MedTrust-RAG: Evidence Verification and Trust Alignment for Biomedical Question Answering

• URL: http://arxiv.org/abs/2510.14400v2
• Date: Sat, 18 Oct 2025 11:57:45 GMT
• Title: MedTrust-RAG: Evidence Verification and Trust Alignment for Biomedical Question Answering
• Authors: Yingpeng Ning, Yuanyuan Sun, Ling Luo, Yanhua Wang, Yuchen Pan, Hongfei Lin,
• Abstract summary: We propose MedTrust-Guided Iterative RAG, a framework designed to enhance factual consistency and hallucinations in medical QA.<n>First, it enforces citation-aware reasoning by requiring all generated content to be explicitly grounded in retrieved medical documents.<n>Second, it employs an iterative retrieval-verification process, where a verification agent assesses evidence adequacy.
• Score: 21.855579328680246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biomedical question answering (QA) requires accurate interpretation of complex medical knowledge. Large language models (LLMs) have shown promising capabilities in this domain, with retrieval-augmented generation (RAG) systems enhancing performance by incorporating external medical literature. However, RAG-based approaches in biomedical QA suffer from hallucinations due to post-retrieval noise and insufficient verification of retrieved evidence, undermining response reliability. We propose MedTrust-Guided Iterative RAG, a framework designed to enhance factual consistency and mitigate hallucinations in medical QA. Our method introduces three key innovations. First, it enforces citation-aware reasoning by requiring all generated content to be explicitly grounded in retrieved medical documents, with structured Negative Knowledge Assertions used when evidence is insufficient. Second, it employs an iterative retrieval-verification process, where a verification agent assesses evidence adequacy and refines queries through Medical Gap Analysis until reliable information is obtained. Third, it integrates the MedTrust-Align Module (MTAM) that combines verified positive examples with hallucination-aware negative samples, leveraging Direct Preference Optimization to reinforce citation-grounded reasoning while penalizing hallucination-prone response patterns.

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