Autoregressive Large Language Models have transformed the landscape of
Natural Language Processing. Pre-train and prompt paradigm has replaced the
conventional approach of pre-training and fine-tuning for many downstream NLP
tasks. This shift has been possible largely due to LLMs and innovative
prompting techniques. LLMs have shown great promise for a variety of downstream
tasks owing to their vast parameters and huge datasets that they are
pre-trained on. However, in order to fully realize their potential, their
outputs must be guided towards the desired outcomes. Prompting, in which a
specific input or instruction is provided to guide the LLMs toward the intended
output, has become a tool for achieving this goal. In this paper, we discuss
the various prompting techniques that have been applied to fully harness the
power of LLMs. We present a taxonomy of existing literature on prompting
techniques and provide a concise survey based on this taxonomy. Further, we
identify some open problems in the realm of prompting in autoregressive LLMs
which could serve as a direction for future research.

自回归大语言模型已经改变了自然语言处理领域的格局。基于预训练和提示范式的方法已经取代了常规的预训练和微调方法用于许多下游自然语言处理任务。本文讨论了已经在自回归大语言模型上使用的各种提示技术，提供了一个基于这个分类体系的简明调研，并且确定了自回归大语言模型中提示领域的一些未解决问题，可作为未来研究的方向。