Towards Better Chain-of-Thought Prompting Strategies: A Survey
October 08, 2023 ยท The Cartographer ยท ๐ arXiv.org
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"Title-pattern auto-detect: Towards Better Chain-of-Thought Prompting Strategies: A Survey"
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Authors
Zihan Yu, Liang He, Zhen Wu, Xinyu Dai, Jiajun Chen
arXiv ID
2310.04959
Category
cs.CL: Computation & Language
Citations
85
Venue
arXiv.org
Last Checked
1 day ago
Abstract
Chain-of-Thought (CoT), a step-wise and coherent reasoning chain, shows its impressive strength when used as a prompting strategy for large language models (LLM). Recent years, the prominent effect of CoT prompting has attracted emerging research. However, there still lacks of a systematic summary about key factors of CoT prompting and comprehensive guide for prompts utilizing. For a deeper understanding about CoT prompting, we survey on a wide range of current research, presenting a systematic and comprehensive analysis on several factors that may influence the effect of CoT prompting, and introduce how to better apply it in different applications under these discussions. We further analyze the challenges and propose some future directions about CoT prompting. This survey could provide an overall reference on related research.
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