Vision Language Models are Confused Tourists

• URL: http://arxiv.org/abs/2511.17004v1
• Date: Fri, 21 Nov 2025 07:14:46 GMT
• Title: Vision Language Models are Confused Tourists
• Authors: Patrick Amadeus Irawan, Ikhlasul Akmal Hanif, Muhammad Dehan Al Kautsar, Genta Indra Winata, Fajri Koto, Alham Fikri Aji,
• Abstract summary: We introduce ConfusedTourist, a novel cultural adversarial robustness suite designed to assess Vision-Language Models (VLMs)<n>Our experiments reveal a critical vulnerability, where accuracy drops heavily under simple image-stacking perturbations and even worsens with its image-generation-based variant.<n>These findings highlight a critical challenge: visual cultural concept mixing can substantially impair even state-of-the-art VLMs.
• Score: 31.85723694463742
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Although the cultural dimension has been one of the key aspects in evaluating Vision-Language Models (VLMs), their ability to remain stable across diverse cultural inputs remains largely untested, despite being crucial to support diversity and multicultural societies. Existing evaluations often rely on benchmarks featuring only a singular cultural concept per image, overlooking scenarios where multiple, potentially unrelated cultural cues coexist. To address this gap, we introduce ConfusedTourist, a novel cultural adversarial robustness suite designed to assess VLMs' stability against perturbed geographical cues. Our experiments reveal a critical vulnerability, where accuracy drops heavily under simple image-stacking perturbations and even worsens with its image-generation-based variant. Interpretability analyses further show that these failures stem from systematic attention shifts toward distracting cues, diverting the model from its intended focus. These findings highlight a critical challenge: visual cultural concept mixing can substantially impair even state-of-the-art VLMs, underscoring the urgent need for more culturally robust multimodal understanding.

Related papers

• CURE: Cultural Understanding and Reasoning Evaluation - A Framework for "Thick" Culture Alignment Evaluation in LLMs [24.598338950728234]
  Large language models (LLMs) are increasingly deployed in culturally diverse environments.<n>Existing methods focus on de-contextualized correctness or forced-choice judgments.<n>We introduce a set of benchmarks that present models with realistic situational contexts.
  arXiv  Detail & Related papers  (2025-11-15T03:39:13Z)
• BLEnD-Vis: Benchmarking Multimodal Cultural Understanding in Vision Language Models [54.16874020794336]
  We introduce BLEnD-Vis, a benchmark designed to evaluate the robustness of everyday cultural knowledge in vision-language models (VLMs)<n> BLEnD-Vis constructs 313 culturally grounded question templates spanning 16 regions and generates three aligned multiple-choice formats.<n>The resulting benchmark comprises 4,916 images and over 21,000 multiple-choice question (MCQ) instances, validated through human annotation.
  arXiv  Detail & Related papers  (2025-10-13T09:10:05Z)
• MMA-ASIA: A Multilingual and Multimodal Alignment Framework for Culturally-Grounded Evaluation [91.22008265721952]
  MMA-ASIA centers on a human-curated, multilingual, and multimodally aligned benchmark covering 8 Asian countries and 10 languages.<n>This is the first dataset aligned at the input level across three modalities: text, image (visual question answering), and speech.<n>We propose a five-dimensional evaluation protocol that measures: (i) cultural-awareness disparities across countries, (ii) cross-lingual consistency, (iii) cross-modal consistency, (iv) cultural knowledge generalization, and (v) grounding validity.
  arXiv  Detail & Related papers  (2025-10-07T14:12:12Z)
• Toward Socially Aware Vision-Language Models: Evaluating Cultural Competence Through Multimodal Story Generation [2.0467354053171243]
  We present the first comprehensive evaluation of Vision-Language Models (VLMs) cultural competence through multimodal story generation.<n>Our analysis reveals significant cultural adaptation capabilities, with rich culturally-specific vocabulary spanning names, familial terms, and geographic markers.<n>We uncover concerning limitations: cultural competence varies dramatically across architectures, some models exhibit inverse cultural alignment, and automated metrics show architectural bias contradicting human assessments.
  arXiv  Detail & Related papers  (2025-08-22T19:39:02Z)
• CAIRe: Cultural Attribution of Images by Retrieval-Augmented Evaluation [61.130639734982395]
  We introduce CAIRe, a novel evaluation metric that assesses the degree of cultural relevance of an image.<n>Our framework grounds entities and concepts in the image to a knowledge base and uses factual information to give independent graded judgments for each culture label.
  arXiv  Detail & Related papers  (2025-06-10T17:16:23Z)
• Evaluation of Cultural Competence of Vision-Language Models [25.953345583487312]
  Modern vision-language models (VLMs) often fail at cultural competency evaluations and benchmarks.<n>This paper argues that foundational methodologies from visual culture studies are necessary for cultural analysis of images.
  arXiv  Detail & Related papers  (2025-05-28T19:04:04Z)
• RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding [79.44246283490665]
  We introduce RAVENEA, a new benchmark designed to advance visual culture understanding through retrieval.<n>RAVENEA focuses on two tasks: culture-focused visual question answering (cVQA) and culture-informed image captioning (cIC)<n>We train and evaluate seven multimodal retrievers for each image query, and measure the downstream impact of retrieval-augmented inputs across fourteen state-of-the-art vision-language models.
  arXiv  Detail & Related papers  (2025-05-20T14:57:16Z)
• 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)
• CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries [63.00147630084146]
  Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding.<n>CultureVerse is a large-scale multimodal benchmark covering 19, 682 cultural concepts, 188 countries/regions, 15 cultural concepts, and 3 question types.<n>We propose CultureVLM, a series of VLMs fine-tuned on our dataset to achieve significant performance improvement in cultural understanding.
  arXiv  Detail & Related papers  (2025-01-02T14:42:37Z)
• 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)
• 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)

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.