From Graph Paths to Natural Language: Enhancing LLM Reasoning for Multi-choice Question-Answering Tasks

Language models have achieved good results in many tasks. However, there are still some challenges with reasoning due to insufficient knowledge, which leads to poor performance of the models on specific tasks. We proposed a framework based on the existing solution (i.e., GreaseLM), using a large language model to replace some complex modules. Our approach converts the knowledge graph paths into natural language sentences, providing contextual support to enhance the reasoning capability of the large language models. Using the framework based on a large language model is simpler and easier to manage, and it also achieves better accuracy on multi-choice question-answering tasks.