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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4 Commonsense Reasoning
- How did chain-of-thought prompting do on commonsense reasoning?On CSQA, StrategyQA, Date and Sports Understanding (BIG-bench) and SayCan robot planning, it added gains on top of scaling, largest for PaLM 540B: e.g. StrategyQA 75.6% vs a prior best of 69.4%, sports understanding 95.4% vs 84% for an unaided sports enthusiast; gains on CSQA were minimal.
- Wei et al., 2022, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Advances in Neural Information Processing Systems 35 (NeurIPS 2022)), §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