Revealing the Impact of Visual Text Style on Attribute-based Descriptions Produced by Large Visual Language Models

2026-04-30

AI Summary

This study investigates whether visual text styling—such as font, color, and size—can still interfere with the conceptual attribute descriptions generated by large vision-language models (LVLMs), even when the underlying concepts are correctly recognized. Through controlled experiments, the work systematically compares the effects of functional versus decorative text styles on LVLM outputs and reveals, for the first time, a phenomenon termed “style leakage”: despite accurate semantic recognition, decorative styling significantly distorts the generated attribute descriptions. This finding underscores the subtle yet substantial influence of visual appearance on semantic reasoning in multimodal systems and calls for the integration of style-aware evaluation and mitigation mechanisms in future model development and deployment.

Abstract

When the visual style of text is considered, a wide variety can be observed in font, color, and size. However, when a word is read, its meaning is independent of the style in which it has been written or rendered. In this paper, we investigate whether, and how, the style in which a word is visualized in an image impacts the description that a Large Visual Language Model (LVLM) provides for the concept to which that word refers. Specifically, we investigate how functional text styles (readability-oriented, e.g., black sans-serif) versus decorative styles (display-oriented, e.g., colored cursive/script) affect LVLMs' descriptions of a concept in terms of the attributes of that concept. Our experiments study the situation in which the LVLM is able to correctly identify the concept referred to by a visual text, i.e., by a word or words rendered as an image, and in which the visual text style should not influence the attribute-based description that the LVLM produces. Our experimental results reveal that even when the concept is correctly identified, text style influences the model's attribute-based descriptions of the concept. Our findings demonstrate non-trivial style leakage from text style into semantic inference and motivate style-aware evaluation and mitigation for LVLM-based multimedia systems.

Problem

• visual text style
• Large Visual Language Models
• attribute-based descriptions
• style leakage
• semantic inference

Innovation

• visual text style
• Large Visual Language Models
• attribute-based description
• style leakage
• style-aware evaluation