Least-to-Most Prompting
Last updated on November 12, 2024
Least-to-Most Prompting is a technique where the LLM first decomposes a problem into smaller sub-problems, then solves these sequentially to arrive at the final answer. This method has shown significant improvements in tasks involving symbolic manipulation, compositional generalization, and mathematical reasoning. However, it's important to note that the prompts for decomposition do not universally apply across different problems. The effectiveness hinges on correctly breaking down the problem, which may not consistently occur with a fixed prompt.
Footnotes
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Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., & Chi, E. (2022). Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. ↩