Few-Shot and Zero-Shot Learning with Large Language Models: Prompting Strategies and In-Context Learning

This paper provides an in-depth overview of fewshot and zero-shot learning using large language models (LLMs). The paper highlighted their architectures, training methods, and adaptation methods. Also, some domain-specific LLMs, knowledge distillation, and common deployment strategies are introduced. Then, this paper also explores data generation and zero-shot learning methods, showing their potential for adapting to new tasks with minimal labeled data. In case studies of this paper, not only the application of LLMs in specialized fields is highlighted, but also unresolved problems and challenges are identified. Additionally, this paper also critically analyzes the evaluation benchmarks of LLM performance and discusses future research directions, including architectural innovations and training strategies. Finally, this paper discusses the impact at the technical and social levels, and this paper provides researchers and stakeholders with a comprehensive knowledge framework to advance LLM research and promote research in few-shot and zero-shot learning.