@inproceedings{yoo-etal-2025-large,
    title = "Can Large Language Models Classify and Generate Antimicrobial Resistance Genes?",
    author = "Yoo, Hyunwoo  and
      Shin, Haebin  and
      Rosen, Gail",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Tsujii, Junichi",
    booktitle = "ACL 2025",
    month = aug,
    year = "2025",
    address = "Viena, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bionlp-1.21/",
    pages = "240--248",
    ISBN = "979-8-89176-275-6",
    abstract = "This study explores the application of generative Large Language Models (LLMs) in DNA sequence analysis, highlighting their advantages over encoder-based models like DNABERT2 and Nucleotide Transformer. While encoder models excel in classification, they struggle to integrate external textual information. In contrast, generative LLMs can incorporate domain knowledge, such as BLASTn annotations, to improve classification accuracy even without fine-tuning. We evaluate this capability on antimicrobial resistance (AMR) gene classification, comparing generative LLMs with encoder-based baselines. Results show that LLMs significantly enhance classification when supplemented with textual information. Additionally, we demonstrate their potential in DNA sequence generation, further expanding their applicability. Our findings suggest that LLMs offer a novel paradigm for integrating biological sequences with external knowledge, bridging gaps in traditional classification methods."
}