KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models
Submission Type: Regular Short Paper
Submission Track: NLP Applications
Submission Track 2: Theme Track: Large Language Models and the Future of NLP
Keywords: Large Language Model, Knowledge Graph, Reasoning, Question Answering, Fact Verification
TL;DR: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models
Abstract: 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.
Submission Number: 5059
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