23 papers
Let LLMs Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question Answering
Zaifu Zhan, Shuang Zhou, Rui Zhang
Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA). Method: We designed a multi-agent peer-r…
RuPLaR : Efficient Latent Compression of LLM Reasoning Chains with Rule-Based Priors From Multi-Step to One-Step
Xiaocheng Luo, Kang Wang, Zaifu Zhan +2
The Chain-of-Thought (CoT) paradigm, while enhancing the interpretability of Large Language Models (LLMs), is constrained by the inefficiencies and expressive limits of natural lan…
PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities
Kai Yu, Shuang Zhou, Yiran Song +9
Multimodal self-supervised pretraining offers a promising route to cancer prognosis by integrating histopathology whole-slide images, gene expression, and pathology reports, yet mo…
Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study
Zaifu Zhan, Mengyuan Cui, Rui Zhang
Large language models (LLMs) have achieved strong performance on medical question answering (medical QA), and chain-of-thought (CoT) prompting has further improved results by elici…
An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review
Zaifu Zhan, Yu Hou, Kai Yu +4
Rare diseases affect over 300 million people worldwide and are characterized by complex care pathways, limited clinical expertise, and substantial unmet communication needs through…
EpiScreen: Early Epilepsy Detection from Electronic Health Records with Large Language Models
Shuang Zhou, Kai Yu, Zaifu Zhan +5
Epilepsy and psychogenic non-epileptic seizures often present with similar seizure-like manifestations but require fundamentally different management strategies. Misdiagnosis is co…