mlsanityData to 14 Oct 2026

The Myth of Culturally Agnostic AI Models

arXiv 2211.15271 ↗First posted 28 Nov 2022cs.AIcited by 11

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

The paper discusses the potential of large vision-language models as objects of interest for empirical cultural studies. Focusing on the comparative analysis of outputs from two popular text-to-image synthesis models, DALL-E 2 and Stable Diffusion, the paper tries to tackle the pros and cons of striving towards culturally agnostic vs. culturally specific AI models. The paper discusses several examples of memorization and bias in generated outputs which showcase the trade-off between risk mitigation and cultural specificity, as well as the overall impossibility of developing culturally agnostic models.

Key facts. Cited by 11 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 5 of its 11 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

5 of 11 references.

References · 5

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  4. 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,555
  5. 2021Robust fine-tuning of zero-shot modelscited by 797

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 5; 2025: 5.

11 papers.

  1. 2023Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensionscited by 112
  2. 2024Survey of Cultural Awareness in Language Models: Text and Beyondcited by 103
  3. 2024Self-Pluralising Culture Alignment for Large Language Modelscited by 31
  4. 2024CIDAR: Culturally Relevant Instruction Dataset For Arabiccited by 17
  5. 2025Provocations from the Humanities for Generative AI Researchcited by 10
  6. 2025Training-Free Style and Content Transfer by Leveraging U-Net Skip Connections in Stable Diffusioncited by 6
  7. 2024I Am Not Them: Fluid Identities and Persistent Out-group Bias in Large Language Modelscited by 5
  8. 2024Reflections on Disentanglement and the Latent Spacecited by 2
  9. 2025Synthetic History: Evaluating Visual Representations of the Past in Diffusion Modelscited by 1
  10. 2025When Discourse Stalls: Moving Past Five Semantic Stopsigns about Generative AI in Design Researchcited by 0
  11. 2025The Persistence of Cultural Memory: Investigating Multimodal Iconicity in Diffusion Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Deconstructing Bias: A Multifaceted Framework for Diagnosing Cultural and Compositional Inequities in Text-to-Image Generative Models
  2. 2025CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries
  3. 2025Toward Socially Aware Vision-Language Models: Evaluating Cultural Competence Through Multimodal Story Generation
  4. 2025Diffusion Models Through a Global Lens: Are They Culturally Inclusive?
  5. 2025Evaluation of Cultural Competence of Vision-Language Models
  6. 2024See It from My Perspective: How Language Affects Cultural Bias in Image Understanding
  7. 2025Exposing Blindspots: Cultural Bias Evaluation in Generative Image Models
  8. 2025Uncovering Cultural Representation Disparities in Vision-Language Models