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