Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning
This addresses a problem for users of LLMs in complex reasoning applications, but it is incremental as it builds on existing graph-based methods.
The paper tackles the challenge of LLMs struggling with reasoning tasks that require understanding implicit relationships, by proposing Reasoning with Graphs (RwG) to structure implicit knowledge from context into graphs, which improves performance on logical reasoning and multi-hop question answering tasks.
Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks; however, they still encounter challenges in reasoning tasks that require understanding and inferring relationships between distinct pieces of information within text sequences. This challenge is particularly pronounced in tasks involving multi-step processes, such as logical reasoning and multi-hop question answering, where understanding implicit relationships between entities and leveraging multi-hop connections in the given context are crucial. Graphs, as fundamental data structures, explicitly represent pairwise relationships between entities, thereby offering the potential to enhance LLMs' reasoning capabilities. External graphs have proven effective in supporting LLMs across multiple tasks. However, in many reasoning tasks, no pre-existing graph structure is provided. Can we structure implicit knowledge derived from context into graphs to assist LLMs in reasoning? In this paper, we propose Reasoning with Graphs (RwG) by first constructing explicit graphs from the context and then leveraging these graphs to enhance LLM reasoning performance on reasoning tasks. Extensive experiments demonstrate the effectiveness of the proposed method in improving both logical reasoning and multi-hop question answering tasks.