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ChatGPT is a powerful large language model (LLM) that covers knowledgeresources such as Wikipedia and supports natural language question answeringusing its own knowledge. Therefore, there is growing interest in exploringwhether ChatGPT can replace traditional knowledge-based question answering(KBQA) models. Although there have been some works analyzing the questionanswering performance of ChatGPT, there is still a lack of large-scale,comprehensive testing of various types of complex questions to analyze thelimitations of the model. In this paper, we present a framework that followsthe black-box testing specifications of CheckList proposed by Ribeiro et. al.We evaluate ChatGPT and its family of LLMs on eight real-world KB-based complexquestion answering datasets, which include six English datasets and twomultilingual datasets. The total number of test cases is approximately 190,000.In addition to the GPT family of LLMs, we also evaluate the well-known FLAN-T5to identify commonalities between the GPT family and other LLMs. The datasetand code are available athttps://github.com/tan92hl/Complex-Question-Answering-Evaluation-of-GPT-family.git

Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family