Exploring Large Language Models for Knowledge Graph Completion
This addresses the incompleteness issue in knowledge graphs, which is crucial for AI tasks, but it is incremental as it applies existing LLM techniques to a known problem.
The paper tackles the problem of incomplete knowledge graphs by using Large Language Models (LLMs) for knowledge graph completion, achieving state-of-the-art performance in tasks like triple classification and relation prediction, with fine-tuned smaller models outperforming ChatGPT and GPT-4.
Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness. In this study, we explore utilizing Large Language Models (LLM) for knowledge graph completion. We consider triples in knowledge graphs as text sequences and introduce an innovative framework called Knowledge Graph LLM (KG-LLM) to model these triples. Our technique employs entity and relation descriptions of a triple as prompts and utilizes the response for predictions. Experiments on various benchmark knowledge graphs demonstrate that our method attains state-of-the-art performance in tasks such as triple classification and relation prediction. We also find that fine-tuning relatively smaller models (e.g., LLaMA-7B, ChatGLM-6B) outperforms recent ChatGPT and GPT-4.