# The Myth of Culturally Agnostic AI Models

arXiv: https://arxiv.org/abs/2211.15271 · HTML: https://mlsanity.com/paper/2211.15271/

## 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.

## 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, 5 of 5)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [Robust fine-tuning of zero-shot models](https://mlsanity.com/paper/2109.01903/) (2021)

## Built on by (most cited first, 11 of 11)

- [Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions](https://mlsanity.com/paper/2309.12342/) (2023)
- [Survey of Cultural Awareness in Language Models: Text and Beyond](https://mlsanity.com/paper/2411.00860/) (2024)
- [Self-Pluralising Culture Alignment for Large Language Models](https://mlsanity.com/paper/2410.12971/) (2024)
- [CIDAR: Culturally Relevant Instruction Dataset For Arabic](https://mlsanity.com/paper/2402.03177/) (2024)
- [Provocations from the Humanities for Generative AI Research](https://mlsanity.com/paper/2502.19190/) (2025)
- [Training-Free Style and Content Transfer by Leveraging U-Net Skip Connections in Stable Diffusion](https://mlsanity.com/paper/2501.14524/) (2025)
- [I Am Not Them: Fluid Identities and Persistent Out-group Bias in Large Language Models](https://mlsanity.com/paper/2402.10436/) (2024)
- [Reflections on Disentanglement and the Latent Space](https://mlsanity.com/paper/2410.09094/) (2024)
- [Synthetic History: Evaluating Visual Representations of the Past in Diffusion Models](https://mlsanity.com/paper/2505.17064/) (2025)
- [When Discourse Stalls: Moving Past Five Semantic Stopsigns about Generative AI in Design Research](https://mlsanity.com/paper/2503.08565/) (2025)
- [The Persistence of Cultural Memory: Investigating Multimodal Iconicity in Diffusion Models](https://mlsanity.com/paper/2511.11435/) (2025)

## 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.