Towards Better Chain-of-Thought Prompting Strategies: A Survey

October 08, 2023 ยท The Cartographer ยท ๐Ÿ› arXiv.org

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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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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