Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
作者: Hyungjoo Chae, Yeonghyeon Kim, Seungone Kim, Kai Tzu-iunn Ong, Beong-woo Kwak, Moohyeon Kim, Seonghwan Kim, Taeyoon Kwon, Jiwan Chung, Youngjae Yu, Jinyoung Yeo
分类: cs.CL
发布日期: 2024-04-03
备注: 38 pages, 4 figures
💡 一句话要点
提出Think-and-Execute框架以提升语言模型的算法推理能力
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 算法推理 语言模型 伪代码 推理框架 任务级逻辑 模型优化 自然语言处理
📋 核心要点
- 现有方法在算法推理中难以动态生成可执行代码，且实例特定的逻辑无法复用。
- 提出的Think-and-Execute框架通过发现共享的任务级逻辑并使用伪代码表达，分步进行推理。
- 实验结果显示，该方法在多个算法推理任务上显著提升了语言模型的推理能力，超越了多种基线方法。
📝 摘要（中文）
算法推理是理解问题背后复杂模式并将其分解为解决方案步骤的能力。尽管大型语言模型在其他推理任务中表现良好，但在算法推理方面仍面临挑战。近期研究尝试使用编程语言表达解决逻辑，但在单次推理中动态生成可执行代码并不简单。本文提出了Think-and-Execute框架，将推理过程分为两个步骤：首先发现任务级逻辑并用伪代码表达，然后针对每个实例定制伪代码并模拟执行。通过在七个算法推理任务上的广泛实验，证明了该方法的有效性，优于现有的实例特定推理方法。
🔬 方法详解
问题定义：本文旨在解决大型语言模型在算法推理任务中动态生成可执行代码的困难。现有方法往往依赖于实例特定的逻辑，导致无法复用和效率低下。
核心思路：提出的Think-and-Execute框架通过分解推理过程，首先识别任务级逻辑并用伪代码表达，随后针对具体实例进行定制和模拟执行。这种设计旨在提高推理的灵活性和准确性。
技术框架：该框架分为两个主要模块：Think和Execute。在Think阶段，识别并表达任务级逻辑；在Execute阶段，定制伪代码并模拟执行。
关键创新：最重要的创新在于通过伪代码的使用，提升了语言模型的推理能力，尤其是在面对复杂的算法问题时，与传统的自然语言指令相比，伪代码提供了更清晰的指导。
关键设计：在伪代码生成过程中，采用了特定的逻辑结构和语法规则，以确保生成的代码能够有效地表达任务逻辑，并在执行阶段进行适当的调整以适应不同实例的需求。实验中使用了多种基线方法进行对比，验证了该设计的有效性。
🖼️ 关键图片
📊 实验亮点
实验结果表明，Think-and-Execute框架在七个算法推理任务上显著提升了语言模型的推理能力，相比于实例特定推理方法（如CoT和PoT），在准确性和效率上均有明显改善，展示了任务级逻辑发现的重要性。
🎯 应用场景
该研究的潜在应用领域包括教育、编程辅助工具和自动化决策系统。通过提升语言模型的算法推理能力，可以在更复杂的任务中实现更高效的解决方案，未来可能对智能助手和编程教育产生深远影响。
📄 摘要（原文）
Algorithmic reasoning refers to the ability to understand the complex patterns behind the problem and decompose them into a sequence of reasoning steps towards the solution. Such nature of algorithmic reasoning makes it a challenge for large language models (LLMs), even though they have demonstrated promising performance in other reasoning tasks. Within this context, some recent studies use programming languages (e.g., Python) to express the necessary logic for solving a given instance/question (e.g., Program-of-Thought) as inspired by their strict and precise syntaxes. However, it is non-trivial to write an executable code that expresses the correct logic on the fly within a single inference call. Also, the code generated specifically for an instance cannot be reused for others, even if they are from the same task and might require identical logic to solve. This paper presents Think-and-Execute, a novel framework that decomposes the reasoning process of language models into two steps. (1) In Think, we discover a task-level logic that is shared across all instances for solving a given task and then express the logic with pseudocode; (2) In Execute, we further tailor the generated pseudocode to each instance and simulate the execution of the code. With extensive experiments on seven algorithmic reasoning tasks, we demonstrate the effectiveness of Think-and-Execute. Our approach better improves LMs' reasoning compared to several strong baselines performing instance-specific reasoning (e.g., CoT and PoT), suggesting the helpfulness of discovering task-level logic. Also, we show that compared to natural language, pseudocode can better guide the reasoning of LMs, even though they are trained to follow natural language instructions.