mlsanityData to 14 Oct 2026

Cultural Awareness in Vision-Language Models: A Cross-Country Exploration

arXiv 2505.20326 ↗First posted 23 May 2025cs.CY cs.AI cs.CLcited by 2

On the map: Multimodal LLMs / vision-language models → Social bias and fairness in VLMs

Abstract (arXiv)

Vision-Language Models (VLMs) are increasingly deployed in diverse cultural contexts, yet their internal biases remain poorly understood. In this work, we propose a novel framework to systematically evaluate how VLMs encode cultural differences and biases related to race, gender, and physical traits across countries. We introduce three retrieval-based tasks: (1) Race to Country retrieval, which examines the association between individuals from specific racial groups (East Asian, White, Middle Eastern, Latino, South Asian, and Black) and different countries; (2) Personal Traits to Country retrieval, where images are paired with trait-based prompts (e.g., Smart, Honest, Criminal, Violent) to investigate potential stereotypical associations; and (3) Physical Characteristics to Country retrieval, focusing on visual attributes like skinny, young, obese, and old to explore how physical appearances are culturally linked to nations. Our findings reveal persistent biases in VLMs, highlighting how visual representations may inadvertently reinforce societal stereotypes.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Social bias and fairness in VLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 20 of its 23 references that are in the map. First posted on arXiv 23 May 2025.

Builds on

20 of 23 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples30 Nov
  2. 2023Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models9 Nov
  3. 2023MultiModal Bias: Introducing a Framework for Stereotypical Bias Assessment beyond Gender and Race in Vision Language Models16 Mar
  4. 2023Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities26 Jan

Other references · 16

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  3. 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,109
  4. 2022Reproducible scaling laws for contrastive language-image learningcited by 1,173
  5. 2021FLAVA: A Foundational Language And Vision Alignment Modelcited by 649
  6. 2021Multimodal datasets: misogyny, pornography, and malignant stereotypescited by 327
  7. 2023Stable Bias: Analyzing Societal Representations in Diffusion Modelscited by 226
  8. 2023Improving CLIP Training with Language Rewritescited by 217
  9. 2021Visually Grounded Reasoning across Languages and Culturescited by 189
  10. 2017No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing Worldcited by 153
  11. 2024Benchmarking Vision Language Models for Cultural Understandingcited by 101
  12. 2023ALIP: Adaptive Language-Image Pre-training with Synthetic Captioncited by 70
4 more
  1. 2023Uncurated Image-Text Datasets: Shedding Light on Demographic Biascited by 52
  2. 2024See It from My Perspective: How Language Affects Cultural Bias in Image Understandingcited by 17
  3. 2024No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Modelscited by 16
  4. 2024From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Modelscited by 14

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Finding Culture-Sensitive Neurons in Vision-Language Modelscited by 3
  2. 2025Labels or Input? Rethinking Augmentation in Multimodal Hate Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Cultural Counterfactuals: Evaluating Cultural Biases in Large Vision-Language Models with Counterfactual Examples
  2. 2025Visual Cues of Gender and Race are Associated with Stereotyping in Vision-Language Models
  3. 2025Breaking Language Barriers or Reinforcing Bias? A Study of Gender and Racial Disparities in Multilingual Contrastive Vision Language Models
  4. 2025Uncovering Cultural Representation Disparities in Vision-Language Models
  5. 2024A Unified Framework and Dataset for Assessing Societal Bias in Vision-Language Models
  6. 2025VisBias: Measuring Explicit and Implicit Social Biases in Vision Language Models
  7. 2025Contrasting Cognitive Styles in Vision-Language Models: Holistic Attention in Japanese Versus Analytical Focus in English
  8. 2025Hidden Bias in the Machine: Stereotypes in Text-to-Image Models