@inproceedings{salvi-etal-2025-towards,
    title = "Towards Understanding {LLM}-Generated Biomedical Lay Summaries",
    author = "Salvi, Rohan Charudatt  and
      Panigrahi, Swapnil  and
      Jain, Dhruv  and
      Yadav, Shweta  and
      Akhtar, Md. Shad",
    editor = "Ananiadou, Sophia  and
      Demner-Fushman, Dina  and
      Gupta, Deepak  and
      Thompson, Paul",
    booktitle = "Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.cl4health-1.22/",
    pages = "260--268",
    ISBN = "979-8-89176-238-1",
    abstract = "In this paper, we investigate using large language models to generate accessible lay summaries of medical abstracts, targeting non-expert audiences. We assess the ability of models like GPT-4 and LLaMA 3-8B-Instruct to simplify complex medical information, focusing on layness, comprehensiveness, and factual accuracy. Utilizing both automated and human evaluations, we discover that automatic metrics do not always align with human judgments. Our analysis highlights the potential benefits of developing clear guidelines for consistent evaluations conducted by non-expert reviewers. It also points to areas for improvement in the evaluation process and the creation of lay summaries for future research."
}