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Survey

A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models

AI Group, WeBank Co., Ltd, Shenzhen 518000, China
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Abstract

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 (e.g., 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 1) illustrate a novel perspective to review existing PE methods within the well-established communication theory framework, 2) facilitate a better/deeper understanding of developing trends of existing PE methods used in three typical tasks, and 3) shed light on promising research directions for future PE methods.

Keywords

prompting methodlarge language modelcommunication theory

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Journal of Computer Science and Technology
Volume 39 Issue 4,
August 2024
Pages 984-1004
DOI: 10.1007/s11390-024-4058-8
Cite this article:
Song Y-F, He Y-Q, Zhao X-F, et al. A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models. Journal of Computer Science and Technology, 2024, 39(4): 984-1004. https://doi.org/10.1007/s11390-024-4058-8

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Received: 21 December 2023
Accepted: 12 April 2024
Published: 20 September 2024
© Institute of Computing Technology, Chinese Academy of Sciences 2024
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