5 papers · 1 filter
What Makes a Medical Checker Trainable? Diagnosing Signal Collapse and Reward Hacking in Checker-Guided RAG for Biomedical QA
Yuelyu Ji, Min Gu Kwak, Hang Zhang +3
Medical RAG needs evidence-grounded claims, so plugging a claim-level NLI checker into retrieval-augmented RL is intuitive. \textbf{We find that the checker's \emph{output distribu…
MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generation
Yuelyu Ji, Min Gu Kwak, Hang Zhang +3
Biomedical retrieval-augmented generation (RAG) can ground LLM answers in medical literature, yet long-form outputs often contain isolated unsupported or contradictory claims with…
DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QA
Yuelyu Ji, Hang Zhang, Shiven Verma +4
We propose DeepRAG, a novel framework that integrates DeepSeek hierarchical question decomposition capabilities with RAG Gym unified retrieval-augmented generation optimization usi…
Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems
Yuelyu Ji, Hang Zhang, Yanshan Wang
Medical Question Answering systems based on Retrieval Augmented Generation is promising for clinical decision support because they can integrate external knowledge, thus reducing i…
RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts
Yuelyu Ji, Zhuochun Li, Rui Meng +7
This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research understandable to laymen through advanced Natural Language Processing (NLP) techni…