SoftCoT：面向大语言模型高效推理的软思维链
SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs
AI总结：
SoftCoT提出一种无需修改LLM的连续空间推理方法，利用轻量辅助模型生成软思维令牌，经投影模块映射，在五个基准上通过参数高效微调提升推理性能。
摘要：
思维链（Chain-of-Thought, CoT）推理通过生成中间推理步骤，使大语言模型（LLMs）能够解决复杂的推理任务。然而，现有大多数方法侧重于硬令牌解码，这限制了推理在离散词汇空间内进行，且未必总是最优的。尽管近期有研究探索连续空间推理，但这些方法通常需要全模型微调，并遭受灾难性遗忘，从而限制了其对已在零样本设置下借助适当指令表现良好的最先进LLMs的适用性。为应对这一挑战，我们提出了一种新颖的连续空间推理方法，该方法无需修改LLM。具体而言，我们采用一个轻量级固定辅助模型来推测性地生成实例特定的软思维令牌作为初始思维链，然后通过一个可训练的投影模块将其映射到LLM的表示空间中。在五个推理基准上的实验结果表明，我们的方法通过监督式、参数高效的微调提升了LLM的推理性能。源代码可在 https://github.com/xuyige/SoftCoT 获取。
英文摘要：
Chain-of-Thought (CoT) reasoning enables Large Language Models (LLMs) to solve complex reasoning tasks by generating intermediate reasoning steps. However, most existing approaches focus on hard token decoding, which constrains reasoning within the discrete vocabulary space and may not always be optimal. While recent efforts explore continuous-space reasoning, they often require full-model fine-tuning and suffer from catastrophic forgetting, limiting their applicability to state-of-the-art LLMs that already perform well in zero-shot settings with a proper instruction. To address this challenge, we propose a novel approach for continuous-space reasoning that does not require modifying the LLM. Specifically, we employ a lightweight fixed assistant model to speculatively generate instance-specific soft thought tokens as the initial chain of thoughts, which are then mapped into the LLM's representation space via a trainable projection module. Experimental results on five reasoning benchmarks demonstrate that our method enhances LLM reasoning performance through supervised, parameter-efficient fine-tuning. Source code is available at https://github.com/xuyige/SoftCoT.
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。
