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.
View on arXiv@article{harris2025_2505.06046,
title={ Healthy LLMs? Benchmarking LLM Knowledge of UK Government Public Health Information },
author={ Joshua Harris and Fan Grayson and Felix Feldman and Timothy Laurence and Toby Nonnenmacher and Oliver Higgins and Leo Loman and Selina Patel and Thomas Finnie and Samuel Collins and Michael Borowitz },
journal={arXiv preprint arXiv:2505.06046},
year={ 2025 }
}