Papers by Guan Wang

AIDA-SEAT: Towards Reliable AI Doctor Assistant via State-Evaluation-Action Tree Enhanced LLMs in Online Hospital (2026.acl-industry)

Challenge:	Existing approaches to multimodal representation learning focus on directional alignment and embedding magnitudes (L2-norm) however, these methods often fail to account for the intrinsic role of L2-norm in the contrastive process.
Approach:	They propose a plug-and-play framework that optimizes L2-norm alignment and Directional consistency jointly.
Outcome:	The proposed framework achieves consistent and significant performance gains over established baselines across 95 tasks using UniIR and VLM2Vec-V2 frameworks.

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Lianxin Sun, Xiaoying Ying, Guangya Yu, Weiyan Zhang, Chenhao Guan, Hao He, Mingxi Shang, Jianhua Li, ChunMing Wang, Tong Ruan

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs (2021.acl-long)

Challenge:	Existing systems rely on large language models or retrieval-augmented generation (RAG) but these methods lack the explicit logical pathways essential for multi-step reasoning.
Approach:	They propose an AIDA-SEAT framework to provide reliable clinical decision-making support by transforming and modifying medical documents and doctors' state-evaluation-action trees.
Outcome:	The proposed framework achieves 1.01% higher than current state-of-the-art (SOTA) baselines across five departments, including common RAG-based methods.

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Zixuan Li, Xiaolong Jin, Saiping Guan, Wei Li, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng

FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization (2025.findings-emnlp)

Challenge:	Temporal Knowledge Graphs (TKGs) are used in many different areas of research.
Approach:	They propose to use a beam search policy to induce multiple clues from historical facts . they propose to adopt a graph convolution network based sequence method to deduce answers from clues .
Outcome:	The proposed model can predict future facts in two stages, Clue Searching and Temporal Reasoning.

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Fangxin Liu, Zongwu Wang, Jinhong Xia, Junping Zhao, Shouren Zhao, Jinjin Li, Jian Liu, Li Jiang, Haibing Guan

BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain (2024.findings-emnlp)

Challenge:	Existing methods for quantization of large language models struggle to adapt to dynamic workloads.
Approach:	a new framework optimizes the trade-off between inference speed and accuracy . FlexQuant enables fine-grained, layer-wise mixed-precision quantization .
Outcome:	a new framework optimizes the trade-off between inference speed and accuracy . it achieves a 1.3 speedup across diverse language tasks with negligible accuracy loss .

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Kaisi Guan, Qian Cao, Yuchong Sun, Xiting Wang, Ruihua Song

Resonating with RoPE: Spectral Quantization for High-Fidelity Key Cache Compression (2026.acl-long)

Challenge:	Existing work adopts separate modules for retrieval and generation, which may be suboptimal since the retrieval task and generation task cannot benefit from each other to improve performance.
Approach:	They propose a backbone-shared RAG framework that uses a domain-specific corpus to continuously pre-train a model and then trains two plug-and-play Low-Rank Adaptation modules based on the shared backbone to minimize retrieval and generation losses respectively.
Outcome:	The proposed framework outperforms baseline models by 5% and 13% in Hit@3 upon two datasets in retrieval evaluation and by 23% in terms of BLEU-3 in generation evaluation.

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Xuefei Wang, Haoyu Tang, Tianyuan Liang, Zhibin Wang, Yupeng Hu, Weili Guan

Challenge:	Existing approaches to reduce memory footprint of long-context LLMs rely on RoPE-induced oscillations.
Approach:	They propose a frequency-domain framework that converts RoPE-induced oscillations into sparse spectral representations.
Outcome:	The proposed framework achieves efficient compression with performance comparable to FP16 benchmarks.

Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review (2025.findings-acl)

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Pei Fu, Tongkun Guan, Zining Wang, Zhentao Guo, Chen Duan, Hao Sun, Boming Chen, Qianyi Jiang, Jiayao Ma, Kai Zhou, Junfeng Luo

MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)

Challenge:	Recent advances in vision-language models have unified perception and understanding tasks within Visual Question Answering paradigms.
Approach:	They propose to outline timeline, architecture, and pipeline of nearly all TIU MLLMs and review their performance on mainstream benchmarks.
Outcome:	The proposed models perform well on mainstream benchmarks and are compared with other models.

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Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li

Information Extraction & Text Mining

Challenge:	Existing datasets for event understanding have limited coverage due to complexity of tasks.
Approach:	They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation .
Outcome:	The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction.

HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models (2026.findings-acl)

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Jizhihui Liu, Guangdao Zhu, Feiyi Du, Niu Lian, Jun Li, Bin Chen, Weili Guan, Yaowei Wang

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

Challenge:	Existing methods for visual token pruning lack insight into the intrinsic property of the vision encoder . et al., 2017: 99.3% of task accuracy with only 1/3 of the tokens.
Approach:	They propose a model-agnostic token pruning method that trains without training . they propose 'HiPrune' method which prunes visual tokens according to their attention .
Outcome:	The proposed method achieves 99.3% of task accuracy with only 1/3 of the tokens . it reduces inference FLOPs by 58.7% and maintains 99.99% accuracy with 2/9 tokens.

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

More Thinking, Less Talking: Internalizing Deliberative Safety into LLM Parameters (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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Guan Wang, Xuehai Tang, Biyu Zhou, Jizhong Han, Songlin Hu

Robustness in NLP

Challenge:	Existing safety alignment methods leave Large Language Models vulnerable to sophisticated jailbreak attacks.
Approach:	They propose a safety reasoning internalization framework that internalizes safety reasoning into an implicit computational pathway using Low-Rank Adaptation (LoRA).
Outcome:	The proposed framework achieves a 43% lower Attack Success Rate (ASR) against distinct jailbreak attacks compared to strong baselines.

AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models (2024.findings-emnlp)

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Xiyang Wu, Tianrui Guan, Dianqi Li, Shuaiyi Huang, Xiaoyu Liu, Xijun Wang, Ruiqi Xian, Abhinav Shrivastava, Furong Huang, Jordan Boyd-Graber, Tianyi Zhou, Dinesh Manocha

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

Challenge:	Large vision-language models are prone to hallucinations, where contextual cues in an image can trigger the language module to produce overconfident and incorrect reasoning about abnormal or hypothetical objects.
Approach:	They propose to automate the generation of hallucination-related questions using images . they propose to use three image manipulation strategies to induce hallucinosity .
Outcome:	The proposed approach reduces human bias in crafting such examples and improves accuracy.

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

Medical Entity Disambiguation with Medical Mention Relation and Fine-grained Entity Knowledge (2024.lrec-main)

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

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Wenpeng Lu, Guobiao Zhang, Xueping Peng, Hongjiao Guan, Shoujin Wang

Challenge:	Existing methods for medical entity disambiguation (MED) fail to fully utilize the knowledge within medical knowledge bases (KBs) Existing models overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguations performance.
Approach:	They propose to combine a mention relation fusion module and an entity knowledge fusion modules to map medical mentions to corresponding entities in a knowledge base (KB)
Outcome:	The proposed method outperforms state-of-the-art MED models on two publicly available real-world datasets.

JointCoder: Exploring Automated ICD Coding on Real-World Chinese EHRs with a Multi-Agent Framework (2026.acl-demo)

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Kangjun Liu, Zhenyu Li, Jianlei Wang, Hongjiao Guan, Ying Lian, Guoqiang Chen, Tao Xin, Wenpeng Lu

Challenge:	Existing automated ICD coding systems face several fundamental challenges due to the limited availability of publicly available Chinese ICD datasets.
Approach:	They propose to use a Chinese ICD coding dataset and a multi-agent framework to reformulate ICD as a joint disease-procedure coding task.
Outcome:	The proposed system outperforms state-of-the-art methods on real-world Chinese ICD coding datasets and 1.7B-parameter models.

Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference (2026.acl-long)

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null Zhouxuwen, Fangxin Liu, Chao Wang, Xiao Zheng, Hao Zheng, Min He, Li Jiang, Haibing Guan

RLKGF: Reinforcement Learning from Knowledge Graph Feedback Without Human Annotations (2025.findings-acl)

Challenge:	Speculative decoding (SD) is a powerful and efficient way to accelerate autoregressive generation.
Approach:	They propose a training-free framework that recovers valid tokens discarded by standard verification . they use online correction memory and Semantic Consistency Gating to analyze rejections .
Outcome:	The proposed framework outperforms existing methods and achieves peak throughput speedup of 2.33x.

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Lian Yan, Chen Tang, Yi Guan, Haotian Wang, Songyuan Wang, Haifeng Liu, Yang Yang, Jingchi Jiang

Structured Data in NLP

Challenge:	Lack of human preference labels remains a significant bottleneck when applying RLHF to a downstream domain.
Approach:	They propose a method that leverages human priors encoded in Knowledge Graphs (KGs) to derive RL rewards in the absence of manual annotations.
Outcome:	Experiments on three public and one private medical dialogue datasets show that the proposed method outperforms the competitive RLAIF in improving LLM diagnostic accuracy.

NeuInfer: Knowledge Inference on N-ary Facts (2020.acl-main)

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Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng

Knowledge Representation

Challenge:	Existing studies on knowledge inference on binary facts have focused on finding out connotative valid facts.
Approach:	They propose a neural network model, NeuInfer, for knowledge inference on n-ary facts.
Outcome:	The proposed model can cope with the task to infer an unknown element in a whole fact, while ignoring the binary facts.

Beyond Single Frames: Can LMMs Comprehend Implicit Narratives in Comic Strip? (2025.findings-emnlp)

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Xiaochen Wang, Heming Xia, Jialin Song, Longyu Guan, Qingxiu Dong, Rui Li, Yixin Yang, Yifan Pu, Weiyao Luo, Yiru Wang, Xiangdi Meng, Wenjie Li, Zhifang Sui

RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents (2026.acl-long)

Challenge:	Large Multimodal Models have demonstrated strong performance on vision-language benchmarks, yet current evaluations focus on single-image reasoning.
Approach:	STRIPCIPHER is a benchmark designed to evaluate model ability on understanding implicit narratives in silent comics.
Outcome:	STRIPCIPHER is a high-quality, human-annotated dataset featuring fine-grained annotations and comprehensive coverage of varying difficulty levels.

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Jize Wang, Han Wu, Zhiyuan You, Yiming Song, Yijun Wang, Zifei Shan, Yining Li, Songyang Zhang, Xinyi Le, Cailian Chen, Xinping Guan, Dacheng Tao

RA2FD: Distilling Faithfulness into Efficient Dialogue Systems (2024.emnlp-main)

Challenge:	Existing methods for mixing-of-agents (MoA) lack model selection criteria and struggle with large model pools.
Approach:	They propose a mixture-of-agents framework with dynamic routing that uses a lightweight scorer to perform initial screening and refines the model scores through self- and cross-assessment.
Outcome:	The proposed framework outperforms existing methods for large model pools and tasks . it reduces cost by 89.8% and latency by 63.6% in the large-scale model pool.

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Zhiyuan Zhu, Yusheng Liao, Chenxin Xu, Yunfeng Guan, Yanfeng Wang, Yu Wang

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

Challenge:	Retrieval Augmented Generation (RAG) is effective but inference inefficient, while Retrieral Free Generations (RFG) are more efficient but sacrifice faithfulness.
Approach:	They propose a retrieval-free model training scheme that uses a teacher-student framework to distill the faithfulness capacity of a student's knowledge-infused responses.
Outcome:	The proposed model surpasses the previous SOTA RFG model on knowledge-grounded dialogue datasets by an average of 33% while improving inference efficiency.

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Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong

Low-Resource NLP

Challenge:	Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity.
Approach:	They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels.
Outcome:	The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency.

MimicLM: Zero-Shot Voice Imitation through Autoregressive Modeling of Pseudo-Parallel Speech Corpora (2026.findings-acl)

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Tao Feng, Yuxiang Wang, Yuancheng Wang, Xueyao Zhang, Dekun Chen, Chaoren Wang, Xun Guan, Zhizheng Wu

Agri-CM3: A Chinese Massive Multi-modal, Multi-level Benchmark for Agricultural Understanding and Reasoning (2025.acl-long)

Challenge:	Existing approaches to voice imitation use complex model design and a quality ceiling when synthetic speech is used as training sources.
Approach:	They propose a model that uses synthetic speech as training sources while retaining real recordings as targets.
Outcome:	The proposed model outperforms existing methods in naturalness while maintaining competitive similarity scores across speaker identity, accent, and emotion dimensions.

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Haotian Wang, Yi Guan, Fanshu Meng, Chao Zhao, Lian Yan, Yang Yang, Jingchi Jiang

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)

Challenge:	Existing benchmarks lack comprehensive evaluations, particularly in multi-level reasoning, making it difficult to identify model limitations.
Approach:	They propose to use Agri-CM3 to assess multi-level reasoning in agricultural management by integrating multiple data modalities.
Outcome:	The Agri-CM3 benchmark includes 3,939 images and 15,901 multi-level multiple-choice questions with detailed explanations.

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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang

CogBench: Benchmarking Cognitive Alignment of Large Language Models in Educational Question Answering (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability.
Approach:	They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality.
Outcome:	The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks.

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Tong Lu, Zhichun Wang, Yuanhao Sun, Yaoyu Zhou, Mingrui Li, Yiming Guan, Zhiyong Bai

Question Answering

Challenge:	Large language models (LLMs) possess strong capabilities in language understanding and generation, as well as remarkable problem-solving abilities.
Approach:	They propose a benchmark to assess the cognitive alignment capabilities of large language models in educational QA.
Outcome:	The proposed evaluation benchmark assesses the cognitive alignment capabilities of large language models in educational QA.

From Coarse to Fine: A Multi-Granularity Multimodal Framework for Teacher Sentiment Analysis (2026.findings-acl)

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Zhiyi Duan, Xiangren Wang, Jiangshan Guan, Bing Jia, Qianli Xing

When Language Model Meets Private Library (2022.findings-emnlp)

Challenge:	Existing approaches to teacher sentiment analysis treat it as a static label . current approaches fail to capture structured heterogeneity of classroom expressions .
Approach:	They propose a coarse-to-fine multimodal framework that decomposes teacher sentiment into three granularities and employ CLS-guided cross-modal attention to recover effective signals from regulated displays.
Outcome:	The proposed framework outperforms state-of-the-art models on T-MED and CMU-MOSEI.

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Daoguang Zan, Bei Chen, Zeqi Lin, Bei Guan, Wang Yongji, Jian-Guang Lou

BloomEval: A Bloom’s Cognitive Taxonomy-Based Benchmark for Evaluating LRMs via Cognitive Hierarchy Trace (2026.findings-acl)

Challenge:	Existing language models have been pre-trained on large-scale code corpora and generate decent code snippets.
Approach:	They propose a framework that can provide pre-trained language models with the ability to generate code using private libraries.
Outcome:	The proposed framework can generate code using private libraries using off-the-shelf language models or pre-trained models on code corpus containing API information.

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Zhiyi Duan, Lei Gao, Jiangshan Guan, Qi Wang, Rui Liu

When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models (2026.findings-acl)

Challenge:	Existing benchmarks for Large Reasoning Models rely on answer correctness, but fail to assess the structural coherence and cognitive soundness of the reasoning process itself.
Approach:	They propose a framework that maps a model's reasoning trajectory onto hierarchical cognitive levels and an annotation pipeline to ensure a scalable yet reliable annotation pipeline.
Outcome:	The proposed framework detects hierarchy jumps, breaks, and overthinking errors and enables scalable yet reliable annotation.

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Yingzhi Mao, Chunkang Zhang, Junxiang Wang, Xinyan Guan, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback (2024.findings-acl)

Challenge:	Existing methods often apply coarse-grained constraints over entire reasoning trajectories . Existing approaches often apply unsafe constraints, causing unsafe outputs .
Approach:	They propose a trajectory-level training framework that mitigates Self-Jailbreak . they propose 'chain-of-guardrail' to mitigate self-jailbreak by targeting step-level interventions .
Outcome:	The proposed framework mitigates Self-Jailbreak by targeting step-level interventions while maintaining reasoning ability.

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Zhangqian Bi, Yao Wan, Zheng Wang, Hongyu Zhang, Batu Guan, Fangxin Lu, Zili Zhang, Yulei Sui, Hai Jin, Xuanhua Shi

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) generate code for given contexts, such as incomplete code, class, data structure, or project-specific information.
Approach:	They propose a compiler feedback-based code generation approach that leverages static analysis to identify mismatches between the generated code and the project's context.
Outcome:	The proposed model outperforms retrieval-based code generation baselines and significantly outperfies the existing large language models.

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)

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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych

Structured Data in NLP

Challenge:	a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications .
Approach:	a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19.
Outcome:	a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing .

PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models (2025.acl-long)

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Jiaqi Zhao, Miao Zhang, Ming Wang, Yuzhang Shang, Kaihao Zhang, Weili Guan, Yaowei Wang, Min Zhang

Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits (2025.coling-main)

Challenge:	Existing methods for sub 2-bit quantization introduce an extra 1-bit or more per weight.
Approach:	They propose a sub 2-bit post-training quantization method that enables weight quantization to 1.61-bit for the first time.
Outcome:	The proposed method reduces the upper bound of quantization error to 1.61-bit for the first time.

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Bohan Li, Jiannan Guan, Longxu Dou, Yunlong Feng, Dingzirui Wang, Yang Xu, Enbo Wang, Qiguang Chen, Bichen Wang, Xiao Xu, Yimeng Zhang, Libo Qin, Yanyan Zhao, Qingfu Zhu, Wanxiang Che

TacoERE: Cluster-aware Compression for Event Relation Extraction (2024.lrec-main)

Challenge:	Existing data on MBTI personality detection are based on self-reported labels and fail to capture the full range of population personality traits.
Approach:	They construct a manually annotated MBTI personality detection dataset with soft labels under the guidance of psychologists and use them to identify the task.
Outcome:	The MBTIBench is the first manually annotated MBti personality detection dataset with soft labels under the guidance of psychologists.

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Yong Guan, Xiaozhi Wang, Lei Hou, Juanzi Li, Jeff Z. Pan, Jiaoyan Chen, Freddy Lecue

Information Extraction & Text Mining

Challenge:	Existing work on event relation extraction focuses on modeling the entire document . existing methods cannot handle long-range dependencies and information redundancy .
Approach:	They propose a compression-then-extraction paradigm for event relation extraction . they propose document clustering for modeling event dependencies and then a cluster summarization method .
Outcome:	The proposed method simplifies and highlights important text content of clusters for mitigating redundancy and event distance.

Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models (2025.naacl-short)

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Sangmin Woo, Kang Zhou, Yun Zhou, Shuai Wang, Sheng Guan, Haibo Ding, Lin Lee Cheong

Large Language Models Meet NL2Code: A Survey (2023.acl-long)

Challenge:	Existing methods to mitigate object hallucination are impractical for proprietary LVLMs.
Approach:	They propose a framework to identify optimal visual prompts that enhance LVLM responses without access to model internals.
Outcome:	The proposed approach is model-agnostic and can be used on open-source and proprietary LVLMs.

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Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, Jian-Guang Lou

Gamma-Guard: Lightweight Residual Adapters for Robust Guardrails in Large Language Models (2025.emnlp-main)

Challenge:	generating code from a natural language description is a pressing and significant challenge in code intelligence.
Approach:	They propose to survey 27 existing large language models for NL2Code and compare them to humanEval benchmarks.
Outcome:	The proposed model is compared with existing models on the HumanEval benchmark.

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Lijia Lv, Yuanshu Zhao, Guan Wang, Xuehai Tang, Wen Jie, Jizhong Han, Songlin Hu

Crafting Adversarial Inputs for Large Vision-Language Models Using Black-Box Optimization (2026.findings-eacl)

Challenge:	Large language models (LLMs) are widely deployed as zero-shot evaluators for answer grading, content moderation, and document ranking.
Approach:	They propose a system that trains LLMs with adapters to denoise embeddings and refocus attention.
Outcome:	The proposed model lifts adversarial accuracy from 5% to 95% a 90 percentage-point gain while reducing clean-data accuracy by just 8 percentage points.

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Jiwei Guan, Haibo Jin, Haohan Wang

Robustness in NLP

Challenge:	Existing white-box jailbreak methods require full model accessibility and require computational costs.
Approach:	They propose a black-box jailbreak attack using Zeroth-Order optimization using ZO-SPSA.
Outcome:	The proposed method achieves highest jailbreak success rate on three LVLMs, including InstructBLIP, LLaVA and MiniGPT-4.

EvolveBench: A Comprehensive Benchmark for Assessing Temporal Awareness in LLMs on Evolving Knowledge (2025.acl-long)

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Zhiyuan Zhu, Yusheng Liao, Zhe Chen, Yuhao Wang, Yunfeng Guan, Yanfeng Wang, Yu Wang

Challenge:	Existing studies have explored how LLMs perceive time, but they often overlook the critical aspect of knowledge utilization.
Approach:	They propose a benchmark that evaluates temporal competence along five key dimensions: Cognition, Awareness, Trustworthiness and reasoning.
Outcome:	EvolveBench measures temporal competence along five key dimensions: Cognition, Awareness, Trustworthiness, Understanding and reasoning.

GRAPHIA: Harnessing Social Graph Data to Enhance LLM-Based Social Simulation (2026.acl-long)

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Jiarui Ji, Zehua Zhang, Zhewei Wei, Bin Tong, Guan Wang, Bo Zheng

UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large Models (2025.acl-long)

Challenge:	Social graphs provide high-quality supervision signals that encode local interactions and global network structure, yet they remain underutilized for LLM training.
Approach:	They propose a general LLM-based social graph simulation framework that leverages graph data as supervision for LLM training.
Outcome:	The proposed framework improves micro-level alignment by 6.1% on three real-world networks compared to the strongest baseline.

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Xueyan Zhang, Jinman Zhao, Zhifei Yang, Yibo Zhong, Shuhao Guan, Linbo Cao, Yining Wang

Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer (2025.findings-acl)

Challenge:	Existing methods such as LoRA and VeRA use a low-rank approximation method that reduces the number of trainable parameters without compromising performance.
Approach:	They propose a parameter-efficient fine-tuning approach that leverages a low-rank approximation method that reduces the number of trainable parameters without compromising performance.
Outcome:	The proposed approach outperforms existing methods on GLUE and E2E benchmarks and is effective in instruction-tuning large language models and image classification models.

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Long Chen, Shuoyu Guan, Xiaohua Huang, Wen-Jing Wang, Cai Xu, Ziyu Guan, Wei Zhao

Low-Resource NLP

Challenge:	Existing multimodal sentiment analysis methods are limited to textual data and cannot handle multimodal scenarios.
Approach:	They propose a transfer learning framework that allows cross-lingual and cross-modal alignments and a language family disentanglement module that enhances the sharing of language universals within families.
Outcome:	The proposed method is superior to existing methods and can handle low-resource languages.

CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code (2024.findings-emnlp)

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Batu Guan, Yao Wan, Zhangqian Bi, Zheng Wang, Hongyu Zhang, Pan Zhou, Lichao Sun

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) have shown significant potential in code generation, but they also present challenges regarding the protection of Intellectual Property (IP) related to model architectures, weights, and training data.
Approach:	They propose a multi-bit watermarking technique that embeds additional information to preserve provenance details, such as the vendor ID of an LLM.
Outcome:	The proposed technique preserves provenance details while maintaining syntactical correctness of generated code.

Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs (2025.coling-main)

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Dingjie Song, Wenjun Wang, Shunian Chen, Xidong Wang, Michael X. Guan, Benyou Wang

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

Challenge:	Recent advances in Multimodal Large Language Models have led to a significant surge in the resource consumption of these models.
Approach:	They propose a method to reduce image tokens using visual query data by using CLIP metrics to reduce computational overhead and maintain consistent performance.
Outcome:	The proposed method has been extensively tested across 12 datasets and shows a significant reduction in computational overhead while maintaining a consistent level of performance.

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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.

AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)

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Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, Xianpei Han

Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)

Challenge:	Automated Alignment (ALM) is a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention.
Approach:	They propose an open-source toolkit that integrates mainstream automated algorithms through a consistent interface and an accessible workflow supporting one-click execution for prompt synthesis and automatic alignment signal construction.
Outcome:	The proposed framework enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components.

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Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang

EVIDENCEMINER: Textual Evidence Discovery for Life Sciences (2020.acl-demos)

Challenge:	Existing studies focus on fine-tuning multilingual dense retrieval models, but data scarcity for low-resource languages makes it difficult to align representations in a shared vector space.
Approach:	They propose to obtain high-quality hard negative samples and effective mini-batch data to boost data utilization for multilingual dense retrieval by obtaining high-quality negative samples.
Outcome:	The proposed method outperforms existing baselines on a multilingual retrieval benchmark, MIRACL, with 16 languages.

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Xuan Wang, Yingjun Guan, Weili Liu, Aabhas Chauhan, Enyi Jiang, Qi Li, David Liem, Dibakar Sigdel, John Caufield, Peipei Ping, Jiawei Han

LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries (2025.acl-srw)

Challenge:	EVIDENCEMINER is a web-based system that allows users to query a natural language statement and retrieve textual evidence from a background corpora for life sciences.
Approach:	They propose a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences.
Outcome:	EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences.

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Zekun Wu, Seonglae Cho, Umar Mohammed, Cristian Enrique Munoz Villalobos, Kleyton Da Costa, Xin Guan, Theo King, Ze Wang, Emre Kazim, Adriano Koshiyama

Challenge:	Open-source AI libraries present significant, underexamined risks spanning security, licensing, maintenance, supply chain integrity, and regulatory compliance.
Approach:	They propose a system that leverages large language models and agentic workflows to perform deep, evidence-based evaluations of open-source AI libraries.
Outcome:	The proposed system covers up to 88% of OpenSSF Scorecard checks and uncovers 19 additional risks per library.

M3TQA: Massively Multilingual Multitask Table Question Answering (2026.findings-acl)

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Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Guan Xiangyuan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, Zhoujun Li

Structured Data in NLP

Challenge:	Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis.
Approach:	They propose a framework for massively multilingual table question answering that includes tables expanded to 97 languages from Chinese and English sources.
Outcome:	Experiments on state-of-the-art LLMs show that synthetically generated training data significantly boosts performance, especially for low-resource languages.

ALYMPICS: LLM Agents Meet Game Theory (2025.coling-main)

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Shaoguang Mao, Yuzhe Cai, Yan Xia, Wenshan Wu, Xun Wang, Fengyi Wang, Qiang Guan, Tao Ge, Furu Wei

Linguistics & Cognitive NLP

Challenge:	Alympics provides a framework for simulating human-like strategic interactions with Large Language Model (LLM) agents.
Approach:	They propose a framework utilizing Large Language Models (LLM) agents for empirical game theory research.
Outcome:	The proposed framework can be used to study human-like strategic interactions with large language model (LLM) agents in a game on the multi-round auction of scarce survival resources.

Resolving the Security-Auditability Dilemma with Auditable Latent Chain-of-Thought Alignment (2026.acl-long)

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Guan Wang, Biyu Zhou, Xuehai Tang, Jizhong Han, Songlin Hu

SAGED: A Holistic Bias-Benchmarking Pipeline for Language Models with Customisable Fairness Calibration (2025.coling-main)

Challenge:	Extensive experiments show that ALCA reduces the success rate of adaptive jailbreak attacks by over 40% compared to strong baselines, while preserving performance.
Approach:	They propose a framework that decouples internal reasoning from external output and allows the model to reconstruct its latent reasoning into human-readable text for supervision under specific guidance.
Outcome:	The proposed framework reduces the success rate of adaptive jailbreak attacks by over 40% compared to baselines while preserving performance.

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Xin Guan, Nate Demchak, Saloni Gupta, Ze Wang, Ediz Ertekin Jr., Adriano Koshiyama, Emre Kazim, Zekun Wu

Improving Role-Oriented Dialogue Summarization with Interaction-Aware Contrastive Learning (2024.lrec-main)

Challenge:	Existing benchmarks for large language models fail to detect bias due to limited scope, contamination, and lack of a fairness baseline.
Approach:	They propose a benchmarking pipeline to detect biases in large language models . they use metrics for max disparity, impact ratio, and bias concentration to analyze disparity .
Outcome:	SAGED(bias) is the first holistic benchmarking pipeline to address biases in large language models.

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Weihong Guan, Shi Feng, Daling Wang, Faliang Huang, Yifei Zhang, Yuan Cui

Hidden in Plain Sight: Reasoning in Underspecified and Misspecified Scenarios for Multimodal LLMs (2025.emnlp-main)

Challenge:	Existing methods for encoding dialogues do not capture interaction information between roles, thus ignore interaction-related key information.
Approach:	They propose a contrastive learning based interaction-aware model for the role-oriented dialogue summarization namely CIAM and use it to train the decoder to learn role-level interaction.
Outcome:	The proposed model captures interaction information between different roles and produces informative summaries on two public datasets.

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

SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL (2026.acl-long)

Challenge:	Multimodal large language models are increasingly deployed in open-ended, real-world environments where inputs are messy, underspecified, and not always trustworthy.
Approach:	They evaluate multimodal large language models in real-world environments where inputs are messy, underspecified, and not always trustworthy.
Outcome:	The proposed models fail to detect hidden issues even when they possess the necessary perceptual and reasoning skills.

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Harper Hua, Zhen Han, Zhengyuan Shen, Meng-Chieh Lee, Sheng Guan, Qi Zhu, Sullam Jeoung, Yueyan Chen, Yunfei Bai, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala

Programming Languages in NLP

Challenge:	Recent large language models (LLMs) have significantly improved Text-to-SQL generation, but a gap remains between AI systems and human experts on challenging benchmarks such as BIRD-Sql.
Approach:	They propose a multi-turn reinforcement learning agentic framework for Text-to-SQL that uses execution feedback to iteratively refine its predictions.
Outcome:	The proposed framework outperforms proprietary systems on 7B and 14B models by **5% on average, underscoring the effectiveness of interactive, agentic workflows for robust Text-to-SQL generation.

When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models (2024.findings-emnlp)

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Weilan Wang, Yu Mao, Tang Dongdong, Du Hongchao, Nan Guan, Chun Jason Xue

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

Challenge:	Large language models (LLMs) exhibit excellent performance in various tasks, but memory requirements present a challenge when deploying on memory-limited devices.
Approach:	They propose a framework to compress LLM after quantization further, achieving about 2.2x compression ratio.
Outcome:	The proposed model can achieve 40% reduction in memory size with negligible loss in accuracy and inference speed.

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

CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks (2026.acl-long)

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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Lingyue Fu, Hao Guan, Bolun Zhang, Haowei Yuan, Yaoming Zhu, Lin Qiu, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Weiwen Liu, Weinan Zhang, Yong Yu

Challenge:	Existing large language models for software engineering rely on coarse-grained pass rates obscuring specific cognitive bottlenecks.
Approach:	They propose a repository-level benchmark that dissects coding capabilities through atomized tasks.
Outcome:	The proposed framework achieves a 78.55% validity yield, surpassing the 31.7% retention rate of SWE-bench-Verified.

Towards Mitigating Modality Bias in Vision-Language Models for Temporal Action Localization (2026.acl-long)

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Jiaqi Li, Guangming Wang, Shuntian Zheng, Minzhe Ni, Xiaoman Lu, Guanghui Ye, Yu Guan

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

Challenge:	Existing vision-language models overemphasize linguistic priors, leading to modality bias.
Approach:	They propose a vision-language aggregation framework that mitigates modality bias in TAL by preserving vision as the dominant signal while adaptively exploiting language only when beneficial.
Outcome:	Experiments on THUMOS14 show that the proposed model outperforms state-of-the-art models by up to 3.2% mAP.

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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou

SOAPTriage: SOAP-Guided Multi-View Clinical Text Modeling Framework for Automated ESI Prediction (2026.acl-long)

Challenge:	a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say .
Approach:	They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible .
Outcome:	The proposed framework achieves state-of-the-art performance among open-source projects.

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Enming Wang, Jianlei Wang, Xueping Peng, Hongjiao Guan, Yinglong Wang, Sibo Wei, Jianbin Guo, Ruifeng Xu, Wenpeng Lu

JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models (2024.findings-emnlp)

Challenge:	Emergency departments rely on the Emergency Severity Index (ESI) to assess patient acuity and prioritize care.
Approach:	They propose a SOAP-guided multi-view clinical text modeling framework for automated ESI prediction based on the SOAP paradigm .
Outcome:	The proposed framework outperforms prompting-based, multi-agent, and encoder-based baselines.

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Ze Wang, Zekun Wu, Xin Guan, Michael Thaler, Adriano Koshiyama, Skylar Lu, Sachin Beepath, Ediz Ertekin, Maria Perez-Ortiz

Sparse Latents Steer Retrieval-Augmented Generation (2025.acl-long)

Challenge:	a framework for benchmarking hierarchical gender hiring bias in Large Language Models (LLMs) is developed to protect vulnerable demographic groups.
Approach:	They propose a framework for benchmarking hierarchical gender hiring bias in Large Language Models for resume scoring.
Outcome:	The proposed framework reveals significant issues of reverse gender hiring bias and overdebiasing in ten state-of-the-art LLMs.

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Chunlei Xin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Xuanang Chen, Xinyan Guan, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun