Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs). CoT explicitly encourages the LLM to generate intermediate rationales for solving a problem, by providing a series of reasoning steps in the demonstrations. Despite its success, there is still little understanding of what makes CoT prompting effective and which aspects of the demonstrated reasoning steps contribute to its performance. In this paper, we show that CoT reasoning is possible even with invalid demonstrations - prompting with invalid reasoning steps can achieve over 80-90% of the performance obtained using CoT under various metrics, while still generating coherent lines of reasoning during inference. Further experiments show that other aspects of the rationales, such as being relevant to the query and correctly ordering the reasoning steps, are much more important for effective CoT reasoning. Overall, these findings both deepen our understanding of CoT prompting, and open up new questions regarding LLMs' capability to learn to reason in context.

翻译：链式思维提示（Chain-of-Thought, CoT）能够显著提升大语言模型（LLMs）的多步推理能力。CoT通过在示例中提供一系列推理步骤，明确鼓励LLM生成中间推理过程以解决问题。尽管CoT取得了成功，但人们对其为何有效、以及范例中的推理步骤哪些方面贡献了性能，仍知之甚少。本文表明，即使使用无效的示例，CoT推理也是可能的——使用无效推理步骤进行提示，在不同评估指标下可达到CoT性能的80-90%，同时在推理过程中仍能生成连贯的推理线路。进一步的实验表明，推理过程中的其他方面，如与查询的相关性及推理步骤的正确排序，对有效的CoT推理更为关键。总体而言，这些发现既加深了我们对CoT提示的理解，也提出了关于LLMs在上下文中学习推理能力的新问题。