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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2 Chain-of-Thought Prompting
- What attractive properties of chain of thought did Wei et al. list?It lets models decompose multi-step problems and spend more computation on harder ones; it gives an interpretable (if incomplete) window into how an answer was reached; it applies in principle to any task humans solve via language; and it can be elicited from off-the-shelf models just by adding examples to the prompt.
- Wei et al., 2022, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Advances in Neural Information Processing Systems 35 (NeurIPS 2022)), §2
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