@inproceedings{murali-etal-2024-remag,
    title = "{R}e{MAG}-{KR}: Retrieval and Medically Assisted Generation with Knowledge Reduction for Medical Question Answering",
    author = "Murali, Sidhaarth  and
      S., Sowmya  and
      R, Supreetha",
    editor = "Fu, Xiyan  and
      Fleisig, Eve",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.acl-srw.13/",
    doi = "10.18653/v1/2024.acl-srw.13",
    pages = "62--67",
    ISBN = "979-8-89176-097-4",
    abstract = "Large Language Models (LLMs) have significant potential for facilitating intelligent end-user applications in healthcare. However, hallucinations remain an inherent problem with LLMs, making it crucial to address this issue with extensive medical knowledge and data. In this work, we propose a Retrieve-and-Medically-Augmented-Generation with Knowledge Reduction (ReMAG-KR) pipeline, employing a carefully curated knowledge base using cross-encoder re-ranking strategies. The pipeline is tested on medical MCQ-based QA datasets as well as general QA datasets. It was observed that when the knowledge base is reduced, the model{'}s performance decreases by 2-8{\%}, while the inference time improves by 47{\%}."
}