计算机工程与应用2025，Vol.61Issue(6)：64-83,20.DOI:10.3778/j.issn.1002-8331.2405-0069
结构化思维提示增强大语言模型推理能力综述
Review on Enhancing Reasoning Abilities of Large Language Model Through Structured Thinking Prompts
摘要
Abstract
In recent years,the field of natural language processing has witnessed the rapid rise of prompt learning,particu-larly with the outstanding performance demonstrated in large language models such as GPT and Claude.It has sparked widespread academic interest and extensive research.In view of its immense potential,how to understand the underlying mechanisms of prompt learning and develope more efficient prompt design strategies have become pressing issues in the field.This paper introduces the innovative concept of structured thinking prompts aiming to systematically analyze and reconstruct existing prompt learning paradigms from the perspective of human cognitive logic.The paper explains the basic principles of prompt learning and delves into how cognitive science theories provide inspiration and guidance for prompt design.It then constructs a comprehensive structured thinking prompt framework,detailing four core methods:chain-of-thought prompts,decomposition-based prompts,framework-based prompts,and team collaboration-based prompts.These methods highlight the unique value of structured thinking prompts in enhancing model performance and generalization capabilities.Furthermore,the paper proposes an evaluation system for structured thinking prompts,aiming at scientifically and objectively assessing their effectiveness.It also explores various optimization strategies to further improve the efficiency and effectiveness of prompt design.Additionally,the challenges currently faced by structured thinking prompts,particularly the issue of rising computational costs,are discussed,providing direction for future research.The paper envisions the future development trends of structured thinking prompts,emphasizing their pivotal role and potential opportunities in advancing not only natural language processing but also the broader field of artificial intelligence.关键词
大语言模型/提示学习/结构化思维提示Key words
large language model/prompt learning/structured thinking prompt分类
计算机与自动化引用本文复制引用
陶江垚,奚雪峰,盛胜利,崔志明,左严..结构化思维提示增强大语言模型推理能力综述[J].计算机工程与应用,2025,61(6):64-83,20.基金项目
国家自然科学基金(62176175) （62176175）
江苏省"六大人才高峰"高层次人才项目(XYDXX-086) （XYDXX-086）
苏州市水利水务科技项目(2023008). （2023008）
