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.

针对大型语言模型（LLMs）中领域特定知识匮乏的问题，本文提出了一种基于知识图谱检索增强的方法，通过利用少量标记样本和大规模语料库，构建了领域特定的知识图，并通过三阶段的知识图与 LLM 对齐策略增强了 LLM 利用知识图的能力，实验证明我们的方法优于现有基准模型。