Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei et al., 2022, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Advances in Neural Information Processing Systems 35 (NeurIPS 2022))
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3.4 Robustness of Chain of Thought
- How robust is chain-of-thought prompting to who writes the exemplars?Chains written by three different annotators, a deliberately concise style, and exemplars sampled from GSM8K’s training set all beat standard prompting by a large margin despite some variance, so success doesn’t depend on a particular linguistic style; it was also robust to exemplar order and number.
- Wei et al., 2022, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Advances in Neural Information Processing Systems 35 (NeurIPS 2022)), §3.4
Source
- Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le and Denny Zhou. 2022. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). https://papers.nips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html