Personalized Preference Optimization for Text-to-Image Generation using Large Language Models
Authors: Kyungsu Kim, Haesun Joung, Dohoon Lee, Seonghyeon Kang, Kyogu Lee


Reviewer paper summaries: Previous research has focused on optimizing for average preferences, but text-to-image tasks require a personalized approach due to diverse individual preferences. This work proposes a two-stage framework for personalized preference optimization in text-to-image generation. The first stage, Personalized Image Aesthetic Assessment (PIAA), learns user preferences from a small amount of user image rating data. The second stage, prompt optimization, adjusts the text-to-image model’s prompt to generate images that score highly according to the learned preference model.
Summary of strengths: This work introduces a two-stage framework for personalized preference optimization in text-to-image generation. The first stage, PIAA, effectively learns user preferences from minimal image rating data. The second stage optimizes prompts to produce images that align with these preferences.
Summary of weaknesses: In Figure 1, the prompt optimizer appears to be a crucial element of the entire system, but detailed explanations, particularly regarding its practical implementation, are lacking. Is it a multi-modal large model capable of processing multiple inputs, such as texts, images, and scores?

The metrics, such as SROCC, require more comprehensive explanations. Additionally, the method "MAML" should be clearly defined.
Comments, suggestions and typos: Typos: In Figure 1, “Prompt Optimzer” should be “Prompt Optimizer”
Overall assessment scores: 3