  The springing up of Large Language Models (LLMs) has shifted the community
from single-task-orientated natural language processing (NLP) research to a
holistic end-to-end multi-task learning paradigm. Along this line of research
endeavors in the area, LLM-based prompting methods have attracted much
attention, partially due to the technological advantages brought by prompt
engineering (PE) as well as the underlying NLP principles disclosed by various
prompting methods. Traditional supervised learning usually requires training a
model based on labeled data and then making predictions. In contrast, PE
methods directly use the powerful capabilities of existing LLMs (i.e., GPT-3
and GPT-4) via composing appropriate prompts, especially under few-shot or
zero-shot scenarios. Facing the abundance of studies related to the prompting
and the ever-evolving nature of this field, this article aims to (i) illustrate
a novel perspective to review existing PE methods, within the well-established
communication theory framework; (ii) facilitate a better/deeper understanding
of developing trends of existing PE methods used in four typical tasks; (iii)
shed light on promising research directions for future PE methods.
