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        <title>A Practical Survey on Zero-Shot Prompt Design for In-Context Learning</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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    <abstract>The remarkable advancements in large language models (LLMs) have brought about significant improvements in Natural Language Processing(NLP) tasks. This paper presents a comprehensive review of in-context learning techniques, focusing on different types of prompts, including discrete, continuous, few-shot, and zero-shot, and their impact on LLM performance. We explore various approaches to prompt design, such as manual design, optimization algorithms, and evaluation methods, to optimize LLM performance across diverse tasks. Our review covers key research studies in prompt engineering, discussing their methodologies and contributions to the field. We also delve into the challenges faced in evaluating prompt performance, given the absence of a single “best” prompt and the importance of considering multiple metrics. In conclusion, the paper highlights the critical role of prompt design in harnessing the full potential of LLMs and provides insights into the combination of manual design, optimization techniques, and rigorous evaluation for more effective and efficient use of LLMs in various NLP tasks.</abstract>
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        <url>https://aclanthology.org/2023.ranlp-1.69</url>
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%0 Conference Proceedings
%T A Practical Survey on Zero-Shot Prompt Design for In-Context Learning
%A Li, Yinheng
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F li-2023-practical
%X The remarkable advancements in large language models (LLMs) have brought about significant improvements in Natural Language Processing(NLP) tasks. This paper presents a comprehensive review of in-context learning techniques, focusing on different types of prompts, including discrete, continuous, few-shot, and zero-shot, and their impact on LLM performance. We explore various approaches to prompt design, such as manual design, optimization algorithms, and evaluation methods, to optimize LLM performance across diverse tasks. Our review covers key research studies in prompt engineering, discussing their methodologies and contributions to the field. We also delve into the challenges faced in evaluating prompt performance, given the absence of a single “best” prompt and the importance of considering multiple metrics. In conclusion, the paper highlights the critical role of prompt design in harnessing the full potential of LLMs and provides insights into the combination of manual design, optimization techniques, and rigorous evaluation for more effective and efficient use of LLMs in various NLP tasks.
%U https://aclanthology.org/2023.ranlp-1.69
%P 641-647

Markdown (Informal)

[A Practical Survey on Zero-Shot Prompt Design for In-Context Learning](https://aclanthology.org/2023.ranlp-1.69) (Li, RANLP 2023)

• A Practical Survey on Zero-Shot Prompt Design for In-Context Learning (Li, RANLP 2023)

ACL

• Yinheng Li. 2023. A Practical Survey on Zero-Shot Prompt Design for In-Context Learning. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 641–647, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.