@inproceedings{jiang-etal-2024-efficient,
title = "Efficient Knowledge Infusion via {KG}-{LLM} Alignment",
author = "Jiang, Zhouyu and
Zhong, Ling and
Sun, Mengshu and
Xu, Jun and
Sun, Rui and
Cai, Hui and
Luo, Shuhan and
Zhang, Zhiqiang",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.176",
doi = "10.18653/v1/2024.findings-acl.176",
pages = "2986--2999",
abstract = "To tackle the problem of domain-specific knowledge scarcity within large language models (LLMs), knowledge graph-retrievalaugmented method has been proven to be an effective and efficient technique for knowledge infusion. However, existing approaches face two primary challenges: knowledge mismatch between public available knowledge graphs and the specific domain of the task at hand, and poor information compliance of LLMs with knowledge graphs. In this paper, we leverage a small set of labeled samples and a large-scale corpus to efficiently construct domain-specific knowledge graphs by an LLM, addressing the issue of knowledge mismatch. Additionally, we propose a three-stage KG-LLM alignment strategy to enhance the LLM{'}s capability to utilize information from knowledge graphs. We conduct experiments with a limited-sample setting on two biomedical question-answering datasets, and the results demonstrate that our approach outperforms existing baselines.",
}
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<abstract>To tackle the problem of domain-specific knowledge scarcity within large language models (LLMs), knowledge graph-retrievalaugmented method has been proven to be an effective and efficient technique for knowledge infusion. However, existing approaches face two primary challenges: knowledge mismatch between public available knowledge graphs and the specific domain of the task at hand, and poor information compliance of LLMs with knowledge graphs. In this paper, we leverage a small set of labeled samples and a large-scale corpus to efficiently construct domain-specific knowledge graphs by an LLM, addressing the issue of knowledge mismatch. Additionally, we propose a three-stage KG-LLM alignment strategy to enhance the LLM’s capability to utilize information from knowledge graphs. We conduct experiments with a limited-sample setting on two biomedical question-answering datasets, and the results demonstrate that our approach outperforms existing baselines.</abstract>
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%0 Conference Proceedings
%T Efficient Knowledge Infusion via KG-LLM Alignment
%A Jiang, Zhouyu
%A Zhong, Ling
%A Sun, Mengshu
%A Xu, Jun
%A Sun, Rui
%A Cai, Hui
%A Luo, Shuhan
%A Zhang, Zhiqiang
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F jiang-etal-2024-efficient
%X To tackle the problem of domain-specific knowledge scarcity within large language models (LLMs), knowledge graph-retrievalaugmented method has been proven to be an effective and efficient technique for knowledge infusion. However, existing approaches face two primary challenges: knowledge mismatch between public available knowledge graphs and the specific domain of the task at hand, and poor information compliance of LLMs with knowledge graphs. In this paper, we leverage a small set of labeled samples and a large-scale corpus to efficiently construct domain-specific knowledge graphs by an LLM, addressing the issue of knowledge mismatch. Additionally, we propose a three-stage KG-LLM alignment strategy to enhance the LLM’s capability to utilize information from knowledge graphs. We conduct experiments with a limited-sample setting on two biomedical question-answering datasets, and the results demonstrate that our approach outperforms existing baselines.
%R 10.18653/v1/2024.findings-acl.176
%U https://aclanthology.org/2024.findings-acl.176
%U https://doi.org/10.18653/v1/2024.findings-acl.176
%P 2986-2999
Markdown (Informal)
[Efficient Knowledge Infusion via KG-LLM Alignment](https://aclanthology.org/2024.findings-acl.176) (Jiang et al., Findings 2024)
ACL
- Zhouyu Jiang, Ling Zhong, Mengshu Sun, Jun Xu, Rui Sun, Hui Cai, Shuhan Luo, and Zhiqiang Zhang. 2024. Efficient Knowledge Infusion via KG-LLM Alignment. In Findings of the Association for Computational Linguistics: ACL 2024, pages 2986–2999, Bangkok, Thailand. Association for Computational Linguistics.