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Large language models (LLMs) such as ChatGPT and GPT-4 have made significantprogress in NLP. However, their ability to memorize, represent, and leveragecommonsense knowledge has been a well-known pain point for LLMs. It remainsunclear that: (1) Can GPTs effectively answer commonsense questions? (2) AreGPTs knowledgeable in commonsense? (3) Are GPTs aware of the underlyingcommonsense knowledge for answering a specific question? (4) Can GPTseffectively leverage commonsense for answering questions? To evaluate the abovecommonsense problems, we conduct a series of experiments to evaluate ChatGPT'scommonsense abilities, and the experimental results show that: (1) GPTs canachieve good QA accuracy in commonsense tasks, while they still struggle withcertain types of knowledge. (2) ChatGPT is knowledgeable, and can accuratelygenerate most of the commonsense knowledge using knowledge prompts. (3) Despiteits knowledge, ChatGPT is an inexperienced commonsense problem solver, whichcannot precisely identify the needed commonsense knowledge for answering aspecific question, i.e., ChatGPT does not precisely know what commonsenseknowledge is required to answer a question. The above findings raise the needto investigate better mechanisms for utilizing commonsense knowledge in LLMs,such as instruction following, better commonsense guidance, etc.

ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models