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))
Subscriber deck — free during early access
1 cards in “3.3 Ablation Study” · whole deck
3.3 Ablation Study
- Which ablations showed that chain of thought isn’t just an equation, extra computation, or knowledge activation?Equation-only prompting barely helped on GSM8K (questions too hard to translate directly into equations), though it helped on 1–2-step data sets. Outputting a matching number of dots (variable compute only) did about as well as the baseline. Putting the chain of thought after the answer also did about as well as the baseline.
- Wei et al., 2022, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Advances in Neural Information Processing Systems 35 (NeurIPS 2022)), §3.3
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