KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models
This addresses the challenge of integrating structured and unstructured data processing for researchers and practitioners in AI, though it is incremental as it builds on existing LLM and KG methods.
The paper tackles the problem of applying large language models to complex reasoning tasks on knowledge graphs, proposing the KG-GPT framework, which achieves competitive performance on benchmarks like fact verification and KGQA, even outperforming some fully-supervised models.
While large language models (LLMs) have made considerable advancements in understanding and generating unstructured text, their application in structured data remains underexplored. Particularly, using LLMs for complex reasoning tasks on knowledge graphs (KGs) remains largely untouched. To address this, we propose KG-GPT, a multi-purpose framework leveraging LLMs for tasks employing KGs. KG-GPT comprises three steps: Sentence Segmentation, Graph Retrieval, and Inference, each aimed at partitioning sentences, retrieving relevant graph components, and deriving logical conclusions, respectively. We evaluate KG-GPT using KG-based fact verification and KGQA benchmarks, with the model showing competitive and robust performance, even outperforming several fully-supervised models. Our work, therefore, marks a significant step in unifying structured and unstructured data processing within the realm of LLMs.