Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Understand

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.