WorkDifferentWithAI.com Academic Paper Alert!
Written by Jonas Oppenlaender, Rhema Linder, Johanna Silvennoinen
Category: “AI for IT”
Article Section: Prompt Engineering
Publication Date: 2023-03-13
SEO Description: The paper investigates prompt engineering, which is an emerging research area, especially for text-to-image generation.
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Oppenlaender, Jonas, et al.
Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering. arXiv:2303.13534, arXiv, 13 Mar. 2023,
https://doi.org/10.48550/arXiv.2303.13534.
Claude.ai-Generated Paper Summary
Based on a quick review of the paper you attached, here are some key points about its novelty and potential commercial applications:
Novelty:
- The paper investigates prompt engineering, which is an emerging research area, especially for text-to-image generation. There is still limited academic research on this topic, so the studies presented provide new insights.
- The paper takes a human-centered approach by conducting user studies to understand whether prompt engineering is an intuitive skill or one that requires learning and expertise. This provides a novel perspective compared to more technical ML research.
- The studies explore different aspects of prompt engineering including assessing prompt quality, writing prompts, and revising prompts. The multi-study approach provides a comprehensive investigation into this skill.
- The paper includes one of the first investigations into using paid crowd workers for experiments on text-to-image generation. The recommendations for conducting such studies are novel and useful.
Commercial Applications:
- The findings suggest opportunities for developing educational resources and tools to teach prompt engineering, as it seems to require practice and expertise.
- Companies could develop specialized interfaces and workflows to simplify prompt engineering for novice users. This could expand access and use of text-to-image generation.
- The skills of prompt engineering could be valuable for creative professionals, presenting career opportunities in creative industries. Training programs could be developed.
- Tools for iterating on and improving prompts more efficiently could have commercial potential based on the difficulties participants faced.
- The prompts and images collected during the studies, with permission, could be a valuable dataset for further text-to-image research and development.
In summary, the paper provides new insights into prompt engineering through its human-centered approach and novel experimental studies. The findings point to opportunities around training, tools, and services to make text-to-image generation more accessible and impactful. The paper makes a solid academic contribution while also surfacing commercial possibilities.
Keywords
Prompt Engineering, AI Art, Text-to-Image Generation, Creative Skill, Crowdsourcing Platform
Author’s Abstract
Humankind is entering a novel era of creativity – an era in which anybody can synthesize digital content. The paradigm under which this revolution takes place is prompt-based learning (or in-context learning). This paradigm has found fruitful application in text-to-image generation where it is being used to synthesize digital images from zero-shot text prompts in natural language for the purpose of creating AI art. This activity is referred to as prompt engineering – the practice of iteratively crafting prompts to generate and improve images. In this paper, we investigate prompt engineering as a novel creative skill for creating prompt-based art. In three studies with participants recruited from a crowdsourcing platform, we explore whether untrained participants could 1) recognize the quality of prompts, 2) write prompts, and 3) improve their prompts. Our results indicate that participants could assess the quality of prompts and respective images. This ability increased with the participants’ experience and interest in art. Participants further were able to write prompts in rich descriptive language. However, even though participants were specifically instructed to generate artworks, participants’ prompts were missing the specific vocabulary needed to apply a certain style to the generated images. Our results suggest that prompt engineering is a learned skill that requires expertise and practice. Based on our findings and experience with running our studies with participants recruited from a crowdsourcing platform, we provide ten recommendations for conducting experimental research on text-to-image generation and prompt engineering with a paid crowd. Our studies offer a deeper understanding of prompt engineering thereby opening up avenues for research on the future of prompt engineering. We conclude by speculating on four possible futures of prompt engineering.
Read the full paper here
