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Human language offers a powerful window into our thoughts -- we tell stories,give explanations, and express our beliefs and goals through words. Abundantevidence also suggests that language plays a developmental role in structuringour learning. Here, we ask: how much of human-like thinking can be captured bylearning statistical patterns in language alone? We first contribute a newchallenge benchmark for comparing humans and distributional large languagemodels (LLMs). Our benchmark contains two problem-solving domains (planning andexplanation generation) and is designed to require generalization to new,out-of-distribution problems expressed in language. We find that humans are farmore robust than LLMs on this benchmark. Next, we propose a hybridParse-and-Solve model, which augments distributional LLMs with a structuredsymbolic reasoning module. We find that this model shows more robust adaptationto out-of-distribution planning problems, demonstrating the promise of hybridAI models for more human-like reasoning.

Structured, flexible, and robust: benchmarking and improving large  language models towards more human-like behavior in out-of-distribution  reasoning tasks