Papers by Mu Huang
MAKAR: a Multi-Agent framework based Knowledge-Augmented Reasoning for Grounded Multimodal Named Entity Recognition (2025.emnlp-main)
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Xinkui Lin, Yuhui Zhang, Yongxiu Xu, Kun Huang, Hongzhang Mu, Yubin Wang, Gaopeng Gou, Li Qian, Li Peng, Wei Liu, Jian Luan, Hongbo Xu
| Challenge: | Existing methods for GMNER fail to address semantic ambiguity caused by polysemy and long-tail distribution of datasets. |
| Approach: | They propose a framework for Grounded Multimodal Named Entity Recognition that leverages a Multimodal Large Language Model to address semantic ambiguity. |
| Outcome: | Extensive experiments show that the proposed framework outperforms existing methods on two benchmark datasets. |
DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) can replicate insecure patterns from training data. |
| Approach: | They propose a framework that leverages distributed security-relevant cues by aggregating representations from multiple upper layers via an attention-based module. |
| Outcome: | Experiments show that the framework improves the secure-and-correct generation rate by 11.9% over baselines. |
MMXU: A Multi-Modal and Multi-X-ray Understanding Dataset for Disease Progression (2025.findings-acl)
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| Challenge: | Existing datasets and models fail to consider critical aspects of medical diagnostics, authors argue . MMXU enables multi-image questions incorporating both current and historical patient data. |
| Approach: | They propose a dataset for MedVQA that focuses on identifying changes in specific regions between two patient visits. |
| Outcome: | The proposed dataset improves diagnostic accuracy by 20% by integrating historical data. |
KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)
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Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang
| Challenge: | Retrieval-augmented generation (RAG) is employed to tackle these challenges . a Knowledge Boundary Model (KBM) is used to express the known/unknown of a given question . |
| Approach: | They propose a Knowledge Boundary Model to express the known/unknown of a given question . they find that not all questions need to trigger RAG to improve performance . |
| Outcome: | The proposed model reduces time and computational costs by retrieving parts of unknown knowledge . the proposed model can express the known/unknown of a given question and determine whether a RAG needs to be triggered . |
Biomedical Event Extraction with Hierarchical Knowledge Graphs (2020.findings-emnlp)
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| Challenge: | Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. |
| Approach: | They propose to integrate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model using Graph Edge-conditioned Attention Networks and hierarchical graph representation. |
| Outcome: | The proposed approach achieves 1.41% F1 and 3.19% F1 improvements on the BioNLP 2011 GENIA Event Extraction task. |
Training Language Model to Critique for Better Refinement (2025.findings-acl)
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Tianshu Yu, Chao Xiang, Mingchuan Yang, Pei Ke, Bosi Wen, Cunxiang Wang, Jiale Cheng, Li Zhang, Xinyu Mu, Chuxiong Sun, Minlie Huang
| Challenge: | Large language models (LLMs) have remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. |
| Approach: | They propose a framework to train critic models using refinement signals to generate feedback loops where critiques guide the model in refining its responses. |
| Outcome: | The proposed framework outperforms traditional methods and open-source models in terms of critique quality and refinement outcomes. |
SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment (2025.coling-main)
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Yuchun Fan, Yongyu Mu, YiLin Wang, Lei Huang, Junhao Ruan, Bei Li, Tong Xiao, Shujian Huang, Xiaocheng Feng, Jingbo Zhu
| Challenge: | Large language models (LLMs) have demonstrated significant improvements in reasoning abilities, but these improvements are primarily focused on English, leading to inferior performance in non-English scenarios. |
| Approach: | They propose a multilingual reasoning alignment approach that fine-tunes the layers responsible for multilingual comprehension in one stage. |
| Outcome: | The proposed method fine-tunes 6 of the 9 layers responsible for multilingual comprehension, while reducing training time by 4.1-11.9 compared to the two-stage method. |
EventPlus: A Temporal Event Understanding Pipeline (2021.naacl-demos)
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Mingyu Derek Ma, Jiao Sun, Mu Yang, Kung-Hsiang Huang, Nuan Wen, Shikhar Singh, Rujun Han, Nanyun Peng
| Challenge: | Event information is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events. |
| Approach: | They propose a temporal event understanding pipeline that integrates state-of-the-art components. |
| Outcome: | The proposed pipeline can be easily adapted to other domains, including biomedical domains. |
Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition (2023.emnlp-main)
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| Challenge: | Existing methods for identifying discourse relations without explicit connectives are limited by the availability of annotated data. |
| Approach: | They propose a method that injects knowledge relevant to discourse relation into pre-trained language models through prompt-based connective prediction. |
| Outcome: | The proposed method achieves outstanding performance against the current state-of-the-art models. |
Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Large vision-language models exhibit an imbalance in multilingual capabilities . |
| Approach: | They propose a training recipe that achieves efficient multilingual enhancement for LVLMs by Precise Language Specific layers fine-tuning. |
| Outcome: | The proposed training recipe achieves efficient multilingual enhancement for LVLMs by fine-tuning language specific layers. |
DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models (2026.findings-acl)
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Yakun Zhu, Zhongzhen Huang, Linjie Mu, Yutong Huang, Wei Nie, Jiaji Liu, Shaoting Zhang, Pengfei Liu, Xiaofan Zhang
| Challenge: | Existing medical benchmarks for diagnostic reasoning are limited in their ability to perform complex tasks. |
| Approach: | They propose to benchmark diagnostic capabilities of large language models to assess their accuracy and generalization bottlenecks. |
| Outcome: | The proposed model achieves 45.82%, 31.09%, and 17.79% accuracy, compared to current models, o3-mini, e1 and DeepSeek-R1 . |
Generating Responses with a Specific Emotion in Dialog (P19-1)
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| Challenge: | EmoDS can express emotions in both ways, but it is difficult to scale to large datasets. |
| Approach: | They propose an emotional dialog system that can express emotions in both ways . they use strong emotional words and neutral words to increase the intensity of emotions . |
| Outcome: | The proposed system performs better than baselines in BLEU, diversity and quality of emotional expression. |
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario (2024.findings-emnlp)
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| Challenge: | Existing tool learning methods focus on selecting the most effective tool from a wide array of options, often overlooking cost-effectiveness. |
| Approach: | They propose to predict query performance and cost required to accomplish a given task . they then assign queries to the optimal tools in a cost-effective manner . |
| Outcome: | The proposed method achieves higher performance at lower cost compared to baseline approaches. |