A Practical Survey on Zero-shot Prompt Design for In-context Learning

Columbia University, New York City, USA; Li, Yinheng

doi:10.26615/978-954-452-092-2_069

Cited by 69Topic ModelingDomain Adaptation and Few-Shot LearningNatural Language Processing Techniques

A Practical Survey on Zero-shot Prompt Design for In-context Learning

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Columbia University, New York City, USA, Li, Yinheng·2023·Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Processings

DOI 10.26615/978-954-452-092-2_069

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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, fewshot, 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.

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Authors: Columbia University, N. Y. C., & Li, Y.
Affiliations: Columbia University (US)
Corresponding author: Li, Yinheng
ORCID: Li, Yinheng
Venue: Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Processings
Publisher: INCOMA Ltd., Shoumen, BULGARIA
Published: 2023
Pages: 641-647
ISSN: 1313-8502 (ISSN-L)
DOI: 10.26615/978-954-452-092-2_069
OpenAlex: W4387074826
Cited by: 68
References: 28
Citations by year: 2026: 29 · 2025: 28 · 2024: 9 · 2023: 2
FWCI: 12.04
Type: conference paper
Language: en
Open access: bronze
doi.org
Locations: International conference Recent advances in natural language processing (publishedVersion)
arXiv (Cornell University) (submittedVersion, cc-by)
arXiv (Cornell University) (cc-by)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords: in-context learning, prompt engineering, large language models

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A Practical Survey on Zero-shot Prompt Design for In-context Learning

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