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 Meina Song Wei Lin
Knowledge Base Question Answering (KBQA) aims to derive answers to natural
language questions over large-scale knowledge bases (KBs), which are generally
divided into two research components: knowledge retrieval and semantic parsing.
However, three core challenges remain, including inefficient knowledge
retrieval, retrieval errors adversely affecting semantic parsing, and the
complexity of previous KBQA methods. In the era of large language models
(LLMs), we introduce ChatKBQA, a novel generate-then-retrieve KBQA framework
built on fine-tuning open-source LLMs such as Llama-2, ChatGLM2 and Baichuan2.
ChatKBQA proposes generating the logical form with fine-tuned LLMs first, then
retrieving and replacing entities and relations through an unsupervised
retrieval method, which improves both generation and retrieval more
straightforwardly. Experimental results reveal that ChatKBQA achieves new
state-of-the-art performance on standard KBQA datasets, WebQSP, and
ComplexWebQuestions (CWQ). This work also provides a new paradigm for combining
LLMs with knowledge graphs (KGs) for interpretable and knowledge-required
question answering. Our code is publicly available.