@inproceedings{yang-etal-2024-kg,
title = "{KG}-Rank: Enhancing Large Language Models for Medical {QA} with Knowledge Graphs and Ranking Techniques",
author = "Yang, Rui and
Liu, Haoran and
Marrese-Taylor, Edison and
Zeng, Qingcheng and
Ke, Yuhe and
Li, Wanxin and
Cheng, Lechao and
Chen, Qingyu and
Caverlee, James and
Matsuo, Yutaka and
Li, Irene",
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://aclanthology.org/2024.bionlp-1.13",
doi = "10.18653/v1/2024.bionlp-1.13",
pages = "155--166",
abstract = "Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities. However, their application in real clinical settings is challenging due to potential deviations from medical facts and inherent biases. In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) with ranking and re-ranking techniques, aiming to improve free-text question-answering (QA) in the medical domain. Specifically, upon receiving a question, we initially retrieve triplets from a medical KG to gather factual information. Subsequently, we innovatively apply ranking methods to refine the ordering of these triplets, aiming to yield more precise answers. To the best of our knowledge, KG-Rank is the first application of ranking models combined with KG in medical QA specifically for generating long answers. Evaluation of four selected medical QA datasets shows that KG-Rank achieves an improvement of over 18{\%} in the ROUGE-L score. Moreover, we extend KG-Rank to open domains, where it realizes a 14{\%} improvement in ROUGE-L, showing the effectiveness and potential of KG-Rank.",
}
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<abstract>Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities. However, their application in real clinical settings is challenging due to potential deviations from medical facts and inherent biases. In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) with ranking and re-ranking techniques, aiming to improve free-text question-answering (QA) in the medical domain. Specifically, upon receiving a question, we initially retrieve triplets from a medical KG to gather factual information. Subsequently, we innovatively apply ranking methods to refine the ordering of these triplets, aiming to yield more precise answers. To the best of our knowledge, KG-Rank is the first application of ranking models combined with KG in medical QA specifically for generating long answers. Evaluation of four selected medical QA datasets shows that KG-Rank achieves an improvement of over 18% in the ROUGE-L score. Moreover, we extend KG-Rank to open domains, where it realizes a 14% improvement in ROUGE-L, showing the effectiveness and potential of KG-Rank.</abstract>
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%0 Conference Proceedings
%T KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
%A Yang, Rui
%A Liu, Haoran
%A Marrese-Taylor, Edison
%A Zeng, Qingcheng
%A Ke, Yuhe
%A Li, Wanxin
%A Cheng, Lechao
%A Chen, Qingyu
%A Caverlee, James
%A Matsuo, Yutaka
%A Li, Irene
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F yang-etal-2024-kg
%X Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities. However, their application in real clinical settings is challenging due to potential deviations from medical facts and inherent biases. In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) with ranking and re-ranking techniques, aiming to improve free-text question-answering (QA) in the medical domain. Specifically, upon receiving a question, we initially retrieve triplets from a medical KG to gather factual information. Subsequently, we innovatively apply ranking methods to refine the ordering of these triplets, aiming to yield more precise answers. To the best of our knowledge, KG-Rank is the first application of ranking models combined with KG in medical QA specifically for generating long answers. Evaluation of four selected medical QA datasets shows that KG-Rank achieves an improvement of over 18% in the ROUGE-L score. Moreover, we extend KG-Rank to open domains, where it realizes a 14% improvement in ROUGE-L, showing the effectiveness and potential of KG-Rank.
%R 10.18653/v1/2024.bionlp-1.13
%U https://aclanthology.org/2024.bionlp-1.13
%U https://doi.org/10.18653/v1/2024.bionlp-1.13
%P 155-166
Markdown (Informal)
[KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques](https://aclanthology.org/2024.bionlp-1.13) (Yang et al., BioNLP-WS 2024)
ACL
- Rui Yang, Haoran Liu, Edison Marrese-Taylor, Qingcheng Zeng, Yuhe Ke, Wanxin Li, Lechao Cheng, Qingyu Chen, James Caverlee, Yutaka Matsuo, and Irene Li. 2024. KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 155–166, Bangkok, Thailand. Association for Computational Linguistics.