Papers by Shuheng Chen
MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval (2026.acl-demo)
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| Challenge: | MED-COPILOT is an interactive research prototype for evidence-aware clinical reasoning . large language models (LLMs) are prone to hallucinations and lack verifiable evidence grounding . |
| Approach: | They propose a system that integrates GraphRAG and semantic-keyword similar-patient retrieval to support transparent clinical reasoning. |
| Outcome: | The proposed system outperforms baseline and standard RAGs on clinical note completion and medical question answering. |
Aligning Retrieval with Reader Needs: Reader-Centered Passage Selection for Open-Domain Question Answering (2025.coling-main)
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Chunlei Xin, Shuheng Zhou, Xuanang Chen, Yaojie Lu, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing retrieval methods aim to gather relevant passages but fail to prioritize consistent and useful information for the reader. |
| Approach: | They propose a novel method which re-ranks passages based on the reader's prediction probability distribution and clusters passage according to the predicted answers. |
| Outcome: | The proposed method improves the quality of evidence passages under zero-shot scenarios. |
Debiasing In-Context Learning by Instructing LLMs How to Follow Demonstrations (2024.findings-acl)
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Lvxue Li, Jiaqi Chen, Xinyu Lu, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | In-context learning (ICL) has gained considerable attention due to its data efficiency and task adaptability. |
| Approach: | They propose to de-biase demonstration bias in in-context learning by focusing on semantic ambiguity induced by demonstrations and reducing the semantic hazard. |
| Outcome: | The proposed methods significantly improve performance on six datasets. |
Sparse Latents Steer Retrieval-Augmented Generation (2025.acl-long)
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Chunlei Xin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Xuanang Chen, Xinyan Guan, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
| Challenge: | In this study, we uncover interpretable latents that govern RAG behavior in large language models . Sparse Autoencoders are used to control large language model (LLM) behavior . |
| Approach: | They leverage Sparse Autoencoders within the LLaMA Scope to uncover latents that govern RAG behaviors. |
| Outcome: | The proposed model can be used to control large language models without architectural modifications. |