Efficient Knowledge Infusion via KG-LLM Alignment

• URL: http://arxiv.org/abs/2406.03746v1
• Date: Thu, 6 Jun 2024 04:55:55 GMT
• Title: Efficient Knowledge Infusion via KG-LLM Alignment
• Authors: Zhouyu Jiang, Ling Zhong, Mengshu Sun, Jun Xu, Rui Sun, Hui Cai, Shuhan Luo, Zhiqiang Zhang,
• Abstract summary: Knowledge graph-retrievalaugmented method has been proven to be an effective and efficient technique for knowledge infusion. 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. We propose a three-stage KG-LLM alignment strategyto enhance the LLM's capability to utilize information from knowledge graphs.
• Score: 10.735490041033113
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 strategyto 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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