Top Research Papers on Prompt Engineering

Prompt engineering is an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT. Prompts are instructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities) of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM. This paper describes a catalog of prompt engineering techniques presented in pattern form that have been applied to solve common problems when conversing with LLMs. Prompt patterns are a knowledge transfer method...

Artificial intelligence-powered generative language models (GLMs), such as ChatGPT, Perplexity AI, and Google Bard, have the potential to provide personalized learning, unlimited practice opportunities, and interactive engagement 24/7, with immediate feedback. However, to fully utilize GLMs, properly formulated instructions are essential. Prompt engineering is a systematic approach to effectively communicating with GLMs to achieve the desired results. Well-crafted prompts yield good responses from the GLM, while poorly constructed prompts will lead to unsatisfactory responses. Besides the chal...

The development of prompt engineering will be provided and its significant contributions to healthcare natural language processing applications such as question-answering systems, text summarization, and machine translation will be emphasized.

The study underscores the importance of prompt structures in jailbreaking LLMs and discusses the challenges of robust jailbreak prompt generation and prevention.

Visual prompt engineering is a fundamental methodology in the field of visual and image artificial general intelligence. As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident. Designing suitable prompts for specific visual tasks has emerged as a meaningful research direction. This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering. We present influential large models in the visual domain ...

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. ...

It is shown that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts.

Critical to this discussion is the role of prompt engineering in artificial intelligence (AI) security, particularly in terms of defending against adversarial attacks that exploit vulnerabilities in LLMs.

It is argued for the integration of AI educational content into current curricula to enable a hybrid intelligent society in which students can effectively use generative AI tools such as ChatGPT.

The paper underscores how the natural language capabilities of LLM AI tools can help students and educators transition from passive recipients to active co-creators of their learning experiences, and charts the evolving trajectory of LLM AI as a tool poised to reshape educational practices and assumptions.

It is revealed that different prompts had variable effects across various models, and the gpt-4-Web with ROT prompt was the most consistent.

This paper summarizes the current state of research about prompt engineering and aims at providing practical recommendations for the wide range of health care professionals to improve their interactions with LLMs.

A study exploring what prompt keywords and model hyperparameters can help produce coherent outputs from text-to-image generative models, structured to include subject and style keywords and investigates success and failure modes of these prompts.

To better understand the role of artificial intelligence in the development of entrepreneurial rhetoric, we examine how generative language models such as ChatGPT serve as viable tools for content creation. Using an established framework for examining CEO celebrity (Creator, Transformer, Rebel, and Savior), we illustrate how such models can effectively produce and refine elevator pitches, social media pitches, and crowdfunding pitches commonly used in the study of entrepreneurial rhetoric. We demonstrate ChatGPT's ability to mimic each celebrity CEO archetype by prompting language in the style...

A systematic analysis of recent advancements in prompt engineering enables a better understanding of this rapidly developing field and facilitates future research by illuminating open challenges and opportunities for prompt engineering.

This article introduces the CLEAR Framework for Prompt Engineering, designed to optimize interactions with AI language models like ChatGPT. The framework encompasses five core principles—Concise, Logical, Explicit, Adaptive, and Reflective—that facilitate more effective AI-generated content evaluation and creation. Additionally, the article discusses technical aspects of prompts, such as tokens, temperature, and top-p settings. By integrating the CLEAR Framework into information literacy instruction, academic librarians can empower students with critical thinking skills for the ChatGPT era and...

This paper presents Prompt-aligned Gradient, dubbed ProGrad to prevent prompt tuning from forgetting the general knowledge learned from VLMs, and demonstrates the stronger few-shot generalization ability of ProGrad over state-of-the-art prompt tuning methods.

A workflow that allows users to first focus on model feedback using small data before moving on to a large data regime that allows empirical grounding of promising prompts using quantitative measures of the task, and then allows easy deployment of the newly created ad-hoc models.

A clinical task-specific prompt framework, incorporating medical knowledge and training samples, significantly enhances GPT models' feasibility for potential clinical applications and suggests a promising direction in leveraging LLMs for clinical NER tasks.

Evaluating the performance of Copilot on a publicly available dataset of 166 programming problems finds that it successfully solves around half of these problems on its very first attempt, and that it solves 60% of the remaining problems using only natural language changes to the problem description.