Complexity-Based Prompting for Multi-Step Reasoning
- URL: http://arxiv.org/abs/2210.00720v1
- Date: Mon, 3 Oct 2022 05:33:27 GMT
- Title: Complexity-Based Prompting for Multi-Step Reasoning
- Authors: Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark and Tushar Khot
- Abstract summary: We study the task of prompting large-scale language models to perform multi-step reasoning.
A central question is which reasoning examples make the most effective prompts.
We propose complexity-based prompting, a simple and effective example selection scheme for multi-step reasoning.
- Score: 72.0057198610614
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study the task of prompting large-scale language models to perform
multi-step reasoning. Existing work shows that when prompted with a chain of
thoughts (CoT), sequences of short sentences describing intermediate reasoning
steps towards a final answer, large language models can generate new reasoning
chains and predict answers for new inputs. A central question is which
reasoning examples make the most effective prompts. In this work, we propose
complexity-based prompting, a simple and effective example selection scheme for
multi-step reasoning. We show that prompts with higher reasoning complexity,
i.e., chains with more reasoning steps, achieve substantially better
performance on math word reasoning tasks over strong baselines. We further
extend our complexity-based criteria from prompting (selecting inputs) to
decoding (selecting outputs), where we sample multiple reasoning chains from
the model, then choose the majority of generated answers from complex reasoning
chains (over simple chains). When used to prompt GPT-3, our approach
substantially improves multi-step reasoning accuracy, with an 8.6% absolute
improvement on GSM8K, and 6.4% on MathQA. Compared with existing example
selection schemes like manual tuning or retrieval-based selection, selection
based on reasoning complexity is intuitive, easy to implement, and
annotation-efficient. Further results demonstrate the robustness of our methods
under format perturbation and distribution shift.
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