Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2201.11903v6
Summary
Large language models handle many tasks well through few-shot prompting, but making them bigger has not been enough for multi-step problems such as arithmetic, commonsense and symbolic reasoning. The authors propose chain-of-thought prompting. Each few-shot exemplar is extended into an (input, chain of thought, output) triple, where the middle part spells out the intermediate reasoning in natural language before the answer. No model is finetuned: off-the-shelf models only see a handful of hand-written demonstrations. The method is tested on five math word problem benchmarks, several commonsense datasets and two synthetic symbolic tasks, and compared against standard input–output prompting. The gains are large but appear only at scale, roughly at 100B parameters and above. Below that size, the generated reasoning does not help. With PaLM 540B, the method sets a new state of the art on GSM8K. This matters because a single general model can match or beat task-specific finetuned systems without a costly rationale-annotated training set. It also suggests that standard prompting underestimates what large models can do.
Key points
- Prompts: one fixed set of eight manually written chain-of-thought exemplars was used for GSM8K, SVAMP, ASDiv and MAWPS. AQuA, a multiple-choice dataset, used four exemplars taken from its training set.
- Main result: PaLM 540B with eight chain-of-thought exemplars scored about 57% on GSM8K, versus about 18% with standard prompting. This beat the previous best, finetuned GPT-3 175B with a verifier.
- Scale dependence: reasoning ability appears only in sufficiently large models. On tasks where standard prompting stays flat as models grow, chain-of-thought prompting gives steeply rising scaling curves.
- Coverage: the tasks include CSQA, StrategyQA, Date and Sports Understanding, and SayCan robot planning. Symbolic tasks (last letter concatenation, coin flip) show generalization to inputs longer than those in the exemplars.
- Limitations noted by the authors: the generated reasoning can be wrong even when the answer is right. It is unclear whether the models truly "reason". Running models this large is expensive.
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Cite
@misc{wei2022chain,
title = {{Chain-of-Thought Prompting Elicits Reasoning in Large Language Models}},
author = {Wei, Jason and Wang, Xuezhi and Schuurmans, Dale and Bosma, Maarten and Ichter, Brian and Xia, Fei and Chi, Ed and Le, Quoc and Zhou, Denny},
year = {2022},
doi = {10.48550/arxiv.2201.11903},
eprint = {2201.11903},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2201.11903}
}References
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