MyGO Multiplex CoT：一种通过双重思维链实现大型语言模型自我反思的方法
MyGO Multiplex CoT: A Method for Self-Reflection in Large Language Models via Double Chain of Thought Thinking
AI总结：
本文提出Multiplex CoT方法，通过双重思维链实现LLM自我反思，无需额外训练即可提升推理质量，并提供了Google Colab实现指南。
摘要：
近年来，大型语言模型（LLM）的进步展示了其在各种推理和决策任务中的卓越能力。然而，推理过程的质量和连贯性仍可通过增强内省和自我反思得到提升。在本文中，我们引入了Multiplex CoT（思维链），一种通过启动双重思维链（CoT）思考，使LLM能够在推理时模拟自我审查形式的方法。Multiplex CoT利用了迭代推理的力量，模型首先生成初始思维链，随后通过第二轮思维生成来批判和完善这一推理。这种递归方法能够产生更连贯、逻辑更严密且更稳健的答案，从而改善整体决策过程。我们展示了如何通过简单的提示工程在现有LLM架构中有效实现该方法，无需额外训练即可达到类似于学习-精炼模型（LRM）的效果。此外，我们提供了在Google Colab中实现该方法的实用指南，便于将其轻松集成到实际应用中。
英文摘要：
Recent advancements in large language models (LLMs) have demonstrated their impressive abilities in various reasoning and decision-making tasks. However, the quality and coherence of the reasoning process can still benefit from enhanced introspection and self-reflection. In this paper, we introduce Multiplex CoT (Chain of Thought), a method that enables LLMs to simulate a form of self-review while reasoning, by initiating double Chain of Thought (CoT) thinking. Multiplex CoT leverages the power of iterative reasoning, where the model generates an initial chain of thought and subsequently critiques and refines this reasoning with a second round of thought generation. This recursive approach allows for more coherent, logical, and robust answers, improving the overall decision-making process. We demonstrate how this method can be effectively implemented using simple prompt engineering in existing LLM architectures, achieving an effect similar to that of the Learning-Refinement Model (LRM) without the need for additional training. Additionally, we present a practical guide for implementing the method in Google Colab, enabling easy integration into real-world applications.
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。
