Large language models can perform various reasoning tasks by using
chain-of-thought prompting, which guides them to find answers through
step-by-step demonstrations. However, the quality of the prompts depends on the
demonstrations given to the models, and creating many of them by hand is
costly. We introduce Synthetic prompting, a method that leverages a few
handcrafted examples to prompt the model to generate more examples by itself,
and selects effective demonstrations to elicit better reasoning. Our method
alternates between a backward and forward process to generate new examples. The
backward process generates a question that match a sampled reasoning chain, so
that the question is solvable and clear. The forward process produces a more
detailed reasoning chain for the question, improving the quality of the
example. We evaluate our method on numerical, symbolic, and algorithmic
reasoning tasks, and show that it outperforms existing prompting techniques.

本文介绍了 Synthetic prompting 方法，该方法利用少量手工示例来提示模型自行生成更多示例，并选择有效的演示文稿以引出更好的推理。在数字、符号和算法推理任务上评估了该方法，并表明其优于现有提示技术。