重新审视链式思维提示：零样本可以比少样本更强
Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot
AI总结：
本研究发现，对于强模型而言，传统CoT示例对推理性能无帮助，且模型更关注指令而非示例，揭示了ICL+CoT框架在数学推理中的局限性。
摘要：
上下文学习（ICL）是大型语言模型（LLMs）的一项关键涌现能力，最近的研究引入了链式思维（CoT）到ICL的示例中，以增强推理能力，尤其是在数学任务中。然而，鉴于模型能力的持续进步，仍然不清楚CoT示例是否仍能为最近的更强模型在这些任务中带来好处。通过系统实验，我们发现对于最近的强模型如Qwen2.5系列，添加传统的CoT示例与零样本CoT相比并不提高推理性能。相反，其主要功能是使输出格式与人类期望对齐。我们进一步研究了使用高级模型如Qwen2.5-Max和DeepSeek-R1构造的增强型CoT示例的有效性。实验结果表明，这些增强的示例仍然无法提高模型的推理性能。进一步分析发现，模型倾向于忽略示例并主要关注指令，导致推理能力没有明显提升。总体而言，我们的发现突显了当前ICL+CoT框架在数学推理中的局限性，呼吁对ICL范式和示例定义进行重新审视。
英文摘要：
In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs), and recent studies introduce Chain-of-Thought (CoT) to exemplars of ICL to enhance the reasoning capability, especially in mathematics tasks. However, given the continuous advancement of model capabilities, it remains unclear whether CoT exemplars still benefit recent, stronger models in such tasks. Through systematic experiments, we find that for recent strong models such as the Qwen2.5 series, adding traditional CoT exemplars does not improve reasoning performance compared to Zero-Shot CoT. Instead, their primary function is to align the output format with human expectations. We further investigate the effectiveness of enhanced CoT exemplars, constructed using answers from advanced models such as \texttt{Qwen2.5-Max} and \texttt{DeepSeek-R1}. Experimental results indicate that these enhanced exemplars still fail to improve the model's reasoning performance. Further analysis reveals that models tend to ignore the exemplars and focus primarily on the instructions, leading to no observable gain in reasoning ability. Overall, our findings highlight the limitations of the current ICL+CoT framework in mathematical reasoning, calling for a re-examination of the ICL paradigm and the definition of exemplars.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
