Uncovering Cultural Representation Disparities in Vision-Language Models

• URL: http://arxiv.org/abs/2505.14729v3
• Date: Thu, 31 Jul 2025 06:24:39 GMT
• Title: Uncovering Cultural Representation Disparities in Vision-Language Models
• Authors: Ram Mohan Rao Kadiyala, Siddhant Gupta, Jebish Purbey, Srishti Yadav, Suman Debnath, Alejandro Salamanca, Desmond Elliott,
• Abstract summary: Vision-Language Models (VLMs) have demonstrated impressive capabilities across a range of tasks, yet concerns about their potential biases exist.<n>This work investigates the extent to which prominent VLMs exhibit cultural biases by evaluating their performance on an image-based country identification task at a country level.
• Score: 45.032609066023504
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vision-Language Models (VLMs) have demonstrated impressive capabilities across a range of tasks, yet concerns about their potential biases exist. This work investigates the extent to which prominent VLMs exhibit cultural biases by evaluating their performance on an image-based country identification task at a country level. Utilizing the geographically diverse Country211 dataset, we probe several large vision language models (VLMs) under various prompting strategies: open-ended questions, multiple-choice questions (MCQs) including challenging setups like multilingual and adversarial settings. Our analysis aims to uncover disparities in model accuracy across different countries and question formats, providing insights into how training data distribution and evaluation methodologies might influence cultural biases in VLMs. The findings highlight significant variations in performance, suggesting that while VLMs possess considerable visual understanding, they inherit biases from their pre-training data and scale that impact their ability to generalize uniformly across diverse global contexts.

Related papers

• An Empirical Study of Federated Prompt Learning for Vision Language Model [50.73746120012352]
  This paper systematically investigates behavioral differences between language prompt learning and vision prompt learning.<n>We conduct experiments to evaluate the impact of various fl and prompt configurations, such as client scale, aggregation strategies, and prompt length.<n>We explore strategies for enhancing prompt learning in complex scenarios where label skew and domain shift coexist.
  arXiv  Detail & Related papers  (2025-05-29T03:09:15Z)
• Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Models [26.051898880298126]
  Investigating value alignment in large language models based on cultural context has become a critical area of research.<n>Similar biases have not been extensively explored in large vision-language models (VLMs)
  arXiv  Detail & Related papers  (2025-02-18T19:03:02Z)
• Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation [71.59208664920452]
  Cultural biases in multilingual datasets pose significant challenges for their effectiveness as global benchmarks.<n>We show that progress on MMLU depends heavily on learning Western-centric concepts, with 28% of all questions requiring culturally sensitive knowledge.<n>We release Global MMLU, an improved MMLU with evaluation coverage across 42 languages.
  arXiv  Detail & Related papers  (2024-12-04T13:27:09Z)
• Vision-Language Models under Cultural and Inclusive Considerations [53.614528867159706]
  Large vision-language models (VLMs) can assist visually impaired people by describing images from their daily lives. Current evaluation datasets may not reflect diverse cultural user backgrounds or the situational context of this use case. We create a survey to determine caption preferences and propose a culture-centric evaluation benchmark by filtering VizWiz, an existing dataset with images taken by people who are blind. We then evaluate several VLMs, investigating their reliability as visual assistants in a culturally diverse setting.
  arXiv  Detail & Related papers  (2024-07-08T17:50:00Z)
• Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration [31.684544472009918]
  We propose a semi-automated framework for constructing cultural VLM benchmarks, specifically targeting multiple-choice QA.<n>This framework combines human-VLM collaboration, where VLMs generate questions based on guidelines, a small set of annotated examples, and relevant knowledge, followed by a verification process by native speakers.<n>We demonstrate the effectiveness of this framework through the creation of K-Viscuit, a dataset focused on Korean culture.
  arXiv  Detail & Related papers  (2024-06-24T09:18:15Z)
• See It from My Perspective: How Language Affects Cultural Bias in Image Understanding [60.70852566256668]
  Vision-language models (VLMs) can respond to queries about images in many languages.<n>We characterize the Western bias of VLMs in image understanding and investigate the role that language plays in this disparity.
  arXiv  Detail & Related papers  (2024-06-17T15:49:51Z)
• CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark [68.21939124278065]
  Culturally-diverse multilingual Visual Question Answering benchmark designed to cover a rich set of languages and cultures. CVQA includes culturally-driven images and questions from across 30 countries on four continents, covering 31 languages with 13 scripts, providing a total of 10k questions. We benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and show that the dataset is challenging for the current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-06-10T01:59:00Z)
• No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models [38.932610459192105]
  We study cultural and socioeconomic diversity in contrastive vision-language models (VLMs) Our work underscores the value of using diverse data to create more inclusive multimodal systems.
  arXiv  Detail & Related papers  (2024-05-22T16:04:22Z)
• A Unified Framework and Dataset for Assessing Societal Bias in Vision-Language Models [9.025958469582363]
  We propose a unified framework for evaluating gender, race, and age biases in vision-language models (VLMs) We generate high-quality synthetic datasets that intentionally conceal gender, race, and age information across different professional domains. The dataset includes action-based descriptions of each profession and serves as a benchmark for evaluating societal biases in vision-language models (VLMs)
  arXiv  Detail & Related papers  (2024-02-21T09:17:51Z)
• Lost in Translation: When GPT-4V(ision) Can't See Eye to Eye with Text. A Vision-Language-Consistency Analysis of VLLMs and Beyond [7.760124498553333]
  We study whether vision-language models execute vision and language tasks consistently or independently. We introduce a systematic framework that quantifies the capability disparities between different modalities in the multi-modal setting. We introduce "Vision Description Prompting," a method that effectively improves performance in challenging vision-related tasks.
  arXiv  Detail & Related papers  (2023-10-19T06:45:11Z)
• Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models [57.08925810659545]
  We conduct a comparative analysis of the visual representations in existing vision-and-language models and vision-only models. Our empirical observations suggest that vision-and-language models are better at label prediction tasks. We hope our study sheds light on the role of language in visual learning, and serves as an empirical guide for various pretrained models.
  arXiv  Detail & Related papers  (2022-12-01T05:00:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.