提示学习中思维链生成和增强方法综述

doi:10.11896/jsjkx.240700172

Abstract

Abstract: Large language models have made breakthroughs in several domains due to their superior language understanding and text generation capabilities.However,their performance in handling complex reasoning tasks is not very good and the accuracy needs to be improved.As a result,academics have proposed chain of thought(CoT),an innovative approach that aims to enhance the reasoning performance of models by allowing them to generate reasoning processes.In this paper,by comprehensively combing and deeply analyzing the existing research on CoT,we not only summarize its concepts and structural framework,but also explore the inference generation method and enhancement method in detail.The application of CoT in different task scenarios is further extensively explored,demonstrating the potential of CoT in enhancing model performance.At the same time,this paper also critically analyzes the limitations of CoT.Finally,this paper provides a prospective outlook on the future development of the chain-of-thinking strategy,aiming to provide guidance on the future research direction of CoT and to provide valuable references and insights for researchers.

Key words: Chain of thought, Large language model, Prompt learning, Reasoning generation, Reasoning enhancement

CLC Number:

TP301

ZHENG Mingqi, CHEN Xiaohui, LIU Bing, ZHANG Bing, ZHANG Ran. Survey of Chain-of-Thought Generation and Enhancement Methods in Prompt Learning[J].Computer Science, 2025, 52(1): 56-64.

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