ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models
This work addresses core challenges in KBQA for users needing interpretable and knowledge-intensive question answering, presenting a new paradigm for combining LLMs with knowledge graphs.
The paper tackles inefficiencies and errors in knowledge retrieval and semantic parsing for Knowledge Base Question Answering (KBQA) by introducing ChatKBQA, a generate-then-retrieve framework using fine-tuned large language models, achieving state-of-the-art performance on WebQSP and CWQ datasets.
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.