Healthy LLMs? Benchmarking LLM Knowledge of UK Government Public Health
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Authors: name: Joshua Harris; name: Fan Grayson; name: Felix Feldman; name: Timothy Laurence; name: Toby Nonnenmacher; name: Oliver Higgins; name: Leo Loman; name: Selina Patel; name: Thomas Finnie; name: Samuel Collins; name: Michael Borowitz
Published: 2025-05-09 13:42:59

Abstract: As Large Language Models (LLMs) become widely accessible, a detailed
understanding of their knowledge within specific domains becomes necessary for
successful real world use. This is particularly critical in public health,
where failure to retrieve relevant, accurate, and current information could
significantly impact UK residents. However, currently little is known about LLM
knowledge of UK Government public health information. To address this issue,
this paper introduces a new benchmark, PubHealthBench, with over 8000 questions
for evaluating LLMs' Multiple Choice Question Answering (MCQA) and free form
responses to public health queries, created via an automated pipeline. We also
release a new dataset of the extracted UK Government public health guidance
documents used as source text for PubHealthBench. Assessing 24 LLMs on
PubHealthBench we find the latest private LLMs (GPT-4.5, GPT-4.1 and o1) have a
high degree of knowledge, achieving >90% in the MCQA setup, and outperform
humans with cursory search engine use. However, in the free form setup we see
lower performance with no model scoring >75%. Therefore, whilst there are
promising signs that state of the art (SOTA) LLMs are an increasingly accurate
source of public health information, additional safeguards or tools may still
be needed when providing free form responses on public health topics.