系统分析提示工程的优劣，提升人机对话效果
Exploring Prompt Engineering: A Systematic Review with SWOT Analysis
- 基于语言学原则，梳理模板与微调等提示技术
- 识别各方法在准确率与稳定性上的差异
- 适合研究人机交互与大模型优化的学者参考
本文对大型语言模型中的提示工程技术进行了全面的SWOT分析。基于语言学原理，考察了包括模板方法和微调在内的多种技术，揭示其优势、劣势、机遇与威胁。研究发现有助于改善AI理解人类提示的能力，增强人机交互效果。分析涵盖各类技术面临的问题与挑战，为未来研究提供方向，旨在提升提示工程在优化人机通信中的有效性。
原文摘要 · Abstract (English)
In this paper, we conduct a comprehensive SWOT analysis of prompt engineering techniques within the realm of Large Language Models (LLMs). Emphasizing linguistic principles, we examine various techniques to identify their strengths, weaknesses, opportunities, and threats. Our findings provide insights into enhancing AI interactions and improving language model comprehension of human prompts. The analysis covers techniques including template-based approaches and fine-tuning, addressing the problems and challenges associated with each. The conclusion offers future research directions aimed at advancing the effectiveness of prompt engineering in optimizing human-machine communication.
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