Unleashing the potential of prompt engineering for large language models

TL;DR

This review highlights the importance of prompt engineering in enhancing large language models and vision-language models, discussing methodologies, security concerns, and evaluation metrics to guide future AI advancements.

Contribution

It provides a comprehensive overview of prompt engineering techniques, including innovative methods and security strategies, for maximizing model performance and robustness.

Findings

Prompt engineering significantly improves LLM and VLM performance.

Advanced techniques like self-consistency and chain-of-thought enhance accuracy.

Security vulnerabilities in prompt methods require effective mitigation strategies.

Abstract

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.