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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5 Symbolic Reasoning
- Which symbolic reasoning tasks did Wei et al. use, and what did they show?Last-letter concatenation (“Amy Brown” → “yn”) and coin-flip state tracking. With CoT, PaLM 540B solved in-domain cases almost perfectly, and CoT enabled length generalization to more words or flips than in the exemplars, where standard prompting failed; small models failed even in-domain.
- Wei et al., 2022, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Advances in Neural Information Processing Systems 35 (NeurIPS 2022)), §5
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