Papers by Xuan Wang

The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents (2026.acl-long)

Challenge:	Large language models are transforming biomedical discovery by linking molecular patterns with knowledge encoded in text.
Approach:	They propose to map 58 foundation and agentic models developed for single-cell research into eight key analytical tasks.
Outcome:	The proposed models are applied to eight key analytical tasks including annotation, trajectory inference, perturbation modeling, and drug-response prediction.

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Weihao Xuan, Qingcheng Zeng, Heli Qi, Yunze Xiao, Junjue Wang, Naoto Yokoya

Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds (2022.naacl-main)

Challenge:	a fundamental pillar of trustworthiness is calibration, which refers to an agent’s ability to express confidence that reliably reflects its actual performance.
Approach:	They propose a reinforcement learning framework that jointly optimizes task accuracy and calibration, supported by a holistic benchmark of reward designs.
Outcome:	The proposed framework improves calibration across tool types and shows that trained agents achieve superior calibration and exhibit robust generalization from local training environments to noisy web settings and to distinct domains such as mathematical reasoning.

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Yu Zhang, Yu Meng, Xuan Wang, Sheng Wang, Jiawei Han

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science (2025.findings-emnlp)

Challenge:	Existing topic models adopt a fully unsupervised setting and their discovered topics may not reflect user preferences well due to their unsupervised nature.
Approach:	They propose a framework that allows out-of-vocabulary seeds to be used to find latent topics from text corpora.
Outcome:	The proposed framework can find topics that are never seen in the corpus and can benefit from the general knowledge of pre-trained language models.

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An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding

Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023.emnlp-demo)

Challenge:	Large language models (LLMs) have advanced the automation of data science workflows, yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice.
Approach:	They propose a benchmark to evaluate how large language models handle external domain knowledge in tabular prediction tasks.
Outcome:	The proposed model evaluates whether it can critically leverage external domain knowledge as human data scientists do in practice.

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Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, Jiawei Han

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

Challenge:	Reaction Miner is a system designed to extract chemical reactions from raw scientific PDFs.
Approach:	They propose a system that extracts chemical reactions directly from raw scientific PDFs.
Outcome:	The proposed system can extract chemical reactions from raw scientific PDFs.

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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

ETR: Entropy Trend Reward for Efficient Chain-of-Thought Reasoning (2026.acl-long)

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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Xuan Xiong, Huan Liu, Li Gu, Zhixiang Chi, Yue Qiu, Yuanhao YU, Yang Wang

ThinkSLM: Towards Reasoning in Small Language Models (2025.emnlp-main)

Challenge:	Existing methods to shorten CoTs use length penalties or global entropy reduction . Existing approaches to CoT reasoning have significant practical drawbacks .
Approach:	They propose a method that shortens CoTs by length penalties or global entropy reduction . they integrate ETR into Group Relative Policy Optimization and evaluate it .
Outcome:	The proposed objective improves accuracy–efficiency trade-off by +9.9% while reducing CoT length by 67% across four benchmarks.

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Gaurav Srivastava, Shuxiang Cao, Xuan Wang

Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future (2023.emnlp-main)

Challenge:	Reasoning has long been viewed as an emergent property of large language models (LLMs), appearing at or above a certain scale (100B parameters).
Approach:	They propose a benchmark to evaluate the reasoning abilities of small language models (SLMs) using quantization, pruning, and distillation.
Outcome:	The proposed benchmark systematically evaluates reasoning abilities of small language models trained from scratch or derived from LLMs using quantization, pruning, and distillation.

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Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang

Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models (2023.findings-acl)

Challenge:	Existing literature on the generalization of machine learning models to out-of-distribution data is lacking.
Approach:	They propose to present the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding.
Outcome:	The proposed survey provides the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding.

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Pengcheng Jiang, Shivam Agarwal, Bowen Jin, Xuan Wang, Jimeng Sun, Jiawei Han

Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models (2026.findings-acl)

Challenge:	Existing methods to augment knowledge graph completion require factual triples or manual prompts to extract knowledge from a pre-trained language model.
Approach:	They propose a tool that generates quality query prompts and retrieves support information from large text corpora to probe knowledge from a pre-trained language model.
Outcome:	The proposed method outperforms embedding-based, graph-based and PLM-based methods on two benchmark datasets.

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Gaurav Srivastava, Aafiya Shamshad Hussain, Sriram Srinivasan, Xuan Wang

Improving Robustness of Language Models from a Geometry-aware Perspective (2022.findings-acl)

Challenge:	Large language models (LLMs) achieve impressive performance on complex benchmarks yet sometimes fail on basic math reasoning.
Approach:	They propose a benchmark to evaluate the efficiency of reasoning in large language models . they formalize the accuracy-verbosity tradeoff and introduce the overthinking score .
Outcome:	The proposed model performs well on complex benchmarks but fails on basic math reasoning . the proposed model generates 18 more tokens while achieving lower accuracy .

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Bin Zhu, Zhaoquan Gu, Le Wang, Jinyin Chen, Qi Xuan

TriageAgent: Towards Better Multi-Agents Collaborations for Large Language Model-Based Clinical Triage (2024.findings-emnlp)

Challenge:	Recent studies have found that removing the norm-bounded projection and increasing search steps in adversarial training can significantly improve robustness.
Approach:	They propose friendly adversarial data augmentation and geometry-aware adversarial training to achieve stronger robustness using fewer search steps.
Outcome:	The proposed method can obtain stronger robustness using fewer steps than existing methods.

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Meng Lu, Brandon Ho, Dennis Ren, Xuan Wang

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

Challenge:	escalation in emergency department patient visits poses challenges to efficient clinical management . Currently, hospitals rely on human experts to review clinical notes and determine case urgency .
Approach:	a team of researchers develop a multi-agent framework to enhance collaborative decision-making in clinical triage.
Outcome:	The proposed framework outperforms state-of-the-art LLM-based methods on three clinical triage test sets.

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

Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)

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

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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang

Information Retrieval

Challenge:	Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains.
Approach:	They propose several strategies for deploying RAG that balance performance and efficiency.
Outcome:	The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning (2025.findings-emnlp)

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Siqu Ou, Hongcheng Liu, Pingjie Wang, Yusheng Liao, Chuan Xuan, Yanfeng Wang, Yu Wang

From Specific-MLLMs to Omni-MLLMs: A Survey on MLLMs Aligned with Multi-modalities (2025.findings-acl)

Challenge:	Existing methods for dynamic spatial reasoning are limited to text or static visual domains .
Approach:	They propose a framework that augments textual reasoning chains with dynamic visual drafts .
Outcome:	The proposed framework outperforms existing methods in dynamic spatial reasoning tasks.

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Shixin Jiang, Jiafeng Liang, Jiyuan Wang, Xuan Dong, Heng Chang, Weijiang Yu, Jinhua Du, Ming Liu, Bing Qin

Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs (2025.emnlp-main)

Challenge:	MLLMs are able to integrate multiple modalities into a single model to tackle complex tasks in real-world scenarios.
Approach:	They propose a comprehensive survey of Omni-MLLMs to address the challenges and opportunities of multimodal modeling.
Outcome:	The proposed model can integrate multiple modalities into a single model and provide novel perspectives.

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Dingkun Zhang, Shuhan Qi, Xinyu Xiao, Kehai Chen, Xuan Wang

ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision (2021.emnlp-main)

Challenge:	Recent advances in Multimodal Large Language Models (MLLMs) have enhanced their versatility as they integrate a growing number of modalities.
Approach:	They propose a simple MCL paradigm that addresses forgetting and misalignment . they propose 'MErge then ReAlign' to extend existing models to more modalities .
Outcome:	The proposed paradigm is easy to deploy and highly reusable in the MLLM community.

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Xuan Wang, Vivian Hu, Xiangchen Song, Shweta Garg, Jinfeng Xiao, Jiawei Han

Knowledge Representation

Challenge:	Named entity recognition (NER) is a fundamental step in scientific literature analysis to build AI-driven systems for molecular discovery, synthetic strategy designing, and manufacturing.
Approach:	They propose an ontology-guided method for fine-grained named entity recognition (NER) it leverages the chemistry type ontologies to generate distant labels with flexible KB-matching .
Outcome:	The proposed method significantly outperforms the state-of-the-art methods with a .25 absolute F1 improvement.

From Words to Pixels: A Comprehensive Survey on Large Language Models in Visual Segmentation (2026.acl-long)

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Yizhou Wang, Mang Tik Chiu, Lingzhi Zhang, Xuan Shen, Sohrab Amirghodsi, Yun Fu

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

Challenge:	Visual segmentation with instruction has been a challenging task for many years . large language models and large multimodal models have spurred a new wave of research .
Approach:	They review recent works in LLM-based visual segmentation and analyze their architectural innovations, training strategies, and benchmark performance.
Outcome:	The present study reviews the most recent works in LLM-driven visual segmentation . it identifies key challenges and promising future directions .

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

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

DiffSkip: Differential Layer Skipping in Large Language Models (2025.findings-acl)

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Xuan Luo, Weizhi Wang, Xifeng Yan

ReactIE: Enhancing Chemical Reaction Extraction with Weak Supervision (2023.findings-acl)

Challenge:	Existing Large Language Models (LLMs) enforce uniform computation across all tokens.
Approach:	They propose to dynamically skip FFN blocks based on self-attention difference . they propose to use a lightweight router module to do the same .
Outcome:	The proposed model can skip FFN blocks even in token generation tasks.

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Ming Zhong, Siru Ouyang, Minhao Jiang, Vivian Hu, Yizhu Jiao, Xuan Wang, Jiawei Han

ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora (2021.emnlp-main)

Challenge:	Structured chemical reaction information is a vital tool for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design.
Approach:	They propose a method which utilizes frequent patterns within the text as linguistic cues to identify specific characteristics of chemical reactions.
Outcome:	The proposed model outperforms baselines and outperformed existing models.

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Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

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

Challenge:	Existing methods for pretraining cross-lingual models are limited in their size due to the limited amount of parallel corpora.
Approach:	They propose a method that encourages the model to align multiple languages with monolingual corpora to overcome the constraint of the parallel corpus size.
Outcome:	The proposed method outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-linguistic downstream tasks.

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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 .

VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation (2024.emnlp-main)

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Xuan He, Dongfu Jiang, Ge Zhang, Max Ku, Achint Soni, Sherman Siu, Haonan Chen, Abhranil Chandra, Ziyan Jiang, Aaran Arulraj, Kai Wang, Quy Do, Yuansheng Ni, Bohan Lyu, Yaswanth Narsupalli, Rongqi Fan, Zhiheng Lyu, Bill Yuchen Lin, Wenhu Chen

Visual Data in NLP

Challenge:	Existing video metrics are lagging behind in providing reliable scores over generated videos due to lack of large-scale human-annotated dataset.
Approach:	They propose to use VideoFeedback to train a human-annotated multi-aspect score over 37.6K synthesized videos from 11 existing video generative models.
Outcome:	The proposed model outperforms the prior best metrics by 50 points in the test.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models (2025.emnlp-main)

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Weihao Xuan, Qingcheng Zeng, Heli Qi, Junjue Wang, Naoto Yokoya

Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language (2026.findings-acl)

Challenge:	Existing approaches to quantify uncertainty are limited in vision-language models . however, current models display notable miscalibration across diverse tasks and settings .
Approach:	They evaluate verbalized confidence in vision-language models using visual reasoning . they propose a prompting strategy that improves confidence alignment in multimodal settings .
Outcome:	The proposed method improves confidence alignment across multimodal settings.

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Peijie Wang, Ming-Liang Zhang, Jun Cao, Chao Deng, Dekang Ran, Pi Bu, Hongda Sun, Xuan Zhang, Yingyao Wang, Jun Song, Bo Zheng, Fei Yin, Cheng-Lin Liu

Reasoning

Challenge:	Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across various vision reasoning tasks.
Approach:	They propose a unified formal language that integrates plane and solid geometry, comprehensively covering geometric structures and semantic relations.
Outcome:	The proposed language achieves state-of-the-art parsing performance and significantly boosts MLLMs’ capabilities for downstream geometry reasoning tasks.

MEGClass: Extremely Weakly Supervised Text Classification via Mutually-Enhancing Text Granularities (2023.findings-emnlp)

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Priyanka Kargupta, Tanay Komarlu, Susik Yoon, Xuan Wang, Jiawei Han

VCORE: Variance-Controlled Optimization-based Reweighting for Chain-of-Thought Supervision (2026.acl-long)

Challenge:	Existing methods for text classification use human annotations or a set of class seed words for supervision, which can be costly, especially in emerging domains.
Approach:	They propose a weakly-supervised method that leverages mutually-enhancing text granularities to learn a contextualized document representation that captures the most discriminative class indicators.
Outcome:	Extensive experiments on seven benchmark datasets show that MEGClass outperforms other weakly and extremely weakly supervised methods.

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Xuan Gong, Senmiao Wang, Hanbo Huang, Ruoyu Sun, Shiyu Liang

Reasoning

Challenge:	Empirical evaluations demonstrate that VCORE achieves the strongest overall average performance, with especially clear gains on lower-capacity models.
Approach:	They propose a framework that reformulates supervision as a constrained optimization problem.
Outcome:	Empirical evaluations show that VCORE achieves the strongest overall average performance, with especially clear gains on lower-capacity models.

A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare (2025.findings-emnlp)

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Manar Aljohani, Jun Hou, Sindhura Kommu, Xuan Wang

Context-Aware Tracking and Dynamic Introduction for Incomplete Utterance Rewriting in Extended Multi-Turn Dialogues (2024.findings-acl)

Challenge:	a survey of large language models in healthcare raises critical concerns around trustworthiness . trustworthy of LLMs in healthcare remains underexplored, lacking a systematic review .
Approach:	a new survey examines the trustworthiness of large language models in healthcare . a review examines how each dimension affects reliability and ethical deployment of LLMs .
Outcome:	The present study examines the trustworthiness of large language models in healthcare . it identifies key gaps in existing approaches and challenges posed by evolving paradigms .

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Xinnan Guo, Qian Zhu, Qiuhui Shi, Xuan Lin, Liubin Wang, DaqianLi DaqianLi, Yongrui Chen

Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction (2024.findings-eacl)

Challenge:	Existing methods to reconstruct utterance with omitted information and pronouns are limited to brief multi-turn dialogues.
Approach:	They propose a method to reconstruct utterance with omitted information and pronouns to be standalone and complete based on context.
Outcome:	The proposed method improves existing models and achieves state-of-the-art on three benchmarks.

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Qingyun Wang, Zixuan Zhang, Hongxiang Li, Xuan Liu, Jiawei Han, Huimin Zhao, Heng Ji

TTM-RE: Memory-Augmented Document-Level Relation Extraction (2024.acl-long)

Challenge:	Existing frameworks for fine-grained few-shot entity extraction are difficult to implement in the chemical domain due to the information overload of scientific papers.
Approach:	They propose a sequence-to-sequence based few-shot entity extraction approach . it uses a seq2seq entity extractor and a self-validation module to reconstruct original input sentence .
Outcome:	The proposed framework achieves 8.26% and 6.84% performance gains on two datasets.

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Chufan Gao, Xuan Wang, Jimeng Sun

DAMRO: Dive into the Attention Mechanism of LVLM to Reduce Object Hallucination (2024.emnlp-main)

Challenge:	Existing methods for document-level relation extraction are ineffective in exploiting the full potential of large amounts of training data with varied noise levels.
Approach:	They propose a novel approach that integrates a trainable memory module with a noisy-robust loss function that accounts for the positive-unlabeled setting to unlock the full potential of large-scale noisy training data.
Outcome:	The proposed model outperforms existing methods on a ReDocRED benchmark dataset with an absolute F1 score improvement of over 3%.

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Xuan Gong, Tianshi Ming, Xinpeng Wang, Zhihua Wei

CONSENSAGENT: Towards Efficient and Effective Consensus in Multi-Agent LLM Interactions Through Sycophancy Mitigation (2025.findings-acl)

Challenge:	Despite the success of Large Vision-Language Models, they suffer from hallucination.
Approach:	They propose a training-free strategy that "Dive into" the attention of LVLMs to "Reduce" object hallucination by using classification tokens of ViT.
Outcome:	The proposed method reduces the impact of outlier tokens on LVLMs . the proposed method is based on LLaVA-1.5, LLvaVA-NeXT and InstructBLIP .

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Priya Pitre, Naren Ramakrishnan, Xuan Wang

Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)

Challenge:	Recent advances in multi-agent large language model systems have shown remarkable performance in tasks such as reasoning, planning, and decision-making.
Approach:	They propose a framework that dynamically refines prompts based on agent interactions to mitigate sycophancy by requiring additional debate rounds to reach consensus.
Outcome:	The proposed framework outperforms both single-agent and multi-a agent baselines and achieves state-of-the-art results across all benchmark datasets.

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Xuan Wang, Shuxiang Cao, Yuchen Zhuang, Wenqi Shi

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

Challenge:	Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems .
Approach:	This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving .
Outcome:	This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance .

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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

Information Retrieval

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.

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

Multilinguality

Challenge:	Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach:	They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome:	The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.

MERIT: Multi-Agent Collaboration for Unsupervised Time Series Representation Learning (2025.findings-acl)

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Shu Zhou, Yunyang Xuan, Yuxuan Ao, Xin Wang, Tao Fan, Hao Wang

CROSSAGENTIE: Cross-Type and Cross-Task Multi-Agent LLM Collaboration for Zero-Shot Information Extraction (2025.findings-acl)

Challenge:	Existing approaches to time series representation learning are time-consuming and expert-dependent, which are difficult to generalize across different tasks.
Approach:	They propose to use large language model agent to guide unsupervised time series representation learning and a framework to integrate three LLM agents to collaboratively generate positive views for time series data.
Outcome:	The proposed framework integrates large language model (LLM) agent to guide unsupervised time series representation learning and compares it with state-of-the-art baselines on multiple time series datasets.

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Meng Lu, Yuzhang Xie, Zhenyu Bi, Shuxiang Cao, Xuan Wang

Low-Resource NLP

Challenge:	Large language models struggle with producing structured output while maintaining accuracy in zero-shot information extraction (IE)
Approach:	They propose a multi-agent framework that enhances zero-shot IE through multi-task collaboration.
Outcome:	CROSSAGENTIE outperforms state-of-the-art models in structured prediction . the framework significantly reduces inference cost while preserving accuracy .

Cross-lingual Text Classification with Heterogeneous Graph Neural Network (2021.acl-short)

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Ziyun Wang, Xuan Liu, Peiji Yang, Shixing Liu, Zhisheng Wang

Multilinguality

Challenge:	Existing methods for cross-lingual text classification only consider factors beyond semantic similarity, causing performance degradation between some language pairs.
Approach:	They propose a method to incorporate heterogeneous information within and across languages for cross-lingual text classification using graph convolutional networks.
Outcome:	The proposed method significantly outperforms state-of-the-art models on all tasks and achieves consistent performance gain over baselines in low-resource settings.

Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (2024.findings-acl)

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Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce Ho, Carl Yang

A Simple and Efficient Learning-Style Prompting for LLM Jailbreaking (2026.findings-eacl)

Challenge:	Clinical natural language processing (NLP) is a subfield that requires the extraction, analysis, and interpretation of unstructured clinical text.
Approach:	They propose a model which infuses knowledge into clinical text generation with LLMs for clinical NLP tasks.
Outcome:	The proposed model improves performance across 8 clinical NLP tasks and 18 datasets by 7.7%-8.7% on average.

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Xuan Luo, Yue Wang, Zefeng He, Geng Tu, Jing Li, Ruifeng Xu

DEBATE, TRAIN, EVOLVE: Self‐Evolution of Language Model Reasoning (2025.emnlp-main)

Challenge:	Learning-style queries can reliably elicit harmful responses, highlighting a critical safety blind spot in modern LLMs.
Approach:	They propose a new reframing paradigm that hides intention by learning from LLMs and uses 4 conceptual components to construct learning-style queries.
Outcome:	The proposed framework achieves top attack success rates on most models and across malicious categories while maintaining high efficiency with concise prompts.

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Gaurav Srivastava, Zhenyu Bi, Meng Lu, Xuan Wang

MelTrim: Coarse-to-Fine Data Pruning for Speech Classification (2026.findings-acl)

Challenge:	Large language models have improved significantly in reasoning through extensive training on massive datasets.
Approach:	They propose a ground truth-free training framework that uses multi-agent debate traces to evolve a single language model.
Outcome:	The proposed framework achieves 8.92% accuracy gain on the GSM-PLUS dataset.

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Shaobo Wang, Tianle Niu, Xuan Ouyang, Xintong Li, Zhengkun Ge, Yue Min, Xiaoqian Liu, Hankun Wang, Linfeng Zhang

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

Challenge:	Unlike image or text classification, speech classification tasks are particularly challenging due to the difficulty in capturing the acoustic, semantic, and contextual representations.
Approach:	They propose a dataset pruning method that coarsely filters redundant samples using DBSCAN clustering on Mel-Frequency Cepstral Coefficients (MFCC) features.
Outcome:	The proposed method achieves 49.5% improvement in WA on the MEAD dataset and 41.9% reduction in EER on speaker identification tasks.

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Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D Williams, Lin Li

Variational Autoregressive Decoder for Neural Response Generation (D18-1)

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

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Jiachen Du, Wenjie Li, Yulan He, Ruifeng Xu, Lidong Bing, Xuan Wang

Text Generation

Challenge:	Existing variational Bayesian models generate responses from a single latent variable, which is not sufficient to model high variability in responses.
Approach:	They propose a conditional variable auto-encoder that sequentially introduces latent variables to condition the generation of each word in the response sequence.
Outcome:	Empirical results show that the proposed model improves on state-of-the-art models on Opensubtitle and Reddit datasets.

Hindsight: Structured Agent Memory that Retains, Recalls, and Reflects (2026.acl-demo)

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Christopher Latimer, Nicolò Boschi, Andrew Neeser, Chris Bartholomew, Gaurav Srivastava, Xuan Wang, Naren Ramakrishnan

Structured Data in NLP

Challenge:	Hindsight organizes long-term memory into four logical networks and exposes three core operations.
Approach:	Hindsight organizes long-term memory into four logical networks and exposes three core operations.
Outcome:	Hindsight is a working memory system for AI agents that separates facts from beliefs . the system outperforms existing models on LongMemEval and LoCoMo with 83.6% accuracy .

Pruning Foundation Models for High Accuracy without Retraining (2024.findings-emnlp)

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Pu Zhao, Fei Sun, Xuan Shen, Pinrui Yu, Zhenglun Kong, Yanzhi Wang, Xue Lin

On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization (2024.findings-emnlp)

Challenge:	Despite the superior performance of foundation models, it is challenging to deploy large language models in practical applications due to their massive parameters and computations.
Approach:	They propose a pruning algorithm to prune LLMs in one-shot without retraining . they propose retrainable pruning algorithms to prune multiple weights in LLM .
Outcome:	The proposed pruning methods perform better than baseline pruning methods on sparse and unstructured sparsity models.

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Yong Lin, Skyler Seto, Maartje Ter Hoeve, Katherine Metcalf, Barry-John Theobald, Xuan Wang, Yizhe Zhang, Chen Huang, Tong Zhang

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is an effective approach for aligning language models to human preferences.
Approach:	They compare the accuracy of DPORM and EXRM with a reward function for scoring human preferences.
Outcome:	The proposed methods can approximate an EXRM on the limit infinite samples, but it is unclear how effective they are in practice.

Multi-Grained Knowledge Distillation for Named Entity Recognition (2021.naacl-main)

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Xuan Zhou, Xiao Zhang, Chenyang Tao, Junya Chen, Bing Xu, Wei Wang, Jing Xiao

Green & Sustainable NLP

Challenge:	Pre-trained big models have delivered top performance in Seq2seq modeling, but their deployments in real-world applications are often hindered by excessive computations and memory demands.
Approach:	They propose a distillation scheme to efficiently transfer knowledge from big models to their cheaper counterparts.
Outcome:	The proposed scheme maximizes the assimilation of knowledge from the teacher model to the student model.

BTW: A Non-Parametric Variance Stabilization Framework for Multimodal Model Integration (2025.findings-emnlp)

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Jun Hou, Le Wang, Xuan Wang

Text Classification

Challenge:	Existing methods for multimodal learning are difficult to scale beyond two modalities and lack resolution for instance-level control.
Approach:	They propose a bi-level weighting framework that combines instance-level Kullback-Leibler divergence and modality-level mutual information to dynamically adjust modality importance during training.
Outcome:	The proposed method significantly improves regression performance and multiclass classification accuracy.

DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation (2021.findings-emnlp)

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Yuzhao Mao, Guang Liu, Xiaojie Wang, Weiguo Gao, Xuan Li

Dialogue Systems & Conversational Agents

Challenge:	Existing studies focus on the self and inter-personal dependencies in multi-modal conversations, but they ignore the temporal and spatial dependencies.
Approach:	They propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics.
Outcome:	The proposed models outperform the state-of-the-art on three benchmark datasets.

SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation (2025.findings-naacl)

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Qilong Wu, Xiaoneng Xiang, Huang Hejia, Xuan Wang, Yeo Wei Jie, Ranjan Satapathy, Ricardo Shirota Filho, Bharadwaj Veeravalli

AI for Science in the Era of Large Language Models (2024.emnlp-tutorials)

Challenge:	Existing tools for financial reporting and ESG analysis are lacking . large language models are not proficient across general finance and ESE domains .
Approach:	They propose a dataset that includes seven financial NLP tasks and a benchmark to improve sustainability report generation.
Outcome:	SusGen-30k, a high-quality dataset, shows state-of-the-art performance . it surpasses all other models except GPT-4 in six adapted tasks and two off-the shelf tasks .

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Zhenyu Bi, Minghao Xu, Jian Tang, Xuan Wang

MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models (2025.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have demonstrated significant prowess in tasks involving natural language, such as translating languages, constructing chatbots, and answering questions.
Approach:	This tutorial explores the application of large language models to three crucial categories of scientific data: 1) textual data, 2) biomedical sequences, and 3) brain signals.
Outcome:	This tutorial will explore the application of large language models to three crucial categories of scientific data.

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Bohan Jin, Shuhan Qi, Kehai Chen, Xinyi Guo, Xuan Wang

Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency (2025.acl-long)

Challenge:	Large Multimodal Models (LMMs) have raised concerns about model toxicity.
Approach:	They propose a model to measure the toxicity gap between models and their hard level to determine whether they can handle dual-implicit toxicity.
Outcome:	The proposed model can handle dual-implicit toxicity effectively on 13 prominent LMMs, but its performance drops significantly in hard level.

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Jiafeng Liang, Shixin Jiang, Xuan Dong, Ning Wang, Zheng Chu, Hui Su, Jinlan Fu, Ming Liu, See-Kiong Ng, Bing Qin

DRGCoder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups (2023.emnlp-demo)

Challenge:	Large Multimodal Models (LMMs) have demonstrated impressive performance on general video comprehension benchmarks, but their robustness needs to be thoroughly investigated for broader applications.
Approach:	They propose a temporal robustness benchmark which introduces temporal inconsistency perturbations separately at the visual and textual modalities to assess the robustness of models.
Outcome:	The proposed method improves the model’s robustness and reliability in temporal analysis.

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Daniel Hajialigol, Derek Kaknes, Tanner Barbour, Daphne Yao, Chris North, Jimeng Sun, David Liem, Xuan Wang

Information Retrieval

Challenge:	Medical claim coding is the process of transforming medical records into structured codes in a classification system such as ICD-10 (International Classification of Diseases, Tenth Revision) or DRG (Diagnosis-Related Group) codes.
Approach:	They propose an explainability-enhanced clinical claim coding system for the early prediction of medical severity DRGs (MS-DRGs) a novel multi-task Transformer model allows users to inspect DRGCoder’s reasoning by visualizing the weights for each word of the input.
Outcome:	The proposed system allows users to analyze the weights of the input and compare across multiple discharge summaries.

Improving Continual Pre-training Through Seamless Data Packing (2025.findings-acl)

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Ruicheng Yin, Xuan Gao, Changze Lv, Xiaohua Wang, Xiaoqing Zheng, Xuanjing Huang