Survey of Chain-of-Thought Generation and Enhancement Methods in Prompt Learning
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 com-bing 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.
Chain of thoughtLarge language modelPrompt learningReasoning generationReasoning enhancement
万方数据
维普
