Enterprise AI Analysis
SoS: Analysis of Surface-over-Semantics in Multilingual Text-To-Image Generation
This paper introduces Surface-over-Semantics (SoS), a novel measure to quantify how Text-to-Image (T2I) models prioritize input language surface forms over semantic meaning, especially in multilingual contexts. The study reveals significant surface tendencies and cultural stereotyping across various models and languages.
Key Findings: Quantifiable Impact
Our analysis quantifies critical aspects of multilingual T2I model behavior, highlighting areas of significant surface-over-semantics tendencies and their impact on generated content.
171
Cultural Identities Covered
14
Languages Analyzed
7
T2I Models Evaluated
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The research followed a structured evaluation setup to systematically analyze T2I model behavior across languages and cultures.
Enterprise Process Flow
Prompt Translation (13 languages + English)
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Image Creation (7 T2I Models)
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SoS Quantification & Visual Manifestation Analysis
A novel embedding-based SoS score was introduced, achieving 74.0% accuracy in identifying surface-over-semantics tendencies, providing a robust, language-independent evaluation metric.
74.0%
SoS Accuracy vs. Human Judgments
SoS score demonstrated robust performance compared to CLIPScore, especially for non-English languages and in capturing surface-level tendencies.
| Comparison Metric | SoS Score | CLIPScore |
|---|---|---|
| Overall Accuracy | 74.0% | 78.2% |
| Surface Tendency Precision | 94.8% | 86.8% |
| Semantic Tendency Precision | 84.8% | 95.4% |
| Non-English Language Performance |
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Layer-Wise Bias Amplification
An analysis of text encoder layers in models like SD21 and K3 revealed that negative SoS tendencies (surface form preference) become more pronounced in later layers. This suggests that insufficient exposure to particular languages in training may lead to models defaulting to surface-level cues over deeper semantic structures. For K3, European languages tend to shift toward neutrality from layer 20, while most Asian languages move toward a more negative SoS score.
Highlight: Later Text Encoder Layers Amplify Surface Tendencies
VQA analysis showed that languages with strong surface tendencies often trigger culturally stereotypical depictions. For example, FLUX generations in Chinese covered 61.9% of Chinese visual stereotypes, while SD depictions for Finnish prompts frequently showed snowy forest scenes (68% of images).
61.9%
Chinese Stereotype Coverage (FLUX)
68%
Finnish Snowy Forests (SD)
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