  Chain-of-thought prompting has demonstrated remarkable performance on various
natural language reasoning tasks. However, it tends to perform poorly on tasks
which requires solving problems harder than the exemplars shown in the prompts.
To overcome this challenge of easy-to-hard generalization, we propose a novel
prompting strategy, least-to-most prompting. The key idea in this strategy is
to break down a complex problem into a series of simpler subproblems and then
solve them in sequence. Solving each subproblem is facilitated by the answers
to previously solved subproblems. Our experimental results on tasks related to
symbolic manipulation, compositional generalization, and math reasoning reveal
that least-to-most prompting is capable of generalizing to more difficult
problems than those seen in the prompts. A notable finding is that when the
GPT-3 code-davinci-002 model is used with least-to-most prompting, it can solve
the compositional generalization benchmark SCAN in any split (including length
split) with an accuracy of at least 99% using just 14 exemplars, compared to
only 16% accuracy with chain-of-thought prompting. This is particularly
noteworthy because neural-symbolic models in the literature that specialize in
solving SCAN are trained on the entire training set containing over 15,000
examples. We have included prompts for all the tasks in the Appendix.
