Papers by Shuai Xu

Learning from Near-Misses: Error-Aware Contrastive Few-Shot Learning for NL2Formula (2026.acl-long)

Challenge:	ProUIE improves universal information extraction (UIE) without external information . many LLM-based methods rely on extra schema cues, external resources or complex alignment and verification pipelines .
Approach:	They propose a Macro-to-Micro progressive learning approach that improves UIE without external information.
Outcome:	ProUIE outperforms instruction-tuned baselines on average for NER and RE while using a smaller backbone.

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Zhihao Shuai, Yiyun Chen, Maolin Ma, Yutong Chen, Hanjia Qiu, Jing Xu, Ziye Chen, Weikai Yang

Hallucination Diversity-Aware Active Learning for Text Summarization (2024.naacl-long)

Challenge:	Existing spreadsheet formulas often produce near-miss outputs due to an incorrect function, operator, or reference.
Approach:	They propose an abstract syntax tree-based error taxonomy that organizes common error modes by the kind of decision that goes wrong in the parse tree.
Outcome:	The proposed framework improves Exact Match (EM) by 6.4 points over supervised fine-tuning and matches self-consistency (SC@5) accuracy.

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Yu Xia, Xu Liu, Tong Yu, Sungchul Kim, Ryan Rossi, Anup Rao, Tung Mai, Shuai Li

Responsible & Trustworthy NLP

Challenge:	Existing methods for alleviating hallucinations require costly human annotations . Existing approaches focus on a specific type of hallucinism, which limits their effectiveness .
Approach:	They propose a method to detect hallucinations from errors in semantic frame, discourse and content verifiability in LLM summarization using HAllucination Diversity-Aware Sampling.
Outcome:	The proposed framework reduces the need for costly human annotations to correct hallucinations in LLM outputs.

Improving Event Detection via Open-domain Trigger Knowledge (2020.acl-main)

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Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie

Understanding and Preventing Entropy Collapse in RLVR with On-Policy Entropy Flow Optimization (2026.findings-acl)

Challenge:	Existing methods for event detecting are prone to overfitting densely labeled trigger words due to the small scale of training data.
Approach:	They propose a novel Enrichment Knowledge Distillation model to leverage external open-domain trigger knowledge to reduce in-built biases to frequent trigger words in annotations.
Outcome:	The proposed model outperforms nine strong baselines and is especially effective for unseen/sparsely labeled trigger words.

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Huimin Xu, Shuai Zhao, Xiaobao Wu, Anh Tuan Luu

Reasoning

Challenge:	Existing RLVR algorithms suffer from entropy collapse, leading to premature determinism and unstable optimization.
Approach:	They propose an adaptive entropy flow balancing mechanism that rescales entropic-increasing and enotro-decreazing updates according to their contributions to enthroy change.
Outcome:	The proposed method outperforms existing RLVR algorithms on six reasoning benchmarks.

Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models (2025.acl-long)

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Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, Richard Yi Da Xu, Yunya Song, Xian Yang

Responsible & Trustworthy NLP

Challenge:	Existing models struggle to balance predictive accuracy with human-understandable rationales.
Approach:	They propose to enhance LLMs by leveraging rationale distillation and domain knowledge injection for trustworthy multimodal rationale generation.
Outcome:	Experiments on real-world medical datasets show that ClinRaGen achieves state-of-the-art performance in disease diagnosis and rationale generation.

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

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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

Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion (2024.emnlp-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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Guanchu Wang, Yu-Neng Chuang, Ruixiang Tang, Shaochen Zhong, Jiayi Yuan, Hongye Jin, Zirui Liu, Vipin Chaudhary, Shuai Xu, James Caverlee, Xia Hu

Sparsity-Accelerated Training for Large Language Models (2024.findings-acl)

Challenge:	Existing mechanisms compromise ownership rights or raise data privacy concerns . existing mechanisms compromise security of released large language models .
Approach:	They propose a TaylorMLP to preserve the ownership of large language models by transforming the weights of LLMs into Taylor-series parameters instead of releasing original weights .
Outcome:	The proposed model preserves ownership of large language models and prevents their abuse by adjusting the generation speed and causing low-speed token generation.

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Da Ma, Lu Chen, Pengyu Wang, Hongshen Xu, Hanqi Li, Liangtai Sun, Su Zhu, Shuai Fan, Kai Yu

Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated proficiency across various NLP tasks but often require additional training, such as continual pre-training and supervised fine-tuning.
Approach:	They propose to leverage sparsity in pre-trained LLMs to accelerate training by disregarding computations for unimportant neurons.
Outcome:	The proposed framework achieves comparable or superior performance to standard training while significantly accelerating the process.

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Sijia Cui, Aiyao He, Shuai Xu, Hongming Zhang, Yanna Wang, Qingyang Zhang, Yajing Wang, Bo Xu

Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)

Challenge:	Existing methods for function calling require expert effort and prompt engineering becomes inefficient.
Approach:	They propose a method that performs fine-grained, stepwise retrieval from a continually updated experience pool.
Outcome:	The proposed method achieves an average improvement of 6.1% on easy and 4.7% on hard questions.

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Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan, Xingyu Chen, Shuai Fan, Lu Chen, Kai Yu

Word-level Commonsense Knowledge Selection for Event Detection (2024.lrec-main)

Challenge:	Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries.
Approach:	They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence.
Outcome:	The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage.

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Shuai Yang, Yu Hong, Shiming He, Qingting Xu, Jianmin Yao

Dialogue Systems & Conversational Agents

Challenge:	Event Detection (ED) is a task of automatically extracting multi-class trigger words . Xie and Tu, 2022, use a Context-specific Knowledge Selector to select commonsense knowledge of words based on living contexts .
Approach:	They use a Context-specific Knowledge Selector to select the exact commonsense knowledge of words from a large knowledge base.
Outcome:	The proposed approach achieves the F1-score of about 78.3% on the ACE-2005 dataset.

LGSA: Label Geometry Structuring and Aligning for Hierarchical Text Classification (2026.acl-long)

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Shuai Zhang, Weibo Xu, Jiahao Nie, Kecheng Huang

Information Retrieval

Challenge:	Existing hierarchical text classification methods use prompt tuning or contrastive learning to implicitly learn label embeddings for classification, but this method fails to model hierarchy-aware geometric relations among labels.
Approach:	They propose a two-stage framework that transforms the label hierarchy from an implicit prior into an explicit embedding by using a general orthogonal frame.
Outcome:	The proposed framework outperforms existing state-of-the-art methods on three real-world HTC datasets.

Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs (2025.coling-main)

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Yu Xia, Rui Wang, Xu Liu, Mingyan Li, Tong Yu, Xiang Chen, Julian McAuley, Shuai Li

Challenge:	Large Language Models (LLMs) have shown impressive reasoning abilities when prompted with Chain-of-Thought (CoT).
Approach:	They propose to categorize Chain-of-X methods by taxonomies of nodes, i.e., the X in CoX, and application tasks, and then categorise them by taxanomies and discuss potential future directions.
Outcome:	The proposed methods are categorised by taxonomies of nodes, i.e., the X in CoX, and application tasks.

Reinforcement Learning on Pre-Training Data (2026.acl-long)

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

KnowVrDU: A Unified Knowledge-aware Prompt-Tuning Framework for Visually-rich Document Understanding (2024.lrec-main)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Yunqi Zhang, Yubo Chen, Jingzhe Zhu, Jinyu Xu, Shuai Yang, Zhaoliang Wu, Liang Huang, Yongfeng Huang, Shuai Chen

BizCompass: Benchmarking the Reasoning Capabilities of LLMs in Business Knowledge and Applications (2026.findings-acl)

Challenge:	Existing methods for integrating layout and image features into pre-training language models are not suitable for few-shot settings.
Approach:	They propose to reformulate VrDU tasks into a single question-answering format with task-specific prompts and train the pre-trained model with the parameter-efficient prompt tuning method.
Outcome:	The proposed framework can be used in few-shot settings and reduces data requirements.

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Jianing Hao, Yuhe Wu, Yuanjian Xu, Shichang Meng, Shuai Yuan, Wei Zeng, Zixuan Wang, Guang Zhang

Reasoning

Challenge:	Existing benchmarks focus on narrow tasks and leave a fundamental question unanswered . Existing models only focus on specific tasks, requiring rigorous reasoning and knowledge .
Approach:	They propose a benchmark to connect theoretical foundations with practical business knowledge and applications.
Outcome:	The benchmark systematically evaluates both open-source and commercial LLMs . it reveals how theoretical knowledge translates into practical performance in business .

Improving Deep Embedded Clustering via Learning Cluster-level Representations (2022.coling-1)

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Qing Yin, Zhihua Wang, Yunya Song, Yida Xu, Shuai Niu, Liang Bai, Yike Guo, Xian Yang

Diffusion Language Model Inference with Monte Carlo Tree Search (2026.findings-eacl)

Challenge:	Existing efforts to learn meaningful representations at the instance level are limited.
Approach:	They propose a deep embedded clustering model with cluster-level representation learning to jointly learn cluster and instance level representations.
Outcome:	The proposed model produces meaningful clusters on real-world short text datasets.

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Zheng Huang, Kiran Ramnath, Yueyan Chen, Aosong Feng, Sangmin Woo, Balasubramaniam Srinivasan, Zhichao Xu, Kang Zhou, Shuai Wang, Haibo Ding, Lin Lee Cheong

GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis (2026.findings-acl)

Challenge:	Existing methods for inference use heuristics to determine which positions to unmask and which tokens to commit . MEDAL is an inference-time scaling framework that integrates Monte Carlo Tree SEarch initialization for Diffusion Language Model inference.
Approach:	They propose a framework that integrates Monte Carlo Tree SEarch initialization for Diffusion Language Model inference.
Outcome:	The proposed framework achieves 22.0% improvement over existing inference strategies across multiple benchmarks.

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Shaoting Tan, Ning Liu, Yuntao Du, Shuyue Wei, Wu Shuai, Qian Li, Yanyu Xu, Wei Zhang, Lizhen Cui, Haitao Yuan

Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)

Challenge:	Existing Large Language Models struggle to reason systematically under cost constraints . Existing approaches lack the knowledge-reasoning capability to reason under cost .
Approach:	They propose a knowledge-enhanced framework that leverages large language models to construct MDKGs . they propose three collaborative agents that handle language understanding and generation .
Outcome:	GraphDx improves diagnostic success rates from 50–68% to 79–93% while reducing test costs by 20–54%.

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Zhen Li, Xiaohan Xu, Tao Shen, Can Xu, Jia-Chen Gu, Yuxuan Lai, Chongyang Tao, Shuai Ma

TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning (2026.findings-acl)

Challenge:	introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance.
Approach:	They propose a taxonomy for organizing existing LLM-based evaluation metrics and a structured framework to understand and compare them.
Outcome:	The proposed taxonomy offers a framework to understand and compare LLM-based evaluation methods.

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Jinyang Wu, Chonghua Liao, Mingkuan Feng, Shuai Zhang, Zhengqi Wen, Haoran Luo, Ling Yang, Huazhe Xu, Jianhua Tao

Responsible & Trustworthy NLP

Challenge:	Existing RL methods rely on unstructured self-sampling to fit scalar rewards, resulting in inefficient rollouts.
Approach:	They propose a structured template-guided RL framework that augments policy optimization with explicit template guidance.
Outcome:	Experiments show that TemplateRL outperforms GRPO and GRPI by 99% on AIME and 41% on AMC with superior stability on weak models and remarkable cross-domain generalization.

FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning (2021.findings-emnlp)

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Xu Wang, Hainan Zhang, Shuai Zhao, Yanyan Zou, Hongshen Chen, Zhuoye Ding, Bo Cheng, Yanyan Lan

Natural Language Interfaces

Challenge:	Existing neural dialogue models only capture syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response.
Approach:	They propose a fine-grained comparison model to capture syntactic and semantic information and then compare each candidate's representation with the whole history to obtain a history consistency representation.
Outcome:	The proposed model obtains higher ranking scores than baseline models on two public dialogue datasets.

Re-Reading Improves Reasoning in Large Language Models (2024.emnlp-main)

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Xiaohan Xu, Chongyang Tao, Tao Shen, Can Xu, Hongbo Xu, Guodong Long, Jian-Guang Lou, Shuai Ma

DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction (2022.naacl-main)

Challenge:	Unlike thought-eliciting prompting methods, RE2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process.
Approach:	They introduce a simple, yet general and effective prompting method, RE2, which rereads the question as input.
Outcome:	The proposed method demonstrates strong generality and compatibility with most thought-eliciting prompting methods, including CoT.

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MeiHan Tong, Bin Xu, Shuai Wang, Meihuan Han, Yixin Cao, Jiangqi Zhu, Siyu Chen, Lei Hou, Juanzi Li

ACEBench: A Comprehensive Evaluation of LLM Tool Usage (2025.findings-emnlp)

Challenge:	Existing datasets focus on sentence-level event extraction, but document-level EE is limited due to the lack of large-scale and practical training and evaluation datasets.
Approach:	They propose a document-level event extraction dataset with 27,000+ events and 180,000+ arguments.
Outcome:	The proposed dataset includes 27,000+ events, 180,000+ arguments and large-scale manual annotations, fine-grained argument types and application-oriented settings.

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Chen Chen, Xinlong Hao, Weiwen Liu, Xu Huang, Xingshan Zeng, Shuai Yu, Dexun Li, Yuefeng Huang, Xiangcheng Liu, Wang Xinzhi, Wu Liu

Dialogue Systems & Conversational Agents

Challenge:	Existing benchmarks for evaluating LLMs’ tool usage face several limitations: limited evaluation scenarios, lacking assessments in real multi-turn dialogue contexts; narrow evaluation dimensions, with insufficient detailed assessments of how LLM use tools; and reliance on LLM or real API executions for evaluation, which introduces significant overhead.
Approach:	ACEBench is a benchmark for evaluating tool usage in Large Language Models . it categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent.
Outcome:	ACEBench categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent.

Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)

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Fangzhi Xu, Zhiyong Wu, Qiushi Sun, Siyu Ren, Fei Yuan, Shuai Yuan, Qika Lin, Yu Qiao, Jun Liu

Exploring Question Guidance and Answer Calibration for Visually Grounded Video Question Answering (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language.
Approach:	They propose a model that integrates symbolic data into LLM training without loss of generality ability.
Outcome:	The proposed model performs better on symbol- and NL-centric tasks.

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Yuanxing Xu, Yuting Wei, Shuai Zhong, Xinming Chen, Jinsheng Qi, Bin Wu

Visual Data in NLP

Challenge:	Existing methods for videoQA lack temporal localization labels, leading to inaccurate localization.
Approach:	They propose a Question-Guided and Answer-Calibrated TRansformer which guides and calibrates localization using question and option texts without localization labels.
Outcome:	The proposed model achieves comparable accuracy to large-scale pretrained models and leads in localization aspects.

Data-to-Text Generation with Style Imitation (2020.findings-emnlp)

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Shuai Lin, Wentao Wang, Zichao Yang, Xiaodan Liang, Frank F. Xu, Eric Xing, Zhiting Hu

Text Generation

Challenge:	Recent approaches to data-to-text generation focus on improving content fidelity, but lack explicit control over writing styles.
Approach:	They propose a way to control writing styles by using existing sentences as "soft" templates . they conduct experiments in restaurants and sports domains to test their approach .
Outcome:	The proposed approach achieves stronger performance than a range of comparison methods.

Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition (2021.acl-long)

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Meihan Tong, Shuai Wang, Bin Xu, Yixin Cao, Minghui Liu, Lei Hou, Juanzi Li

Low-Resource NLP

Challenge:	Existing methods to classify named entity mentions with fewshots fail to differentiate rich semantics in other-class words, which will aggravate overfitting under few shot scenario.
Approach:	They propose a model that can automatically induce different unde- fined classes from the other class to improve few-shot Named Entity Recognition (NER) .
Outcome:	The proposed model outperforms five state-of-the-art models in 1- shot and 5-shots settings on four NER bench marks.

Modelling Long-distance Node Relations for KBQA with Global Dynamic Graph (2020.coling-main)

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Xu Wang, Shuai Zhao, Jiale Han, Bo Cheng, Hao Yang, Jianchang Ao, Zhenzi Li

Structured Data in NLP

Challenge:	Existing studies rely on deep graph neural networks (GNNs) to capture rich structural information, but they lack the structural information needed for QA.
Approach:	They propose a framework which captures structural information from KBs and models long-distance node relations from two perspectives.
Outcome:	The proposed framework models long-distance node relations from two perspectives . it is based on two widely used multi-hop KBQA datasets .

ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series (2025.findings-acl)

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Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, V. W., Richard Yi Da Xu, Guo Li, Xian Yang