RAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering
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arXiv:2603.03541v1 Announce Type: cross
Abstract: Automated question-answering (QA) systems increasingly rely on retrieval-augmented generation (RAG) to ground large language models (LLMs) in authoritative medical knowledge, ensuring clinical accuracy and patient safety in Artificial Intelligence (AI) applications for healthcare. Despite progress in RAG evaluation, current benchmarks focus only on simple multiple-choice QA tasks and employ metrics that poorly capture the semantic precision requ
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--> Computer Science > Computation and Language arXiv:2603.03541 [Submitted on 3 Mar 2026] Title: RAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering Authors: Aswini Sivakumar , Vijayan Sugumaran , Yao Qiang View a PDF of the paper titled RAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering, by Aswini Sivakumar and 2 other authors View PDF HTML Abstract: Automated question-answering systems increasingly rely on retrieval-augmented generation to ground large language models in authoritative medical knowledge, ensuring clinical accuracy and patient safety in Artificial Intelligence applications for healthcare. Despite progress in RAG evaluation, current benchmarks focus only on simple multiple-choice QA tasks and employ metrics that poorly capture the semantic precision required for complex QA tasks. These approaches fail to diagnose whether an error stems from faulty retrieval or flawed generation, limiting developers from performing targeted improvement. To address this gap, we propose RAG-X, a diagnostic framework that evaluates the retriever and generator independently across a triad of QA tasks: information extraction, short-answer generation, and multiple-choice question answering. RAG-X introduces Context Utilization Efficiency metrics to disaggregate system success into interpretable quadrants, isolating verified grounding from deceptive accuracy. Our experiments reveal an ``Accuracy Fallacy", where a 14\% gap separates perceived system success from evidence-based grounding. By surfacing hidden failure modes, RAG-X offers the diagnostic transparency needed for safe and verifiable clinical RAG systems. Comments: 7 pages, 1 figure Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.03541 [cs.CL] (or arXiv:2603.03541v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.03541 Focus to learn more arXiv-issued DOI via DataCite (pen...
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