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

Adrián Sánchez-Freire, Gonzalo Pérez-Gómez, Aitziber Atutxa, Ander Barrena

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. We compared abstract, flat full-text, and hierarchical full-text representations across first-stage retrieval, reranking, and end-to-end multiple-choice question answering. Configurations selected on development data were evaluated on five external biomedical benchmarks and a held-out test set, while retrieval latency and index storage were measured to quantify operational cost. Performance patterns observed with small or controlled collections did not consistently persist as collections grew, and the usefulness of reranking depended on the corpus and retrieval setting. RAG improved three of four model configurations across the external benchmarks, with mean gains of up to 9.6 percentage points, and improved all four configurations on the held-out test set by 10.2-30.3 points. Abstract retrieval performed best when reference…