Large language models (LLMs) have made significant strides in various tasks, yet they often struggle with complex reasoning and exhibit poor performance in scenarios where knowledge traceability, timeliness, and accuracy are crucial. To address these limitations, we present Think-on-Graph (ToG), a novel framework that leverages knowledge graphs to enhance LLMs' ability for deep and responsible reasoning. By employing ToG, we can identify entities relevant to a given question and conduct exploration and reasoning to retrieve related triples from an external knowledge database. This iterative procedure generates multiple reasoning pathways consisting of sequentially connected triplets until sufficient information is gathered to answer the question or the maximum depth is reached. Through experiments on complex multi-hop reasoning question-answering tasks, we demonstrate that ToG outperforms existing methods, effectively addressing the aforementioned limitations of LLMs without incurring additional training costs.

翻译：大型语言模型（LLMs）在各领域任务中取得了显著进展，但在需要知识可追溯性、时效性和准确性的场景中，仍常面临复杂推理能力不足和表现欠佳的问题。为解决这些局限，我们提出Think-on-Graph（ToG）——一种创新框架，通过利用知识图谱增强LLMs的深度与负责任推理能力。应用ToG时，可识别给定问题中的相关实体，并通过探索与推理从外部知识数据库中检索关联三元组。这一迭代过程生成由顺序连接的三元组构成的多条推理路径，直至收集到足够信息来回答问题或达到最大深度。通过在多跳复杂推理问答任务上的实验，我们证明ToG无需额外训练成本即可超越现有方法，有效解决LLMs的上述局限性。