@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://aclanthology.org/2024.acl-srw.13",
pages = "140--145",
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{\%}.",
}
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<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%.</abstract>
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%0 Conference Proceedings
%T ReMAG-KR: Retrieval and Medically Assisted Generation with Knowledge Reduction for Medical Question Answering
%A Murali, Sidhaarth
%A S., Sowmya
%A R, Supreetha
%Y Fu, Xiyan
%Y Fleisig, Eve
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F murali-etal-2024-remag
%X 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%.
%U https://aclanthology.org/2024.acl-srw.13
%P 140-145
Markdown (Informal)
[ReMAG-KR: Retrieval and Medically Assisted Generation with Knowledge Reduction for Medical Question Answering](https://aclanthology.org/2024.acl-srw.13) (Murali et al., ACL 2024)
ACL