Digest the Knowledge: Large Language Models empowered Message Passing for Knowledge Graph Question Answering

Despite their success, large language models (LLMs) suffer from notorious hallucination issue. By introducing external knowledge stored in knowledge graphs (KGs), existing methods use paths as the medium to represent the graph information sent into LLMs. However, paths only contain limited graph structure information and are unorganized with redundant sequentially appearing keywords, which are difficult for LLMs to digest. We aim to find a suitable medium that captures the essence of structural knowledge in KGs. Inspired by Neural Message Passing in Graph Neural Networks, we propose Language Message Passing (LMP), which first learns a concise facts graph by iteratively aggregating neighbor entities and transforming them into semantic facts, and then performs Topological Readout that encodes the graph structure information into multi-level lists of texts to augment LLMs. Our method serves as a brand-new innovative framework that brings a new perspective into KG-enhanced LLMs, and also offers humanlevel semantic explainability with significant performance improvements over existing methods on all five knowledge graph question answering datasets. Our code is available at https://github.com/wanjunhong0/LMP .