⚖️ Self-Consistency Improves Chain-of-Thought Reasoning in LMs

In this episode, we explore self-consistency, a novel strategy that significantly improves how large language models perform complex reasoning. The method builds on chain-of-thought prompting by generating multiple diverse reasoning paths for a single problem instead of just one. By simply selecting the most consistent answer from these different lines of thought, this unsupervised technique dramatically boosts accuracy on arithmetic and commonsense tasks without any additional model training.

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