Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
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We propose a novel prompting strategy, least-to-most prompting, that enables large language models to better perform multi-step reasoning tasks. Least-to-most prompting first reduces a complex problem into a list of subproblems, and then sequentially solves the subproblems, whereby solving a given subproblem is facilitated by the model's answers to previously solved subproblems. Experiments on symbolic manipulation, compositional generalization and numerical reasoning demonstrate that least-to-most prompting can generalize to examples that are harder than those seen in the prompt context, outperforming other prompting-based approaches by a large margin. A notable empirical result is that the GPT-3 code-davinci-002 model with least-to-most-prompting can solve the SCAN benchmark with an accuracy of 99.7 using 14 examples. As a comparison, the neural-symbolic models in the literature specialized for solving SCAN are trained with the full training set of more than 15,000 examples.
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