Papers by Hang Xu

Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration (2022.acl-long)

Challenge:	Representative models like LLaVA and MiniGPT-4 have great capabilities in various tasks.
Approach:	They propose a unified model to represent various multi-modal tasks using a single representation.
Outcome:	The proposed model outperforms existing models in a variety of tasks while maintaining generality and scalability.

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Xiwen Liang, Fengda Zhu, Li Lingling, Hang Xu, Xiaodan Liang

DetGPT: Detect What You Need via Reasoning (2023.emnlp-main)

Challenge:	Existing methods of fine-tuning vision-language navigation models require extra human-labeled data and lack self-exploration capabilities in environments.
Approach:	They propose a method that can self-explore environments without human labeling . they use a large-scale cross-modal pretrained model to build an in-domain dataset .
Outcome:	The proposed model can self-explore environments without human labeling without human supervision and generates structured instructions without human intervention.

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Renjie Pi, Jiahui Gao, Shizhe Diao, Rui Pan, Hanze Dong, Jipeng Zhang, Lewei Yao, Jianhua Han, Hang Xu, Lingpeng Kong, Tong Zhang

Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions (2025.acl-long)

Challenge:	Recent advances in the field of computer vision have enabled more effective and sophisticated interactions between humans and machines.
Approach:	They propose a reasoning-based object detection paradigm that leverages state-of-the-art multi-modal models and open-vocabulary object detectors to perform reasoning within the context of the user’s instructions and the visual scene.
Outcome:	The proposed method enables users to interact with the system using natural language instructions, allowing for a higher level of interactivity.

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Hang Li, Tianlong Xu, Kaiqi Yang, Yucheng Chu, Yanling Chen, Yichi Song, Qingsong Wen, Hui Liu

Numerical Reasoning

Challenge:	Recent studies have demonstrated the potential of large language models (LLMs) for automatic error detection in math word problems (MWPs).
Approach:	They propose a framework that generates adaptive reference solutions using LLMs to enhance error detection by reducing conformity bias in MWPs.
Outcome:	The proposed framework mitigates the performance gap between conventional and alternative solutions in MWPs, especially when combined with reasoning-enhancing techniques like chain-of-thought prompting.

MedAdapter: Efficient Test-Time Adaptation of Large Language Models Towards Medical Reasoning (2024.emnlp-main)

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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Haotian Sun, Hang Wu, Carl Yang, May Dongmei Wang

Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge (2021.emnlp-main)

Challenge:	Large language models (LLMs) have improved generation and reasoning capabilities compared to traditional BERT-sized models due to massive number of parameters and extensive pre-training on vast textual corpora.
Approach:	They propose a unified post-hoc adapter for test-time adaptation of large language models . they propose to fine-tune only a small BERT-sized adapter to rank candidate LLMs .
Outcome:	The proposed adapter improves performance on four biomedical tasks without requiring computational resources or sharing data with third parties.

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Bin Liang, Hang Su, Rongdi Yin, Lin Gui, Min Yang, Qin Zhao, Xiaoqi Yu, Ruifeng Xu

Sentiment Analysis

Challenge:	Existing methods for aspect category sentiment analysis do not necessarily occur in a sentence.
Approach:	They propose a Beta Distribution-guided aspect-aware graph construction based on external knowledge . they use aspect-related words as the pivots to derive aspect-relevant weights .
Outcome:	The proposed approach outperforms the state-of-the-art methods on 6 benchmark datasets.

CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs (2025.findings-emnlp)

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Qi Xu, Qian Liu, Hao Fei, Hang Yu, Shuhao Guan, Xiao Wei

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)

Challenge:	Existing work focuses on enabling LLMs to leverage legal rules to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules.
Approach:	They propose a legal paragraph prediction task that aims to predict the legal paragraph given criminal facts and a framework CLEAR to enhance their legal reasoning ability.
Outcome:	The proposed model improves the ability of LLMs to analyze legal cases with the guidance of legal rule insights.

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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang

Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025.findings-emnlp)

Challenge:	EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers.
Approach:	They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs.
Outcome:	The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets.

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Mengting Hu, Jianfeng Wu, Ming Jiang, Yalan Xie, Zhunheng Wang, Rui Ying, Xiaoyi Liu, Ruixuan Xu, Hang Gao, Renhong Cheng

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection (2026.findings-acl)

Challenge:	Existing approaches to recognize relational relationships with a few support samples are limited for unlimited queries.
Approach:	They propose a simple but effective framework that uses relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics.
Outcome:	The proposed framework outperforms strong baselines while being robust against various NOTA rates.

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Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, YiFan Zhang, Tianlong Xu, Zhendong Chu, Aoxiao Zhong, Kun Wang, Hui Xiong, Philip S. Yu, Xuming Hu, Qingsong Wen

Reasoning

Challenge:	Current mathematical benchmarks focus on evaluating MLLMs’ problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection.
Approach:	They propose to evaluate multimodal error detection by evaluating two sub-tasks error step identification and error categorization.
Outcome:	The proposed task evaluates MLLMs' ability to handle multimodal questions compared to text-only models.

Noise Stability Regularization for Improving BERT Fine-tuning (2021.naacl-main)

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Hang Hua, Xingjian Li, Dejing Dou, Chengzhong Xu, Jiebo Luo

TRUST: Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification (2026.findings-acl)

Challenge:	Recent studies show that fine-tuning pre-trained language models is unstable when there are only a small number of training samples available.
Approach:	They propose to use a method to regularize noise in deep nets to improve fine-tuning on NLP tasks.
Outcome:	The proposed method improves fine-tuning on natural language processing tasks by incorporating noise to the input and demonstrating generalizability and stability.

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Ruixuan Xu, Mengting Hu, Zhunheng Wang, Ming Jiang, Rui Ying, Zhen Zhang, Hang Gao, Shuaipeng Liu, Renhong Cheng

MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026.acl-long)

Challenge:	Existing graph-based detection models are vulnerable to deceptive message propagation, where bots deliberately interact with legitimate users.
Approach:	They propose a framework to mitigate deceptive message propagation by node-level uncertainty estimation and graph structure purification.
Outcome:	The proposed framework improves on three benchmark datasets and six GNN backbones on real-world social bots.

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Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu

A Survey of Uncertainty Estimation Methods on Large Language Models (2025.findings-acl)

Challenge:	Existing methods for optimizing reasoning quality are limited by overthinking.
Approach:	They propose a method that allocates thinking budgets to critical reasoning steps by tracking and aggregating step-wise uncertainty over time.
Outcome:	The proposed method reduces computation by over 45% on average while improving accuracy by 0.33–3.46%.

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Zhiqiu Xia, Jinxuan Xu, Yuqian Zhang, Hang Liu

Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models (2025.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities but could produce biased, hallucinated, or non-factual responses.
Approach:	They propose to conduct extensive experimental evaluations of LLM uncertainty estimation methods . large language models have demonstrated remarkable capabilities across tasks .
Outcome:	The proposed method could produce biased, hallucinated, or non-factual responses . a lack of comprehensive surveys on LLM uncertainty estimation is a problem .

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Wei Wang, Zhaowei Li, Qi Xu, Linfeng Li, YiQing Cai, Botian Jiang, Hang Song, Xingcan Hu, Pengyu Wang, Li Xiao

Multimodality

Challenge:	Recent advances in multi-modal large language models have demonstrated remarkable capabilities in multimodal understanding, reasoning, and interaction.
Approach:	They propose a method that effectively aligns and integrates multi-scale knowledge of objects . they use a pipeline that provides over 300K essential training data to enhance alignment .
Outcome:	The proposed method effectively aligns and integrates multi-scale knowledge of objects, including texts, coordinates, and images.

GroundingGPT: Language Enhanced Multi-modal Grounding Model (2024.acl-long)

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Zhaowei Li, Qi Xu, Dong Zhang, Hang Song, YiQing Cai, Qi Qi, Ran Zhou, Junting Pan, Zefeng Li, Vu Tu, Zhida Huang, Tao Wang

Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025.acl-long)

Challenge:	Existing multi-modal large language models focus on capturing global information while neglecting the fine-grained local information in multimodal inputs.
Approach:	They propose an end-to-end language enhanced multi-modal grounding model that performs fine-grained grounding tasks for image, video and audio.
Outcome:	The proposed model achieves impressive fine-grained understanding of multi-modal inputs while maintaining or improving its global comprehension capabilities.

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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu

CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation (2024.findings-acl)

Challenge:	Existing methods for enhancing LLM reasoning rely on supervisory signals . current methods rely heavily on outcome supervision and auxiliary reward models .
Approach:	They propose a gen-eralizable and purely unsupervised self-training framework to enhance LLM reasoning without supervision.
Outcome:	The proposed framework improves LLM reasoning without supervision without external supervision.

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Xiwen Liang, Liang Ma, Shanshan Guo, Jianhua Han, Hang Xu, Shikui Ma, Xiaodan Liang

MLeVLM: Improve Multi-level Progressive Capabilities based on Multimodal Large Language Model for Medical Visual Question Answering (2024.findings-acl)

Challenge:	Existing vision-and-language navigation methods do not incorporate environmental feedback into their decision-making processes.
Approach:	They propose a framework that incorporates environmental feedback into decision-making and a 3D simulator that renders realistic scenarios using Unreal Engine 5.
Outcome:	The proposed framework outperforms existing vision-and-language navigation methods in a zero-shot multi-task setting by 28.1% on average.

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Dexuan Xu, Yanyuan Chen, Jieyi Wang, Yue Huang, Hanpin Wang, Zhi Jin, Hongxing Wang, Weihua Yue, Jing He, Hang Li, Yu Huang

Multimodality

Challenge:	Existing MVQA models ignore multi-level progressive capabilities due to unspecific data and plain architecture.
Approach:	They propose a multi-level visual language model for medical visual question answering (MVQA) which covers multi- level questions and answers as well as reasoning processes from visual clues to semantic cognition.
Outcome:	The proposed model outperforms existing medical multimodal large language models on a multi-level instruction dataset and a feature alignment module.

Toward Optimal LLM Alignments Using Two-Player Games (2025.findings-emnlp)

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Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang, Yuanshun Yao, Xiaojun Xu, Zhaoran Wang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Yang Liu, Hang Li

Divide and Conquer: Legal Concept-guided Criminal Court View Generation (2024.findings-emnlp)

Challenge:	Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values.
Approach:	They propose an alignment method based on a two-agent game consisting of an adversarial agent and a defensive agent.
Outcome:	The proposed method improves on a two-agent game with an adversarial agent and a defensive agent.

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Qi Xu, Xiao Wei, Hang Yu, Qian Liu, Hao Fei

ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)

Challenge:	Existing methods for creating rationales for criminal cases do not pay enough attention to the important legal concepts.
Approach:	They propose a legal concept-guided court view generation framework that generates rationales based on predicted legal concepts . they first divide the court view into sub-views, then employ a solver and verifier to generate and select rationale.
Outcome:	The proposed model generates coherent and coherent court views on a real-world criminal case dataset.

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Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, Lingpeng Kong

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm (2022.acl-long)

Challenge:	Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios.
Approach:	They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset.
Outcome:	The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation.

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Shaoyi Huang, Dongkuan Xu, Ian Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding

Getting More Juice Out of Your Data: Hard Pair Refinement Enhances Visual-Language Models Without Extra Data (2025.naacl-long)

Challenge:	Conventional wisdom in pruning Transformer-based language models is that it reduces model expressiveness, but new research shows pruning increases risk of overfitting when performed at the fine-tuning phase.
Approach:	They propose to reduce pruning risk under pretrain-and-finetune paradigm . they propose to use knowledge distillation to improve pruning performance .
Outcome:	The proposed method outperforms the leading competitors on the GLUE benchmark.

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Haonan Wang, Minbin Huang, Runhui Huang, Lanqing Hong, Hang Xu, Tianyang Hu, Xiaodan Liang, Zhenguo Li, Hong Cheng, Kenji Kawaguchi

EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation (2021.findings-emnlp)

Challenge:	Contrastive Language-Image Pre-training (CLIP) is a standard for cross-modal image-text representation learning.
Approach:	They propose a framework that enhances pre-trained CLIP models by exploiting challenging text-image pairs within existing datasets.
Outcome:	The proposed framework improves CLIP models by exploiting text-image pairs in training.

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Chenhe Dong, Guangrun Wang, Hang Xu, Jiefeng Peng, Xiaozhe Ren, Xiaodan Liang

𝜙-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation (2025.acl-long)

Challenge:	Pre-trained language models have shown remarkable results on various NLP tasks.
Approach:	They propose to improve the feed-forward network (FFN) in BERT with a higher computational cost than improving the multi-head attention (MHA).
Outcome:	The proposed model is 6.9 smaller and 4.4 faster than BERTBASE and has competitive performances on GLUE and SQuAD Benchmarks.

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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Jun Liu, Qika Lin, Zhiyong Wu

Green & Sustainable NLP

Challenge:	Existing inference-time optimization strategies address the shortsightedness of auto-regressive generation, but the vast search space leads to excessive exploration and insufficient exploitation.
Approach:	They propose a decoding strategy that approximates two distributions via foresight and clustering to provide an efficient estimation of step value.
Outcome:	The proposed decoding strategy outperforms strong baselines in performance and efficiency.

Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)

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Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang

Reasoning

Challenge:	Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored.
Approach:	They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes.
Outcome:	The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks.

QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models (2025.emnlp-main)

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Wei Wang, Zhaowei Li, Qi Xu, YiQing Cai, Hang Song, Qi Qi, Ran Zhou, Zhida Huang, Tao Wang, Li Xiao