@inproceedings{to-etal-2024-deakinnlp,
    title = "{D}eakin{NLP} at {B}io{L}ay{S}umm: Evaluating Fine-tuning Longformer and {GPT}-4 Prompting for Biomedical Lay Summarization",
    author = "To, Huy Quoc  and
      Liu, Ming  and
      Huang, Guangyan",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2024.bionlp-1.67/",
    doi = "10.18653/v1/2024.bionlp-1.67",
    pages = "748--754",
    abstract = "This paper presents our approaches for the BioLaySumm 2024 Shared Task. We evaluate two methods for generating lay summaries based on biomedical articles: (1) fine-tuning the Longformer-Encoder-Decoder (LED) model, and (2) zero-shot and few-shot prompting on GPT-4. In the fine-tuning approach, we individually fine-tune the LED model using two datasets: PLOS and eLife. This process is conducted under two different settings: one utilizing 50{\%} of the training dataset, and the other utilizing the entire 100{\%} of the training dataset. We compare the results of both methods with GPT-4 in zero-shot and few-shot prompting. The experiment results demonstrate that fine-tuning with 100{\%} of the training data achieves better performance than prompting with GPT-4. However, under data scarcity circumstances, prompting GPT-4 seems to be a better solution."
}