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.

翻译：在大语言模型智能体（LMAs）的开发过程中，确保事实准确性同时维持其创造性能力是重大挑战。在处理知识密集型任务时，LMAs面临信息幻觉、灾难性遗忘以及长上下文处理能力受限等普遍问题。本文提出KG-RAG（知识图谱-检索增强生成）流水线，这是一个创新框架，通过将结构化知识图谱（KG）与大语言模型（LLM）功能相结合来增强LMAs的知识能力，从而显著降低对LLM潜在知识的依赖。该KG-RAG流水线从非结构化文本中构建知识图谱，随后对新生成的图谱进行信息检索以实现KGQA（知识图谱问答）。其检索方法采用名为“探索链”（CoE）的新型算法，该算法利用LLM的推理能力在知识图谱中顺序探索节点与关系。在ComplexWebQuestions数据集上的初步实验表明，该方法在减少幻觉内容方面取得显著改进，为开发能够处理知识密集型任务的智能系统提供了可行路径。