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Paper Walkthrough: Chain of Thought (

The paper explores a technique called "chain-of-thought prompting" to improve reasoning skills in large language models. This involves providing examples of intermediate reasoning steps that break complex problems into simpler parts. They tested this on models like GPT-3 and PaLM on math, common sense and symbolic reasoning tasks. The key finding was that chain-of-thought prompting significantly boosted performance compared to regular prompting, especially for larger models. The 540B PaLM model achieved state-of-the-art accuracy on math word problems when prompted with reasoning chains. Gains were much higher for multi-step versus simple problems. Overall, this demonstrates a promising way to unlock complex reasoning in language models without training, by showing examples of human-like reasoning chains.