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

arXiv: https://arxiv.org/abs/2604.27553 · HTML: https://mlsanity.com/paper/2604.27553/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 3 of 3)

- [Performance Gap in Entity Knowledge Extraction Across Modalities in Vision Language Models](https://mlsanity.com/paper/2412.14133/) (2024)
- [Texture or Semantics? Vision-Language Models Get Lost in Font Recognition](https://mlsanity.com/paper/2503.23768/) (2025)
- [Can VLMs Recall Factual Associations From Visual References?](https://mlsanity.com/paper/2508.18297/) (2025)

## Built on by (most cited first, 1 of 1)

- [Visual Input and Its Framing Affect Attribute-based Descriptions Produced by Large Vision-Language Models](https://mlsanity.com/paper/2609.18345/) (2026)

## Abstract (arXiv)

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.