arXiv:2505.10113v3 Announce Type: replace
Abstract: In this paper, we introduce S-MedQA, an English medical question-answering (QA) dataset for benchmarking large language models (LLMs) in fine-grained clinical specialties. S-MedQA has over 20k examples, covers 15 medical specialties, and QA pairs can have multiple specialty annotations (e.g., when a question is cross-disciplinary), constructed with both machine and expert verification to maximize data availability. We use S-MedQA to investigate the role of clinical specialty data in the knowledge-intensive scenario of medical QA. Our results show that 1) training on data from a clinical specialty does not necessarily lead to best performance on that specialty, and 2) regardless of the specialty the LLM was fine-tuned on, token probabilities of clinically relevant terms increase consistently across all specialties. Thus, we hypothesize improvement gains are derived mostly from domain shifting (e.g., general to medical) rather than specialty-specific knowledge injection, and suggest rethinking the role of fine-tuning data in the medical domain.
