同一問題，不同來源，不同答案：審核醫療多源RAG中的來源依賴性

摘要

在一個由多作者機構語料庫部署的檢索增強生成（RAG）系統中，根據檢索到的不同來源，同一問題可能得到不同答案——這種失效模式是主流的單一黃金答案範式所無法診斷的。我們認為，來源依賴性是自然語言處理評測中缺失的一個軸向，而對其進行審計意味著將評測單位從答案正確性轉向來源間關係。我們在移植病患教育領域將此具體化，該領域中機構來源確實存在歧見，並釋出三項產物：TransplantQA，一個包含真實病患問題的基準測試，每個問題都以多個機構手冊作為候選來源進行生成；HERO-QA，一種分層檢索策略，為每個答案提供基礎並進行審計；以及一個結構化輸出評判器，根據經過驗證的5標籤分類法對來源間關係進行評分。在大規模應用中，更佳的檢索機制所揭示的分歧遠超先前估計——其重點在於低估了分歧的普遍性，而非分歧的強度。該框架不受領域限制，可遷移至法律與教育領域的RAG系統：對來源依賴性的衡量，是部署多來源自然語言處理系統時普遍應承擔的責任。

English

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.