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arxiv: 2310.08975 · v3 · pith:IOVEHBG7new · submitted 2023-10-13 · 💻 cs.CL · cs.AI

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models

Haoran Luo , Haihong E , Zichen Tang , Shiyao Peng , Yikai Guo , Wentai Zhang , Chenghao Ma , Guanting Dong
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Meina Song Wei Lin Yifan Zhu Luu Anh Tuan
This is my paper
classification 💻 cs.CL cs.AI
keywords knowledgeretrievalkbqaansweringchatkbqaquestionbasechallenges
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Knowledge Base Question Answering (KBQA) aims to answer natural language questions over large-scale knowledge bases (KBs), which can be summarized into two crucial steps: knowledge retrieval and semantic parsing. However, three core challenges remain: inefficient knowledge retrieval, mistakes of retrieval adversely impacting semantic parsing, and the complexity of previous KBQA methods. To tackle these challenges, we introduce ChatKBQA, a novel and simple generate-then-retrieve KBQA framework, which proposes first generating the logical form with fine-tuned LLMs, then retrieving and replacing entities and relations with an unsupervised retrieval method, to improve both generation and retrieval more directly. Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and CWQ. This work can also be regarded as a new paradigm for combining LLMs with knowledge graphs (KGs) for interpretable and knowledge-required question answering. Our code is publicly available.

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