Large-Scale Biomedical RAG: Corpus Scaling, Document Representation, and Efficiency Trade-offs

Key points

• Retrieval collections were progressively expanded while retaining the relevant source documents, enabling consistent comparisons across corpus sizes.1
• We compared abstract, flat full-text, and hierarchical full-text representations across first-stage retrieval, reranking, and end-to-end multiple-choice question answering.1
• Hierarchical representation matched or outperformed flat full-text retrieval in most end-to-end comparisons while requiring 67% fewer indexed units and 44-56% less storage.1
• These findings show that corpus scale should be treated as an explicit evaluation variable in biomedical RAG, and that the most appropriate design depends on evidence format, model behavior, and operational constraints.1

Sources (1)

• [1]Large-Scale Biomedical RAG: Corpus Scaling, Document Representation, and Efficiency Trade-offs
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 01:08 PM

  We present a controlled, large-scale evaluation of how corpus scale, document representation, and reranking affect the effectiveness and efficiency of biomedical Retrieval-Augmented Generation (RAG) systems.

  Retrieval collections were progressively expanded while retaining the relevant source documents, enabling consistent comparisons across corpus sizes.

Extractive summary: sentences quoted from the sources.