FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering

TL;DR

FiDeLiS is a training-free framework that enhances the factuality and interpretability of large language models in knowledge graph question answering by anchoring answers to verifiable reasoning steps and efficiently traversing KGs.

Contribution

It introduces a unified, training-free framework with step-wise reasoning validation and a Path-RAG module to improve factuality and efficiency in KG-based LLM question answering.

Findings

Improves factual accuracy of LLM responses in KG tasks.

Reduces computational costs with Path-RAG module.

Enhances interpretability of reasoning process.

Abstract

Large Language Models (LLMs) are often challenged by generating erroneous or hallucinated responses, especially in complex reasoning tasks. Leveraging Knowledge Graphs (KGs) as external knowledge sources has emerged as a viable solution. However, existing KG-enhanced methods, either retrieval-based or agent-based, encounter difficulties in accurately retrieving knowledge and efficiently traversing KGs at scale. In this paper, we propose a unified framework, FiDeLiS, designed to improve the factuality of LLM responses by anchoring answers to verifiable reasoning steps retrieved from KGs. To achieve this, we leverage step-wise beam search with a deductive scoring function, allowing the LLM to validate reasoning process step by step, and halt the search once the question is deducible. In addition, we propose a Path-RAG module to pre-select a smaller candidate set for each beam search step, reducing computational costs by narrowing the search space. Extensive experiments show that our method, as a training-free framework, not only improve the performance but also enhance the factuality and interpretability across different benchmarks. Code is released at https://github.com/Y-Sui/FiDeLiS.