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This paper investigates the problem-solving capabilities of Large LanguageModels (LLMs) by evaluating their performance on stumpers, unique single-stepintuition problems that pose challenges for human solvers but are easilyverifiable. We compare the performance of four state-of-the-art LLMs(Davinci-2, Davinci-3, GPT-3.5-Turbo, GPT-4) to human participants. Ourfindings reveal that the new-generation LLMs excel in solving stumpers andsurpass human performance. However, humans exhibit superior skills in verifyingsolutions to the same problems. This research enhances our understanding ofLLMs' cognitive abilities and provides insights for enhancing theirproblem-solving potential across various domains.

Decoding Stumpers: Large Language Models vs. Human Problem-Solvers