mlsanityData to 7 Oct 2026

Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image Models

arXiv 2310.01929 ↗First posted 3 Oct 2023cs.CL cs.AI cs.LGcited by 16

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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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28 more
  1. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
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  11. 2022Probing Pre-Trained Language Models for Cross-Cultural Differences in Valuescited by 147
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  16. 2022AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilitiescited by 84
  17. 2022StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuationcited by 74
  18. 2022Large-scale Text-to-Image Generation Models for Visual Artists' Creative Workscited by 60
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  20. 2023Knowledge of cultural moral norms in large language modelscited by 51
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  22. 2023The Hidden Language of Diffusion Modelscited by 34
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  24. 2022Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesiscited by 29
  25. 2022Cultural Incongruencies in Artificial Intelligencecited by 21
  26. 2023Imitation versus Innovation: What children can do that large language and language-and-vision models cannot (yet)?cited by 13
  27. 2023On the Cultural Gap in Text-to-Image Generationcited by 10
  28. 2023Multilingual Conceptual Coverage in Text-to-Image Modelscited by 9

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