Deciphering Personalization: Towards Fine-Grained Explainability in Natural Language for Personalized Image Generation Models

ICLR 2026 Conference Submission14505 Authors

Keywords: Image Generation Model, Personalization, Fine-grained Explainability, Natural Language

TL;DR: This paper presents a new technique for fine-grained explainability in natural language for personalized image generation models. It provides descriptions about each aspect of personalization, with quantitative scores of the level of personalization.

Abstract: Image generation models are usually personalized in practical uses in order to better meet the individual users' heterogeneous needs, but most personalized models lack explainability about how they are being personalized. Such explainability can be provided via visual features in generated images, but is difficult for human users to understand. Explainability in natural language is a better choice, but the existing approaches to explainability in natural language are limited to be coarse-grained. They are unable to precisely identify the multiple aspects of personalization, as well as the varying levels of personalization in each aspect. To address such limitation, in this paper we present a new technique, namely FineXL, towards Fine-grained eXplainability in natural Language for personalized image generation models. FineXL can provide natural language descriptions about each distinct aspect of personalization, along with quantitative scores indicating the level of each aspect of personalization. Experiment results show that FineXL can improve the accuracy of explainability by 56\%, when different personalization scenarios are applied to multiple types of image generation models.

Primary Area: interpretability and explainable AI

Submission Number: 14505