Efficient Knowledge Infusion via KG-LLM Alignment
<|reference_start|>Efficient Knowledge Infusion via KG-LLM Alignment: 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.<|reference_end|>
@article{jiang2024efficient,
    title={Efficient Knowledge Infusion via KG-LLM Alignment},
    author={Zhouyu Jiang, Ling Zhong, Mengshu Sun, Jun Xu, Rui Sun, Hui Cai,
  Shuhan Luo, Zhiqiang Zhang},
    journal={arXiv preprint arXiv:2406.03746},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03746},
    primaryClass={cs.CL cs.AI}
}