Chain-of-Thought Reasoning Without Prompting

The paper introduces a novel method for eliciting chain-of-thought reasoning from large language models without the need for explicit prompting. By altering the decoding process, the study reveals that models can inherently generate reasoning paths, demonstrating a significant improvement in reasoning capabilities and model confidence over standard decoding methods.

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