Title: Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation Show Chinese title

Title: 战略性链式思维：通过策略揭示引导大型语言模型的准确推理

Abstract: The Chain-of-Thought (CoT) paradigm has emerged as a critical approach for enhancing the reasoning capabilities of large language models (LLMs). However, despite their widespread adoption and success, CoT methods often exhibit instability due to their inability to consistently ensure the quality of generated reasoning paths, leading to sub-optimal reasoning performance. To address this challenge, we propose the \textbf{Strategic Chain-of-Thought} (SCoT), a novel methodology designed to refine LLM performance by integrating strategic knowledge prior to generating intermediate reasoning steps. SCoT employs a two-stage approach within a single prompt: first eliciting an effective problem-solving strategy, which is then used to guide the generation of high-quality CoT paths and final answers. Our experiments across eight challenging reasoning datasets demonstrate significant improvements, including a 21.05\% increase on the GSM8K dataset and 24.13\% on the Tracking\_Objects dataset, respectively, using the Llama3-8b model. Additionally, we extend the SCoT framework to develop a few-shot method with automatically matched demonstrations, yielding even stronger results. These findings underscore the efficacy of SCoT, highlighting its potential to substantially enhance LLM performance in complex reasoning tasks. Show Chinese abstract

Abstract: 思维链（CoT）范式已成为提升大型语言模型（LLMs）推理能力的关键方法。然而，尽管其广泛应用和成功，由于无法始终保证生成推理路径的质量，CoT 方法常常表现出不稳定性，导致推理性能不佳。为了解决这一挑战，我们提出了\textbf{战略链式思维} （SCoT），这是一种新颖的方法，旨在通过在生成中间推理步骤之前整合策略性知识先验来优化 LLM 的表现。SCoT 在单个提示内采用两阶段方法：首先诱导出有效的解题策略，然后用该策略引导生成高质量的 CoT 路径和最终答案。 我们在八个具有挑战性的推理数据集上的实验表明了显著的改进，使用 Llama3-8b 模型时，在 GSM8K 数据集上提高了 21.05%，在 Tracking_Objects 数据集上提高了 24.13%。此外，我们将 SCoT 框架扩展为开发一种带有自动匹配演示的少量学习方法，取得了更强的结果。 这些发现强调了 SCoT 的有效性，突显了其在复杂推理任务中大幅提升 LLM 表现的潜力。