Same Question, Different Source, Different Answer: Auditing Source-Dependence in Medical Multi-Source RAG
📰 ArXiv cs.AI
Learn to audit source-dependence in medical multi-source RAG systems to ensure accurate answers despite different sources
Action Steps
- Identify potential sources of variation in RAG outputs using multi-source institutional corpora
- Analyze inter-source relationships to detect source-dependence
- Evaluate RAG systems using metrics that account for source-dependence, such as agreement between sources
- Develop strategies to mitigate source-dependence, like source weighting or answer aggregation
- Test and refine RAG systems to ensure consistent and accurate outputs across different sources
Who Needs to Know This
NLP engineers and researchers working on RAG systems, particularly in the medical domain, can benefit from understanding source-dependence to improve system reliability
Key Insight
💡 Source-dependence is a critical axis of NLP evaluation that can impact RAG system performance, particularly in high-stakes domains like medicine
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🚨 Same question, different source, different answer? 🤔 Auditing source-dependence in medical RAG systems is crucial for accurate outputs 📊
Key Takeaways
Learn to audit source-dependence in medical multi-source RAG systems to ensure accurate answers despite different sources
Full Article
Title: Same Question, Different Source, Different Answer: Auditing Source-Dependence in Medical Multi-Source RAG
Abstract:
arXiv:2605.29084v1 Announce Type: cross Abstract: 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 m
Abstract:
arXiv:2605.29084v1 Announce Type: cross Abstract: 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 m
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