An automated framework for assessing how well LLMs cite relevant medical references
Kevin Z. L. Wu, Eric Q. Wu, Kevin Wei, Angela Zhang, Allison Casasola, Teresa Nguyen, Sith Riantawan, Patricia A. Shi, Daniel E. Ho, James Zou
Abstract
As large language models (LLMs) are increasingly used to address health-related queries, it is crucial that they support their conclusions with credible references. While models can cite sources, the extent to which these support claims remains unclear. To address this gap, we introduce SourceCheckup, an automated agent-based pipeline that evaluates the relevance and supportiveness of sources in LLM responses. We evaluate seven popular LLMs on a dataset of 800 questions and 58,000 pairs of statements and sources on data that represent common medical queries. Our findings reveal that between 50% and 90% of LLM responses are not fully supported, and sometimes contradicted, by the sources they cite. Even for GPT-4o with Web Search, approximately 30% of individual statements are unsupported, and nearly half of its responses are not fully supported. Independent assessments by doctors further validate these results. Our research underscores significant limitations in current LLMs to produce trustworthy medical references. Assessing the degree to which medical large language models reliably convey existing, trustworthy knowledge is crucial. This study introduces SourceCheckup, an automated framework revealing that large language models frequently cite medical references that do not fully support, or even contradict, their responses, showing significant gaps in reliability for clinical use.