Academic

ValueGround: Evaluating Culture-Conditioned Visual Value Grounding in MLLMs

arXiv:2604.06484v1 Announce Type: new Abstract: Cultural values are expressed not only through language but also through visual scenes and everyday social practices. Yet existing evaluations of cultural values in language models are almost entirely text-only, making it unclear whether models can ground culture-conditioned judgments when response options are visualized. We introduce ValueGround, a benchmark for evaluating culture-conditioned visual value grounding in multimodal large language models (MLLMs). Built from World Values Survey (WVS) questions, ValueGround uses minimally contrastive image pairs to represent opposing response options while controlling irrelevant variation. Given a country, a question, and an image pair, a model must choose the image that best matches the country's value tendency without access to the original response-option texts. Across six MLLMs and 13 countries, average accuracy drops from 72.8% in the text-only setting to 65.8% when options are visualize

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Zhipin Wang, Christoph Leiter, Christian Frey, Mohamed Hesham Ibrahim Abdalla, Josif Grabocka, Steffen Eger · · 1 min read · 10 views
#cs.CL

arXiv:2604.06484v1 Announce Type: new Abstract: Cultural values are expressed not only through language but also through visual scenes and everyday social practices. Yet existing evaluations of cultural values in language models are almost entirely text-only, making it unclear whether models can ground culture-conditioned judgments when response options are visualized. We introduce ValueGround, a benchmark for evaluating culture-conditioned visual value grounding in multimodal large language models (MLLMs). Built from World Values Survey (WVS) questions, ValueGround uses minimally contrastive image pairs to represent opposing response options while controlling irrelevant variation. Given a country, a question, and an image pair, a model must choose the image that best matches the country's value tendency without access to the original response-option texts. Across six MLLMs and 13 countries, average accuracy drops from 72.8% in the text-only setting to 65.8% when options are visualized, despite 92.8% accuracy on option-image alignment. Stronger models are more robust, but all remain prone to prediction reversals. Our benchmark provides a controlled testbed for studying cross-modal transfer of culture-conditioned value judgments.

Executive Summary

The article introduces ValueGround, a novel benchmark evaluating multimodal large language models' (MLLMs) ability to ground culture-conditioned value judgments in visual contexts. Unlike prior text-only assessments, ValueGround leverages World Values Survey questions and minimally contrastive image pairs to represent opposing cultural tendencies across 13 countries. The study reveals a significant performance drop when MLLMs transition from text-only to visually grounded evaluations, with average accuracy falling from 72.8% to 65.8%, despite high image-option alignment. While stronger MLLMs exhibit greater robustness, all models demonstrate susceptibility to prediction reversals. This benchmark offers a controlled environment for investigating cross-modal transfer of cultural values, highlighting a critical area for MLLM development.

Key Points

  • ▸ ValueGround is a new benchmark for assessing MLLMs' culture-conditioned visual value grounding.
  • ▸ It uses World Values Survey questions and minimally contrastive image pairs to represent cultural value tendencies.
  • ▸ MLLM accuracy significantly drops from 72.8% (text-only) to 65.8% (visual grounding), despite high image-option alignment.
  • ▸ Stronger MLLMs show better robustness, but all tested models are prone to prediction reversals.
  • ▸ The benchmark facilitates controlled study of cross-modal transfer of culture-conditioned value judgments.

Merits

Novelty of Approach

The introduction of a visual grounding benchmark for cultural values addresses a critical gap in existing MLLM evaluations, moving beyond text-centric assessments.

Methodological Rigor

The use of minimally contrastive image pairs, controlling for irrelevant variation, enhances the precision and validity of the evaluation by isolating the cultural value dimension.

Real-World Relevance (WVS)

Building upon the World Values Survey lends significant sociological and cross-cultural validity to the questions and underlying value dimensions being tested.

Clear Performance Insights

The quantitative data clearly demonstrates a performance degradation in visual grounding, providing actionable insights into MLLM limitations in this domain.

Demerits

Scope of Countries and Models

While 13 countries and six MLLMs provide a starting point, expanding this diversity would offer a more comprehensive understanding of generalizability and model-specific nuances.

Definition of 'Minimally Contrastive'

The article's abstract doesn't fully elaborate on the precise methodology for ensuring 'minimally contrastive' images, which is crucial for controlling confounding variables in visual stimuli.

Explanation of 'Prediction Reversals'

Further detail on the nature and frequency of 'prediction reversals' would be beneficial, as this phenomenon could reveal deeper issues in MLLM reasoning or cultural representation.

Causal Mechanisms Unexplored

The study identifies a performance gap but does not delve deeply into the underlying causal mechanisms for the MLLMs' difficulties in visually grounding cultural values.

Expert Commentary

This work represents a critical advancement in the rigorous evaluation of MLLMs, moving beyond mere linguistic competence to tackle the far more complex domain of cultural value grounding in visual contexts. The observed performance drop from text-only to visually grounded tasks, despite high image-option alignment, is profoundly telling. It suggests that MLLMs, even 'stronger' ones, are not merely translating text knowledge into visual recognition but are struggling with the deeper cognitive leap required to infer abstract cultural values from visual cues. This isn't just about identifying objects; it's about understanding the implicit social practices and underlying value systems represented visually. The 'prediction reversals' are particularly concerning, indicating a potential fragility or superficiality in their cultural reasoning. Future research must dissect these failures, perhaps through explainable AI techniques, to understand if models are misinterpreting visual semantics, lacking appropriate cultural priors, or failing to integrate cross-modal information effectively. This benchmark serves as an indispensable diagnostic tool for developing truly culturally intelligent MLLMs.

Recommendations

  • ✓ Conduct qualitative error analysis on 'prediction reversals' to discern underlying causes (e.g., visual feature misinterpretation, lack of cultural context, flawed cross-modal integration).
  • ✓ Expand ValueGround to include a wider array of countries, cultural dimensions (beyond WVS), and visual modalities (e.g., video, mixed media) to enhance generalizability and robustness.
  • ✓ Investigate methods for injecting explicit cultural knowledge graphs or symbolic representations into MLLMs to aid in visual value grounding.
  • ✓ Explore the use of human-in-the-loop validation for image pair generation and cultural value assessment to refine the benchmark's objectivity and cultural sensitivity.

Sources

Original: arXiv - cs.CL

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