Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning
This work addresses a specific issue in knowledge graph reasoning for question answering, representing an incremental improvement over existing methods.
The paper tackles the problem of hallucinatory tool invocations and poor readability in LLM-based knowledge graph question answering by proposing a memory-augmented query reconstruction method, achieving state-of-the-art performance on benchmarks WebQSP and CWQ.
Large language models (LLMs) have achieved remarkable performance on knowledge graph question answering (KGQA) tasks by planning and interacting with knowledge graphs. However, existing methods often confuse tool utilization with knowledge reasoning, harming readability of model outputs and giving rise to hallucinatory tool invocations, which hinder the advancement of KGQA. To address this issue, we propose Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (MemQ) to decouple LLM from tool invocation tasks using LLM-built query memory. By establishing a memory module with explicit descriptions of query statements, the proposed MemQ facilitates the KGQA process with natural language reasoning and memory-augmented query reconstruction. Meanwhile, we design an effective and readable reasoning to enhance the LLM's reasoning capability in KGQA. Experimental results that MemQ achieves state-of-the-art performance on widely used benchmarks WebQSP and CWQ.