Same Question, Different Source, Different Answer: Auditing Source-Dependence in Medical Multi-Source RAG

Yubo Li, Rema Padman, Ramayya Krishnan

Same Question, Different Source, Different Answer: Auditing Source-Dependence in Medical Multi-Source RAG: 1 upvotes on Hugging Face Daily Papers, #58 of 72 papers on 2026-06-02. Day-by-day upvote history.

A retrieval-augmented generation (RAG) system deployed over a multi-author institutional corpus can give a different answer to the same question depending on which source it retrieves -- a failure mode the dominant single-gold-answer paradigm cannot diagnose. We argue that source-dependence is a missing axis of NLP evaluation, and that auditing it means shifting the unit of evaluation from answer correctness to the inter-source relationship. We make this concrete in transplant patient education, where institutional sources demonstrably disagree, releasing three artefacts: TransplantQA, a benchmark of real patient questions, each answered by grounding generation in multiple institutional handbooks as candidate sources; HERO-QA, a hierarchical retrieval strategy that grounds and audits each answer; and a structured-output judge that scores inter-source relationships on a validated 5-label taxonomy. At scale, better retrieval reveals far more disagreement than prior estimates suggested -- understating its prevalence, not its intensity. The framework is domain-agnostic and transfers to legal and educational RAG: measuring source-dependence is a responsibility for deployed multi-source NLP generally.

Paper page on Hugging Face · arXiv

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