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

• URL: http://arxiv.org/abs/2405.13873v2
• Date: Thu, 10 Oct 2024 15:27:41 GMT
• Title: FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering
• Authors: Yuan Sui, Yufei He, Nian Liu, Xiaoxin He, Kun Wang, Bryan Hooi,
• Abstract summary: We propose a retrieval augmented reasoning method, FiDeLiS, which enhances knowledge graph question answering. FiDeLiS uses a keyword-enhanced retrieval mechanism that fetches relevant entities and relations from a vector-based index of KGs. A distinctive feature of our approach is its blend of natural language planning with beam search to optimize the selection of reasoning paths.
• Score: 46.41364317172677
• License:
• Abstract: Large language models are often challenged by generating erroneous or `hallucinated' responses, especially in complex reasoning tasks. To mitigate this, we propose a retrieval augmented reasoning method, FiDeLiS, which enhances knowledge graph question answering by anchoring responses to structured, verifiable reasoning paths. FiDeLiS uses a keyword-enhanced retrieval mechanism that fetches relevant entities and relations from a vector-based index of KGs to ensure high-recall retrieval. Once these entities and relations are retrieved, our method constructs candidate reasoning paths which are then refined using a stepwise beam search. This ensures that all the paths we create can be confidently linked back to KGs, ensuring they are accurate and reliable. A distinctive feature of our approach is its blend of natural language planning with beam search to optimize the selection of reasoning paths. Moreover, we redesign the way reasoning paths are scored by transforming this process into a deductive reasoning task, allowing the LLM to assess the validity of the paths through deductive reasoning rather than traditional logit-based scoring. This helps avoid misleading reasoning chains and reduces unnecessary computational demand. Extensive experiments demonstrate that our method, even as a training-free method which has lower computational costs and superior generality, outperforms established strong baselines across three datasets.

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