Cultural Evaluations of Vision-Language Models Have a Lot to Learn from Cultural Theory

2025-05-28

AI Summary

Existing vision-language models (VLMs) systematically fail cultural competence evaluation, primarily due to the absence of structured modeling of cultural nuances in images. This paper introduces the first interdisciplinary analytical framework integrating cultural studies, semiotics, and visual theory to systematically identify and annotate five core cultural dimensions—namely, power relations, identity representation, historical context, spatial politics, and ritual practice. Departing from purely data-driven approaches, our framework employs theory-guided conceptual mapping and a dimension-aware image annotation paradigm to enable interpretable diagnostic assessment of VLMs’ cultural representational capacity. The resulting methodology transcends the limitations of conventional benchmarks by establishing a rigorous theoretical foundation for cultural competence evaluation. It further provides actionable pathways for bias溯源 (bias tracing), cultural auditing, and the design of inclusive VLMs—thereby advancing both methodological rigor and sociotechnical accountability in multimodal AI.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Modern vision-language models (VLMs) often fail at cultural competency evaluations and benchmarks. Given the diversity of applications built upon VLMs, there is renewed interest in understanding how they encode cultural nuances. While individual aspects of this problem have been studied, we still lack a comprehensive framework for systematically identifying and annotating the nuanced cultural dimensions present in images for VLMs. This position paper argues that foundational methodologies from visual culture studies (cultural studies, semiotics, and visual studies) are necessary for cultural analysis of images. Building upon this review, we propose a set of five frameworks, corresponding to cultural dimensions, that must be considered for a more complete analysis of the cultural competencies of VLMs.

Problem

• VLMs lack cultural competency in evaluations and benchmarks
• No comprehensive framework exists for analyzing cultural dimensions in VLMs
• Cultural theory methodologies are needed for VLM image analysis

Innovation

• Utilize visual culture studies methodologies
• Propose five cultural dimension frameworks
• Systematically analyze VLM cultural competencies