Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering
Large language models (LLMs) achieve promising results on medical question answering benchmarks, yet their use in public health is constrained by hallucinations and the rapid evolution of official guidance. Retrieval-Augmented Generation (RAG) mitigates these risks by grounding responses in an explicitly maintained corpus, but end-to-end performance depends critically on retrieval configuration and on evaluation beyond multiple-choice formats. We extend PubHealthBench, a question answering (QA) benchmark of 7,929 questions derived from UK Government public health guidance, into a retrieval-augmented setting and systematically evaluate retrieval and generation choices. We compare dense, sp...
| Authors | Felix Feldman, Joshua Harris, Timothy Laurence et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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