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

TL;DR

This paper reviews various prompt design strategies for in-context learning with large language models, emphasizing their impact on NLP task performance and the importance of evaluation methods.

Contribution

It provides a comprehensive overview of prompt design approaches, including manual, optimization, and evaluation techniques, highlighting their roles in enhancing LLM performance.

Findings

Prompt design significantly influences LLM performance.

Combining manual and automated prompt techniques improves results.

Evaluation metrics are crucial for assessing prompt effectiveness.

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.