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Retrieval-augmented generation (RAG) improves biomedical question answering. MedDiscover, a new benchmark for metabolomics literature, shows MedCPT outperforms Ada in answer correctness, enhancing RAG system evaluation.

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Metabolomics

Background:

  • Retrieval-augmented generation (RAG) enhances biomedical question answering.
  • Domain-specific retriever evaluation needs transparency and reproducibility.
  • Metabolomics and metabolic disorders literature present unique challenges for RAG.

Purpose of the Study:

  • Introduce MedDiscover, an open-source RAG implementation and benchmark for metabolomics.
  • Provide a reproducible evaluation framework for domain-specific RAG systems.
  • Compare the performance of different retrieval methods (Ada, MedCPT) in a RAG context.

Main Methods:

  • Developed MedDiscover with a two-tier dataset: Gold (expert-curated) and Silver (ICD-coded).
  • Created expert-curated and synthetic question-answer pairs with retrievability metadata.
  • Evaluated RAG performance on grounding, faithfulness, context recall/precision, answer relevancy, and correctness.

Main Results:

  • Retrieval augmentation improved grounding on the Gold set compared to a non-RAG baseline.
  • MedCPT and Ada demonstrated comparable retrieval-centric performance on the Silver benchmark.
  • MedCPT significantly outperformed Ada in answer correctness on the Silver benchmark.

Conclusions:

  • MedDiscover provides a robust benchmark for evaluating RAG in specialized biomedical domains.
  • The study highlights MedCPT's superior performance in generating correct answers for metabolomics queries.
  • Open-sourced code, scripts, and document lists promote reproducible research in biomedical RAG.