The Impact of Reasoning Step Length on Large Language Models

📄 arXiv: 2401.04925v4 📥 PDF

作者: Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du

分类: cs.CL, cs.AI

发布日期: 2024-01-10 (更新: 2024-06-22)

备注: Findings of ACL 2024

🔗 代码/项目: GITHUB

💡 一句话要点

研究推理步骤长度对大型语言模型的影响

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 推理步骤 大型语言模型 链式思维 模型性能 复杂任务 实证实验 推理能力

📋 核心要点

  1. 现有研究对推理步骤长度对大型语言模型性能的影响缺乏深入探讨，限制了其在复杂问题解决中的应用。
  2. 本文通过设计实验，系统性地研究了推理步骤长度对LLMs推理能力的影响，提出了优化提示的方法。
  3. 实验结果表明，推理步骤的长度显著影响模型性能，简单任务需要较少步骤，而复杂任务则需更多推理步骤。

📝 摘要（中文）

链式思维（CoT）在提升大型语言模型（LLMs）推理能力方面具有重要意义。然而，CoT的有效性与提示中推理步骤长度之间的关系仍然不明。为此，本文通过多项实证实验探讨了这一关系。研究发现，延长推理步骤显著提升LLMs的推理能力，而缩短步骤则会显著降低能力。此外，即使使用不正确的推理，只要保持所需的推理长度，仍能获得良好结果。最后，推理步骤的优势与任务复杂性相关，简单任务需要较少步骤，而复杂任务则从较长的推理序列中获益显著。

🔬 方法详解

问题定义：本文旨在解决推理步骤长度对大型语言模型推理能力影响的未知性，现有方法未能系统探讨这一问题。

核心思路：通过扩展和压缩CoT演示中的推理步骤，保持其他因素不变，探索推理步骤长度与模型性能之间的关系。

技术框架：研究设计了多组实验，分别对推理步骤进行扩展和压缩，分析其对不同数据集上LLMs推理能力的影响。

关键创新：发现推理步骤长度对模型性能的显著影响，尤其是在复杂任务中，提供了实用的指导以优化LLMs的使用。

关键设计：实验中保持了推理信息的一致性，重点考察了推理步骤的数量及其对模型输出的影响，确保了实验的严谨性。

🖼️ 关键图片

fig_0
fig_1
fig_2

📊 实验亮点

实验结果显示，推理步骤的延长显著提升了模型在多个数据集上的推理能力，尤其是在复杂任务中，推理步骤增加带来的性能提升幅度可达20%以上。这一发现为优化LLMs的使用提供了新的视角。

🎯 应用场景

该研究为大型语言模型在复杂问题解决中的应用提供了重要指导，尤其是在教育、医疗和法律等领域，能够帮助模型更有效地进行推理和决策。未来，研究结果可用于优化LLMs的训练和应用策略，提高其在实际场景中的表现。

📄 摘要（原文）

Chain of Thought (CoT) is significant in improving the reasoning abilities of large language models (LLMs). However, the correlation between the effectiveness of CoT and the length of reasoning steps in prompts remains largely unknown. To shed light on this, we have conducted several empirical experiments to explore the relations. Specifically, we design experiments that expand and compress the rationale reasoning steps within CoT demonstrations while keeping all other factors constant. We have the following key findings. First, the results indicate that lengthening the reasoning steps in prompts, even without adding new information into the prompt, considerably enhances LLMs' reasoning abilities across multiple datasets. Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models. This finding highlights the importance of the number of steps in CoT prompts and provides practical guidance to make better use of LLMs' potential in complex problem-solving scenarios. Second, we also investigated the relationship between the performance of CoT and the rationales used in demonstrations. Surprisingly, the result shows that even incorrect rationales can yield favorable outcomes if they maintain the requisite length of inference. Third, we observed that the advantages of increasing reasoning steps are task-dependent: simpler tasks require fewer steps, whereas complex tasks gain significantly from longer inference sequences. The code is available at https://github.com/MingyuJ666/The-Impact-of-Reasoning-Step-Length-on-Large-Language-Models