Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image Models
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Abstract (arXiv)
Text-To-Image (TTI) models, such as DALL-E and StableDiffusion, have demonstrated remarkable prompt-based image generation capabilities. Multilingual encoders may have a substantial impact on the cultural agency of these models, as language is a conduit of culture. In this study, we explore the cultural perception embedded in TTI models by characterizing culture across three hierarchical tiers: cultural dimensions, cultural domains, and cultural concepts. Based on this ontology, we derive prompt templates to unlock the cultural knowledge in TTI models, and propose a comprehensive suite of evaluation techniques, including intrinsic evaluations using the CLIP space, extrinsic evaluations with a Visual-Question-Answer (VQA) model and human assessments, to evaluate the cultural content of TTI-generated images. To bolster our research, we introduce the CulText2I dataset, derived from six diverse TTI models and spanning ten languages. Our experiments provide insights regarding Do, What, Which and How research questions about the nature of cultural encoding in TTI models, paving the way for cross-cultural applications of these models.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 40 of its 65 references that are in the map. First posted on arXiv 3 Oct 2023.
Builds on
40 of 65 references.
References · 40
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Citing papers per year: 2024: 10; 2025: 6.
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- 2025Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Models18 Feb 2025
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- 2024No Longer Trending on Artstation: Prompt Analysis of Generative AI Art24 Jan 2024
- 2024Vision-Language Models under Cultural and Inclusive Considerationscited by 8
- 2024NL-Eye: Abductive NLI for Imagescited by 5
- 2024NL-Eye: Abductive NLI for Images3 Oct 2024
- 2024Vision-Language Models under Cultural and Inclusive Considerations8 Jul 2024
- 2024Survey of Cultural Awareness in Language Models: Text and Beyondcited by 101
- 2024VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Modelscited by 42
- 2024An image speaks a thousand words, but can everyone listen? On image transcreation for cultural relevancecited by 36
- 2024Beyond Aesthetics: Cultural Competence in Text-to-Image Modelscited by 30
- 2024Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performancecited by 20
- 2025Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Modelscited by 14
- 2025CuRe: Cultural Gaps in the Long Tail of Text-to-Image Systemscited by 8
- 2024Uplifting Lower-Income Data: Strategies for Socioeconomic Perspective Shifts in Large Multi-modal Modelscited by 2
- 2024Locally Measuring Cross-lingual Lexical Alignment: A Domain and Word Level Perspectivecited by 1
- 2025Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activitiescited by 0
- 2025CuRe: Cultural Gaps in the Long Tail of Text-to-Image Systems9 Jun 2025
- 2025Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities8 Apr 2025
- 2025Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Models18 Feb 2025
- 2024VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models20 Nov 2024
- 2024Survey of Cultural Awareness in Language Models: Text and Beyond30 Oct 2024
- 2024Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance24 Oct 2024
- 2024Locally Measuring Cross-lingual Lexical Alignment: A Domain and Word Level Perspective7 Oct 2024
- 2024Beyond Aesthetics: Cultural Competence in Text-to-Image Models9 Jul 2024
- 2024Uplifting Lower-Income Data: Strategies for Socioeconomic Perspective Shifts in Large Multi-modal Models2 Jul 2024
- 2024An image speaks a thousand words, but can everyone listen? On image transcreation for cultural relevance1 Apr 2024