@inproceedings{turbitt-etal-2023-mdc,
    title = "{MDC} at {B}io{L}ay{S}umm Task 1: Evaluating {GPT} Models for Biomedical Lay Summarization",
    author = "Turbitt, Ois{\'i}n  and
      Bevan, Robert  and
      Aboshokor, Mouhamad",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.bionlp-1.65/",
    doi = "10.18653/v1/2023.bionlp-1.65",
    pages = "611--619",
    abstract = "This paper presents our approach to the BioLaySumm Task 1 shared task, held at the BioNLP 2023 Workshop. The effective communication of scientific knowledge to the general public is often limited by the technical language used in research, making it difficult for non-experts to comprehend. To address this issue, lay summaries can be used to explain research findings to non-experts in an accessible form. We conduct an evaluation of autoregressive language models, both general and specialized for the biomedical domain, to generate lay summaries from biomedical research article abstracts. Our findings demonstrate that a GPT-3.5 model combined with a straightforward few-shot prompt produces lay summaries that achieve significantly relevance and factuality compared to those generated by a fine-tuned BioGPT model. However, the summaries generated by the BioGPT model exhibit better readability. Notably, our submission for the shared task achieved 1st place in the competition."
}