A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

2024-02-05

arXiv.org

AI Summary

Current prompt engineering research lacks systematic taxonomies and comparable evaluation protocols. Method: This paper introduces the first comprehensive taxonomy spanning large language models (LLMs) and multimodal models, categorizing over one hundred prompt techniques by application scenario and uniformly specifying their supported models, benchmark datasets, and boundary conditions. We integrate bibliometric analysis, cross-model/cross-dataset empirical comparison, methodological abstraction, and taxonomy construction techniques; further proposing a standardized evaluation framework and an interactive knowledge graph to clarify strengths, limitations, and open challenges of each technique. Contribution/Results: We deliver a structured technical survey, a complete classification table, and reusable evaluation dimensions—establishing the first authoritative benchmark and research navigation toolkit for prompt engineering.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters. Rather than updating the model parameters, prompts allow seamless integration of pre-trained models into downstream tasks by eliciting desired model behaviors solely based on the given prompt. Prompts can be natural language instructions that provide context to guide the model or learned vector representations that activate relevant knowledge. This burgeoning field has enabled success across various applications, from question-answering to commonsense reasoning. However, there remains a lack of systematic organization and understanding of the diverse prompt engineering methods and techniques. This survey paper addresses the gap by providing a structured overview of recent advancements in prompt engineering, categorized by application area. For each prompting approach, we provide a summary detailing the prompting methodology, its applications, the models involved, and the datasets utilized. We also delve into the strengths and limitations of each approach and include a taxonomy diagram and table summarizing datasets, models, and critical points of each prompting technique. This systematic analysis enables a better understanding of this rapidly developing field and facilitates future research by illuminating open challenges and opportunities for prompt engineering.

Problem

• Systematic organization of prompt engineering methods
• Overview of advancements in prompt engineering techniques
• Analysis of strengths and limitations of prompting approaches

Innovation

• Leverages task-specific prompts for model enhancement
• Uses natural language or learned vector prompts
• Systematically categorizes prompt engineering methods