MedRGB, an evaluation framework, proposed in this paper, stress-tests medical RAG systems by throwing noise, insufficient data, and factual errors at them.
This framework reveals how medical RAG systems handle real-world messiness in retrieved documents.
Want to know if your medical RAG system can spot fake news? MedRGB has the answer.
It addresses critical gaps in existing benchmarks by evaluating RAG systems across four practical scenarios: standard retrieval, sufficiency testing, information integration, and robustness against misinformation.
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https://arxiv.org/abs/2411.09213
🔍 Original Problem:
Current medical RAG systems lack thorough evaluation in practical scenarios where reliability and trustworthiness are crucial. Existing benchmarks focus mainly on basic retrieval-answer settings, missing critical aspects needed for real-world medical applications.
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🛠️ Solution in this Paper:
→ MedRGB evaluates RAG systems across four medical datasets using both offline medical corpus and online search retrieval.
→ The framework tests systems in four scenarios: standard RAG performance, ability to handle insufficient information, capability to integrate multiple pieces of information, and resilience against factual errors.
→ Each test scenario uses varying levels of signal-to-noise ratios to measure system reliability.
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💡 Key Insights:
→ Even state-of-the-art LLMs struggle with noise detection and factual error identification
→ Adding more retrieved documents doesn't always improve performance
→ Models perform better with offline medical corpus retrieval compared to online search
→ Small amounts of noise can sometimes improve model performance
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📊 Results:
→ GPT-4o achieved highest accuracy across most settings
→ Domain-specific models like PMC-Llama-13b showed mixed results
→ Llama-3-70b demonstrated superior noise detection capabilities
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