"Chain-of-thought prompting allows language models to generate intermediate reasoning steps before producing a final answer."
Wei and colleagues demonstrated that intermediate reasoning examples can improve model performance on selected arithmetic, commonsense and symbolic reasoning tasks. This establishes chain-of-thought prompting as a computational technique rather than proof of genuine transparency into a model's internal operations.
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