无线电通信技术2025，Vol.51Issue(5)：877-887,11.DOI:10.3969/j.issn.1003-3114.2025.05.001
大语言模型中的思维链技术综述
Survey of Chain of Thought Techniques in Large Language Models
摘要
Abstract
Driven by the ever-increasing supply of computational resources,the parameter size of Large Language Models(LLMs)continues to expand and their task performance in natural language processing has become more superior.However,there are still limi-tations when faced with reasoning problems,especially in common-sense reasoning or mathematical problems.Chain of Thought(CoT)significantly improves its ability to solve problems in different domains by guiding the model to generate reasoning steps.In this paper,we not only sort out the theoretical foundation system and technical evolution of CoT from the perspective of training method,but also further discuss application scenarios such as government service and enterprise digitalisation.Finally,in the light of the development trend of Artificial Intelligence(AI),the paper discusses the essential role of CoT in the development of LLMs towards a higher cogni-tive level from the perspective of the degree of AI,and points out the challenges and technical bottlenecks that need to be solved at the present time.关键词
大语言模型/思维链/推理/人工智能Key words
LLMs/CoT/reasoning/AI分类
信息技术与安全科学引用本文复制引用
杜家乐,陈曙东,叶亮,王尔刚,赵一同..大语言模型中的思维链技术综述[J].无线电通信技术,2025,51(5):877-887,11.基金项目
北京市科技计划项目(Z231100001323004)Beijing Science and Technology Plan(Z231100001323004) （Z231100001323004）
