Li, Jiaqi Li, Zhuo Xiao, Tiankai Yang, Yi Nian, Xiyang Hu, Yue Zhao

It’s All About In-Context Learning! Teaching Extremely Low-Resource Languages to LLMs (2025.emnlp-main)

Challenge:	Anomaly detection (AD) is an important machine learning task, but its effectiveness in detecting harmful content, phishing attempts, and spam reviews is limited.
Approach:	They introduce NLP-ADBench, the most comprehensive NLP anomaly detection benchmark to date . it includes eight curated datasets and 19 state-of-the-art algorithms .
Outcome:	The NLP-ADBench benchmark includes 19 state-of-the-art methods and 8 curated datasets . no single model dominates across all datasets, indicating need for automated model selection .

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Yue Li, Zhixue Zhao, Carolina Scarton

Low-Resource NLP

Challenge:	Low-resource languages, especially those written in rare scripts, remain unsupported by large language models due to lack of training data.
Approach:	They evaluate 20 under-represented languages across three state-of-the-art multilingual LLMs and compare their methods to parameter-efficient fine-tuning.
Outcome:	The proposed methods compare with parameter-efficient fine-tuning (PEFT) on low-resource languages.

Label Set Optimization via Activation Distribution Kurtosis for Zero-Shot Classification with Generative Models (2025.emnlp-main)

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Yue Li, Zhixue Zhao, Carolina Scarton

Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models (2025.findings-acl)

Challenge:	In-context learning (ICL) performance is highly sensitive to prompt design, yet the impact of class label options (e.g. lexicon or order) in zero-shot classification remains underexplored.
Approach:	They propose a post-hoc method for selecting optimal label sets in zero-shot ICL with large language models.
Outcome:	The proposed method consistently achieves performance gains of 0.54 to 0.76 compared to the conventional method.

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Qianqi Yan, Yue Fan, Hongquan Li, Shan Jiang, Yang Zhao, Xinze Guan, Ching-Chen Kuo, Xin Eric Wang

Self-Training with Differentiable Teacher (2022.findings-naacl)

Challenge:	Existing Multimodal Large Language Models (MLLMs) are predominantly trained on consistent visual-textual inputs, leaving open the question of whether they can handle semantic mismatches in layout-rich content.
Approach:	They propose to use multimodal inconsistency reasoning to assess MLLMs' ability to reason about semantic mismatches in webpages, presentation slides, and posters.
Outcome:	The proposed model outperforms open-source models in detecting inconsistencies in webpages, presentation slides, and posters while remaining vulnerable to inconsistent errors.

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Simiao Zuo, Yue Yu, Chen Liang, Haoming Jiang, Siawpeng Er, Chao Zhang, Tuo Zhao, Hongyuan Zha

Treble Counterfactual VLMs: A Causal Approach to Hallucination (2025.findings-emnlp)

Challenge:	Existing methods for self-training are interpreted as teacher-student frameworks, where the teacher generates pseudo-labels and the student makes predictions.
Approach:	They propose a differentiable self-training method that treats teacher-student as a Stackelberg game where a leader is always in a more advantageous position than a follower.
Outcome:	The proposed model outperforms existing methods on semi- and weakly-supervised learning tasks on semi and weak supervised tasks.

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Li Li, Jiashu Qu, Linxin Song, Yuxiao Zhou, Yuehan Qin, Tiankai Yang, Yue Zhao

H-LegalKI: A Hierarchical Legal Knowledge Integration Framework for Legal Community Question Answering (2024.findings-emnlp)

Challenge:	Existing studies link hallucination to data or representation biases, but their causal origins remain unclear.
Approach:	They propose a causal framework to analyze and mitigate hallucination in vision-language models by using counterfactual analysis to estimate the Natural Direct Effect (NDE) of each modality and their interaction.
Outcome:	The proposed framework significantly reduces hallucination while preserving task performance while retaining reliability.

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Yue Jiang, Ziyu Guan, Jie Zhao, Wei Zhao, Jiaqi Yang

CoAct: Co-Active LLM Preference Learning with Human-AI Synergy (2026.acl-long)

Challenge:	Legal question answering (LQA) aims to bridge the gap between limited availability of legal professionals and the extensive volume of legal issues.
Approach:	They propose a legal knowledge retriever and a hierarchical legal knowledge integration framework to address multiple user-specific circumstances.
Outcome:	The proposed framework outperforms baselines on the legal community question-answering dataset.

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Ruiyao Xu, Mihir Parmar, Tiankai Yang, Zhengyu Hu, Yue Zhao, Kaize Ding

Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)

Challenge:	Existing methods to learn from preference-based feedback are expensive and scarce.
Approach:	They propose a framework that synergistically combines self-rewarding and active learning through human-AI collaboration.
Outcome:	The proposed framework outperforms existing methods on three reasoning benchmarks and achieves average improvements of +13.25% on GSM8K, +8.19% on MATH, and +13.16% on WebInstruct.

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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng

DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search (2026.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge.
Approach:	They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy.
Outcome:	The proposed model protects private data while enhancing the model's knowledge.

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Zhibang Yang, Xinke Jiang, Rihong Qiu, Ruiqing Li, Yihang Zhang, Yue Fang, Yongxin Xu, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang

Information Retrieval

Challenge:	Existing methods for ambiguous queries struggle to retrieve high-quality documents . DFAMS outperforms advanced FR methods by 14.37% in knowledge classification accuracy .
Approach:	They propose a framework that leverages dynamic information flow to identify latent query intents and construct semantically aligned knowledge partitions for accurate retrieval across heterogeneous sources.
Outcome:	The proposed framework outperforms existing methods in classification accuracy and retrieval recall tests.

Curriculum Learning based Hierarchical Scoring and Analysis Framework for Question Answering Task Evaluation (2026.findings-acl)

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Qiong Wu, Tan Yue, Jianxin Liang, Zhen Li, Kai He, Shuai Zhao, Dongyan Zhao

Question Answering

Challenge:	Existing evaluation methods rely on rule-based matching with shallow semantic understanding or adopt LLM-as-a-Judge approaches that incur high cost and latency while offering limited error interpretability.
Approach:	They propose a curriculum learning based hierarchical framework for QA task evaluation that supports quick scoring and fine-grained error analysis.
Outcome:	The proposed framework outperforms baseline methods on quick scoring and error analysis tasks while being 25 faster.

SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer (2024.acl-long)

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Jie Zhao, Ziyu Guan, Cai Xu, Wei Zhao, Yue Jiang

Text Generation

Challenge:	Existing methods for short TST are difficult to implement and can cause content degradation.
Approach:	They propose a method to vary the style polarity of text while preserving semantic content.
Outcome:	The proposed method improves over baselines and is highly efficient.

Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings (2022.acl-long)

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Jiangbin Zheng, Yile Wang, Ge Wang, Jun Xia, Yufei Huang, Guojiang Zhao, Yue Zhang, Stan Li

Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph (2023.findings-acl)

Challenge:	Contextualized embeddings are expensive and resource-demanding, hence environmentally unfriendly.
Approach:	They propose a method to convert contextualized embeddings from pre-trained models into static embeddables using synonym knowledge and weighted vector distribution.
Outcome:	The proposed method outperforms baseline embeddings by a large margin through extrinsic and intrinsic tasks.

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Yue Tan, Bo Wang, Anqi Liu, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

UNIVID: Unified Vision-Language Model for Video Moderation (2026.acl-industry)

Challenge:	Existing knowledge graphs are incomplete in tracking complex semantic relations of the target-oriented dialogue.
Approach:	They combine methods of knowledge retrieval and relationship prediction to construct a context-related dynamic KG and a metric to evaluate the tracked path automatically.
Outcome:	The proposed method can control the agent more logically and smoothly toward the complex target.

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Kejuan Yang, Yizhuo Zhang, Mingyuan Du, Yue Zhang, Dixin Zheng, Kaili Zhao, Yang Xiao, Hanzhong Liang, Kenan Xiao

Self-EmoQ: Plutchik-Guided Value-based Planning to Drive Streaming Emotional TTS (2026.findings-acl)

Challenge:	Existing video moderation systems rely on fragmented black-box classification models that are difficult to maintain and lack transparency.
Approach:	They propose a Unified Vision-Language model for Video Moderation that generates policy-aware captions that serve as an interpretable intermediate representation.
Outcome:	The proposed model reduces violation leakage and overkill rate by 42.7% while reducing maintenance costs.

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Yue Zhao, Hongyan Li, Yong Chen, Luo Ji

Defenses Against Prompt Attacks Learn Surface Heuristics (2026.acl-long)

Challenge:	Existing systems lack a self-emotion determination mechanism to drive the streaming text-to-speech (TTS) synthesis.
Approach:	They propose an emotion-planning framework that determines the emotion prior to the textual generation, grounding the downstream emotional TTS in a streaming manner.
Outcome:	The proposed framework outperforms baselines on DailyDialog, EmoryNLP, IMEOCAP, and MELD on emotional alignment, contextual coherence, and expressive fluency.

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Li Li, Chenxiao Yu, Zhiyu Ni, Hao Li, Charith Peris, Chaowei Xiao, Yue Zhao

ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs (2026.acl-long)

Challenge:	Large language models (LLMs) are increasingly deployed in security-sensitive applications . recent defenses rely on supervised fine-tuning with benign and malicious labels . position bias arises when benign content placed later in a prompt is rejected at much higher rates .
Approach:	They analyze three recurring shortcut behaviors induced by supervised fine-tuning . position bias arises when benign content placed later in a prompt is rejected . token trigger bias occurs when strings common in attack data raise rejection probability .
Outcome:	The proposed model overrides intended logic when adversarial instructions appear . the proposed model has low rejection rates but narrow correlations in defense data .

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Hongxin Ding, Baixiang Huang, Yue Fang, Weibin Liao, Xinke Jiang, Jinyang Zhang, Yinghao Zhu, Zheng Li, Liantao Ma, Junfeng Zhao, Yasha Wang

Challenge:	Existing medical Large Language Models (LLMs) follow a reactive paradigm, risking diagnostic errors by answering before seeking sufficient details.
Approach:	They propose a reinforcement learning framework that transitions LLMs toward a proactive paradigm, enabling them to ask clinically valuable questions before decision-making.
Outcome:	Experiments on partial-information medical benchmarks show that ProMed outperforms state-of-the-art methods by 6.29% on average and delivers a 54.45% gain over the reactive paradigm.

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

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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

Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training (2026.acl-long)

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

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Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu, Jingxuan Xing, Yue Cao, Zekun Zhu, Wei Jiang, Ziming Wang, Yingxiu Zhao, Ming-Liang Zhang, Jun Song, Yuning Jiang, Bo Zheng

Multimodal Machine Translation with Text-Image In-depth Questioning (2025.findings-acl)

Challenge:	Existing approaches to training agents for visual-language models trap them in local optima, hindering exploration and error correction with the environment.
Approach:	They propose a hierarchical training recipe that bridges atomic action execution and strategic task completion.
Outcome:	The proposed training recipe bridges atomic action execution and strategic task completion.

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Yue Gao, Jing Zhao, Shiliang Sun, Xiaosong Qiao, Tengfei Song, Hao Yang