释放大语言模型提示工程的潜力
Unleashing the potential of prompt engineering for large language models
AI总结：
该综述系统梳理大语言模型与视觉语言模型的提示工程基础及前沿方法，分析相关安全风险与评估方式，为后续研究与应用提供结构化框架。
摘要：
本综述全面探讨了提示工程在释放大语言模型（Large Language Models, LLMs）能力方面的关键作用。人工智能（Artificial Intelligence, AI）从20世纪50年代诞生至今，历经先进神经网络与深度学习架构的发展，已在LLMs（如GPT-4o、Claude-3）和视觉语言模型（Vision-Language Models, VLMs，如CLIP、ALIGN）领域取得突破。提示工程是对输入进行结构化设计的过程，已成为最大化这些模型实用性与准确性的关键技术。本文探究了提示工程的基础与前沿方法，包括自一致性、思维链、生成式知识等技术，它们可显著提升模型性能。此外，本文还通过上下文优化（Context Optimization, CoOp）、条件上下文优化（Conditional Context Optimization, CoCoOp）、多模态提示学习（Multimodal Prompt Learning, MaPLe）等创新方法，研究了VLMs的提示方法。本次讨论的核心内容还包括AI安全问题，尤其是利用提示工程漏洞的对抗性攻击，本文全面梳理了缓解此类风险、增强模型鲁棒性的策略。本文还通过主观与客观指标探讨了提示方法的评估，确保对其有效性的分析可靠。本综述也体现了提示工程在提升AI能力中的重要作用，为未来研究与应用提供了结构化框架。
英文摘要：
This comprehensive review delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs). The development of Artificial Intelligence (AI), from its inception in the 1950s to the emergence of advanced neural networks and deep learning architectures, has made a breakthrough in LLMs, with models such as GPT-4o and Claude-3, and in Vision-Language Models (VLMs), with models such as CLIP and ALIGN. Prompt engineering is the process of structuring inputs, which has emerged as a crucial technique to maximize the utility and accuracy of these models. This paper explores both foundational and advanced methodologies of prompt engineering, including techniques such as self-consistency, chain-of-thought, and generated knowledge, which significantly enhance model performance. Additionally, it examines the prompt method of VLMs through innovative approaches such as Context Optimization (CoOp), Conditional Context Optimization (CoCoOp), and Multimodal Prompt Learning (MaPLe). Critical to this discussion is the aspect of AI security, particularly adversarial attacks that exploit vulnerabilities in prompt engineering. Strategies to mitigate these risks and enhance model robustness are thoroughly reviewed. The evaluation of prompt methods is also addressed through both subjective and objective metrics, ensuring a robust analysis of their efficacy. This review also reflects the essential role of prompt engineering in advancing AI capabilities, providing a structured framework for future research and application.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
