Prompt-for-Prompt: Prompt Augmentation by Large Language Models
Authors: Eunkyung Park, Raymond K. Wong, Junbum Kwon


Reviewer paper summaries: The authors introduce a novel approach named Prompt-for-Prompt, which utilizes Large Language Models (LLMs) for prompt augmentation to enhance the generation of social media creatives. By analyzing the most popular Instagram posts from top brands, the study employs GPT-4 to generate effective prompts for leading text-to-image generators such as Midjourney, DALL-E 3, and Stable Diffusion. These LLM-augmented prompts are designed to improve AI-generated images by focusing on objectives, engagement strategy, lighting, and brand consistency.
Summary of strengths: The images generated using GPT-4's prompt augmentation align more closely with users' expectations.
Summary of weaknesses: It primarily involves compiling LLMs and T2I models, which appears to be an engineering effort. Utilizing LLMs to enhance prompts for text-to-image models does not offer much in terms of innovation.
Comments, suggestions and typos: Figure 2 does not appear to be suitable for presentation at an academic conference such as *CL.

There are some typos in the paper. Please prepare a preprint for review and correction.
Overall assessment scores: 2.5