Papers by Huixue Zhou
MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills (2026.eacl-long)
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Zonghai Yao, Zihao Zhang, Chaolong Tang, Xingyu Bian, Youxia Zhao, Zhichao Yang, Junda Wang, Huixue Zhou, Won Seok Jang, Feiyun Ouyang, Hong Yu
| Challenge: | Current clinical LLM benchmarks fail to evaluate advanced clinical skills in AI and large language models (LLMs). |
| Approach: | They propose a framework to evaluate large language models (LLMs) using two instruction-following tasks designed to reflect real clinical scenarios. |
| Outcome: | The proposed framework evaluates LLMs through two instruction-following tasks designed to reflect real clinical scenarios. |
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)
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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen
| Challenge: | Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy. |
| Approach: | They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges. |
| Outcome: | The proposed framework reduces retrieval time while maintaining high model performance. |
NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes (2024.findings-acl)
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| Challenge: | NoteChat is a cooperative multi-agent framework for generating patient-physician dialogues . evaluator finds it outperforms state-of-the-art models for generating clinical notes . clinical documentation is largely done by physicians at both steps . |
| Approach: | They propose a cooperative multi-agent framework leveraging Large Language Models to generate patient-physician dialogues. |
| Outcome: | The proposed framework outperforms state-of-the-art models for generating clinical notes . it can engage patients directly and help clinical documentation, a leading cause of physician burnout . |
DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains (2025.findings-emnlp)
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| Challenge: | Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs . current approaches encode graph context in textual form, which fails to exploit its potential . |
| Approach: | a new method is proposed to predict missing triples in knowledge graphs by leveraging existing triples and textual information. |
| Outcome: | The proposed model learns structural embeddings and logical rules within the KG and extracts a subgraph for each query guided by the learned rules. |
MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback (2025.naacl-long)
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| Challenge: | Generating multiple-choice questions (MCQG) for professional exams is challenging due to outdated knowledge, hallucination issues, and prompt sensitivity. |
| Approach: | They propose a framework for converting medical cases into high-quality USMLE-style questions using a self-refine-based framework. |
| Outcome: | The proposed framework improves human expert satisfaction regarding quality and difficulty of medical questions. |