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Large language models (LLMs) have exhibited remarkable performance on variousnatural language processing (NLP) tasks, especially for question answering.However, in the face of problems beyond the scope of knowledge, these LLMs tendto talk nonsense with a straight face, where the potential solution could beincorporating an Information Retrieval (IR) module and generating responsebased on these retrieved knowledge. In this paper, we present a novel frameworkto assist LLMs, such as ChatGPT, to retrieve question-related structuredinformation on the knowledge graph, and demonstrate that Knowledge-basedquestion answering (Keqing) could be a nature Chain-of-Thought (CoT) mentor toguide the LLM to sequentially find the answer entities of a complex questionthrough interpretable logical chains. Specifically, the workflow of Keqing willexecute decomposing a complex question according to predefined templates,retrieving candidate entities on knowledge graph, reasoning answers ofsub-questions, and finally generating response with reasoning paths, whichgreatly improves the reliability of LLM's response. The experimental results onKBQA datasets show that Keqing can achieve competitive performance andillustrate the logic of answering each question.

keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM