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We explore the creative problem-solving capabilities of modern large languagemodels (LLMs) in a constrained setting. The setting requires circumventing acognitive bias known in psychology as ''functional fixedness'' to use familiarobjects in innovative or unconventional ways. To this end, we create MacGyver,an automatically generated dataset consisting of 1,600 real-world problems thatdeliberately trigger functional fixedness and require thinking'out-of-the-box'. We then present our collection of problems to both LLMs andhumans to compare and contrast their problem-solving abilities. We show thatMacGyver is challenging for both groups, but in unique and complementary ways.For example, humans typically excel in solving problems that they are familiarwith but may struggle with tasks requiring domain-specific knowledge, leadingto a higher variance. On the other hand, LLMs, being exposed to a variety ofhighly specialized knowledge, attempt broader problems but are prone tooverconfidence and propose actions that are physically infeasible orinefficient. We also provide a detailed error analysis of LLMs, and demonstratethe potential of enhancing their problem-solving ability with novel promptingtechniques such as iterative step-wise reflection and divergent-convergentthinking. This work provides insight into the creative problem-solvingcapabilities of humans and AI and illustrates how psychological paradigms canbe extended into large-scale tasks for comparing humans and machines.

MacGyver: Are Large Language Models Creative Problem Solvers?