FactPICO: Factuality Evaluation for Plain Language Summarization of
  Medical Evidence
Sebastian Antony Joseph Lily Chen Jan Trienes Hannah Louisa Göke Monika Coers Wei Xu Byron C Wallace Junyi Jessy Li
Plain language summarization with LLMs can be useful for improving textual
accessibility of technical content. But how factual are these summaries in a
high-stakes domain like medicine? This paper presents FactPICO, a factuality
benchmark for plain language summarization of medical texts describing
randomized controlled trials (RCTs), which are the basis of evidence-based
medicine and can directly inform patient treatment. FactPICO consists of 345
plain language summaries of RCT abstracts generated from three LLMs (i.e.,
GPT-4, Llama-2, and Alpaca), with fine-grained evaluation and natural language
rationales from experts. We assess the factuality of critical elements of RCTs
in those summaries: Populations, Interventions, Comparators, Outcomes (PICO),
as well as the reported findings concerning these. We also evaluate the
correctness of the extra information (e.g., explanations) added by LLMs. Using
FactPICO, we benchmark a range of existing factuality metrics, including the
newly devised ones based on LLMs. We find that plain language summarization of
medical evidence is still challenging, especially when balancing between
simplicity and factuality, and that existing metrics correlate poorly with
expert judgments on the instance level.