Ensuring factual accuracy while maintaining the creative capabilities of
Large Language Model Agents (LMAs) poses significant challenges in the
development of intelligent agent systems. LMAs face prevalent issues such as
information hallucinations, catastrophic forgetting, and limitations in
processing long contexts when dealing with knowledge-intensive tasks. This
paper introduces a KG-RAG (Knowledge Graph-Retrieval Augmented Generation)
pipeline, a novel framework designed to enhance the knowledge capabilities of
LMAs by integrating structured Knowledge Graphs (KGs) with the functionalities
of LLMs, thereby significantly reducing the reliance on the latent knowledge of
LLMs. The KG-RAG pipeline constructs a KG from unstructured text and then
performs information retrieval over the newly created graph to perform KGQA
(Knowledge Graph Question Answering). The retrieval methodology leverages a
novel algorithm called Chain of Explorations (CoE) which benefits from LLMs
reasoning to explore nodes and relationships within the KG sequentially.
Preliminary experiments on the ComplexWebQuestions dataset demonstrate notable
improvements in the reduction of hallucinated content and suggest a promising
path toward developing intelligent systems adept at handling
knowledge-intensive tasks.

该论文介绍了一种名为 KG-RAG（知识图谱 - 检索增强生成）的框架，通过将结构化的知识图谱与 LLMs 的功能集成，显著减少对 LLMs 潜在知识的依赖，从而提高 LLM 的知识能力。通过使用一种名为 CoE（Chain of Explorations）的算法，利用 LLMs 的推理能力在知识图谱中顺序地探索节点和关系，该方法较之前取得了显著减少虚构内容的结果，为发展处理知识密集型任务的智能系统提供了有希望的路径。