Efficient Knowledge Infusion via KG - LLM Alignment (2024)

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.

## introduction recent advancements in large language models (llms), such as chatgpt, have demonstrated impressive capabilities in general-purpose content creation @xcite . nevertheless, their proficiency in domain-specific applications, particularly in the medical field, is notably constrained by insufficient knowledge @xcite @xcite . to enhance the domain-specific performance of llms, the primary strategies for knowledge infusion include two main approaches: continual pretraining on domain-specific corpora and retrievalaugmented method, which involves integrating external information into the models. compared to continual pre-training, the retrievalaugmented approach is gaining popularity in * both authors contributed equally to this work. some retrievalaugmented methods involve integrating llms with resources directly such as professional literature, news articles and tables through supervised finetuning @xcite . however, the knowledge required by the model may be scattered among vast amounts of data, and directly retrieving from raw data instances will introduce noise inevitably, preventing the model from effectively utilizing the information. to mitigate this issue, leveraging structured knowledge, especially knowledge graphs (kgs), is an effective method @xcite @xcite . however, the existing kg-retrieval-augmented methods still encounter two principal challenges. the first challenge pertains to knowledge mismatch. while many existing strategies utilize publicly available kgs for knowledge infusion, the knowledge demanded by domain-specific tasks is frequently of a highly specialized nature, leading to a substantial likelihood that the kg might not cover all the requisite information, or might even present gaps. the second challenge involves poor information compliance. the structured format of triples in kgs diverges from the free-flowing format of natural language @xcite and the target text often includes additional information that is not found in the triples. this disparity can lead to confusion within llms, which could result in outputs from the trained model that do not align with the information incorporated from the kg, particularly in scenarios with a scarcity of supervised examples. in this work, we construct a domain-specific corpus-based knowledge graph efficiently by llms and develop a knowledge infusion approach to enhance the ability of llms to utilize graph information, enabling them to generate comprehensive, logical, and low-hallucination responses. firstly, we train a knowledge extraction model based on an llm using a small amount of labeled data. then, we obtain a domain knowledge graph that resolves knowledge mismatch by performing extraction on unsupervised domain-specific corpora and reducing errors in the results through simple post-processing. subsequently, we propose a novel three-phase kg-llm alignment framework to optimize the exploitation of kg content by llms. the framework consists of the following stages: to simulate a realistic context where specialized annotations are scarce, we conduct experiments on limited-sample datasets constructed based on two public biomedical question answering datasets, bioasq @xcite and cmedqa @xcite . in summary, our main contributions are as follows: experimental results demonstrate that our method significantly outperforms the baselines. retrievalaugmented generation methods @xcite @xcite retrieve relevant information from an external database for the query and enable the llm to generate results using this information. chainrag @xcite focuses on addressing the problem of incorrect knowledge retrieved by information retrieval systems, which can mislead the llm or disrupt its reasoning chain through their interaction. while these methods enhance factuality, they also introduce new hallucinations. to address this challenge, webbrain @xcite incorporates both specific information and general knowledge, which are intertwined with text snippets and used as references to complete the task. llm-augmented kg construction. autokg @xcite proposes a framework for constructing a kg from unstructured documents using information extraction (ie) and internal semantic alignment. since the graphs constructed by ie typically suffer from edge sparsity and node redundancy, @xcite have applied contrastive pre-training and node clustering to overcome this issue. leveraging the capabilities of llms, @xcite designs prompts for various knowledge graph construction tasks. another line of research has aimed to extract knowledge from llms to construct kgs @xcite @xcite . additionally, pive @xcite utilizes iterative verification prompts to rectify errors in kgs generated by larger llms. ## methodology figure 1 illustrates the proposed framework, which is referred to as enhanced llm with knowledge (paralysis of the sciatic nerve，causes，the lack of strength in the waist area and the inability to sit ) (dysfunction in the autonomic nervous system causes muscle atrophy) (dysfunction in the autonomic nervous system，is responsible for，regulating blood flow ) (dysfunction the autonomic nervous system，is responsible for，nourishing muscles) the lack of strength in the waist area and the inability to sit still are the result of paralysis of the sciatic nerve, ……, as long as the nerves can be stimulated, self-recovery is possible. pre-learning and feedback (elpf). ## conclusions in this work, we propose a framework for efficiently infuse domain knowledge into llms. by employing efficient construction of domain knowledge graphs and a three-stage kg-llm alignment process, we address the issues of knowledge mismatch and poor information compliance. experiments demonstrate that our method significantly improves the quality of text generation and knowledge correctness in limited sample scenarios. we hope our work will provide insight into the challenge of connecting kg with llms in future exploration.