聚焦式思维链：通过结构化输入信息实现高效大型语言模型推理

摘要

近期大型语言模型通过生成详尽的思维链轨迹实现了强大的推理性能，但这往往导致令牌使用量激增和推理延迟升高。现有提升效率的方法通常聚焦于模型层面的干预，例如采用强化学习或监督微调来降低冗余度。与之相对，我们提出了一种无需训练、以输入为核心的创新方案。受认知心理学启发，我们引入了聚焦式思维链（F-CoT）方法，将信息提取与推理过程相分离。F-CoT首先将查询中的关键信息组织成简洁的结构化上下文，随后引导模型仅基于该上下文进行推理。通过规避无关细节的干扰，F-CoT能自然生成更简短的推理路径。在数学应用题测试中，F-CoT在保持与标准零样本思维链相当准确度的同时，将生成令牌数减少至原有量的1/2到1/3。这一结果表明，结构化输入是实现更高效大语言模型推理的简单而有效的关键途径。

English

Recent large language models achieve strong reasoning performance by generating detailed chain-of-thought traces, but this often leads to excessive token use and high inference latency. Existing efficiency approaches typically focus on model-centric interventions, such as reinforcement learning or supervised fine-tuning, to reduce verbosity. In contrast, we propose a training-free, input-centric approach. Inspired by cognitive psychology, we introduce Focused Chain-of-Thought (F-CoT), which separates information extraction from the reasoning process. F-CoT first organizes the essential information from a query into a concise, structured context and then guides the model to reason exclusively over this context. By preventing attention to irrelevant details, F-CoT naturally produces shorter reasoning paths. On arithmetic word problems, F-CoT reduces generated tokens by 2-3x while maintaining accuracy comparable to standard zero-shot CoT. These results highlight structured input as a simple yet effective lever for more efficient LLM reasoning.

聚焦式思维链：通过结构化输入信息实现高效大型语言模型推理

Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information

摘要

Support