arXiv subjects

Haorui Yu

Publications and source records attributed to Haorui Yu.

4 recordsLinked to original sources

arXiv2026

Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models

Vision-Language Models (VLMs) excel at visual description yet remain under-validated for cultural interpretation. Existing benchmarks assess perception without interpretation, and common evaluation proxies, such as automated metrics and LLM-judge averaging, are unreliable for culturally sensitive generative tasks. We address this measurement gap with a tri-tier evaluation framework grounded in art-theoretical constructs (Section 2). The framework operationalises cultural understanding through five levels (L1--L5) and 165 culture-specific dimensions across six traditions: Tier I computes automated quality indicators, Tier II applies rubric-based single-judge scoring, and Tier III calibrates the aggregate score to human expert ratings via sigmoid calibration. Applied to 15 VLMs across 294 evaluation pairs, the validated instrument reveals that (i) automated metrics and judge scoring measure different constructs, establishing single-judge calibration as the more reliable alternative; (ii) cultural understanding degrades from visual description (L1--L2) to cultural interpretation (L3--L5); and (iii) Western art samples consistently receive higher scores than non-Western ones. To our knowledge, this is the first cross-cultural evaluation instrument for generative art critique, providing a reproducible methodology for auditing VLM cultural competence. Framework code is available at https://github.com/yha9806/VULCA-Framework.

cs.CL↗

arXiv2026

VULCA-Bench: A Multicultural Vision-Language Benchmark for Evaluating Cultural Understanding

We introduce VULCA-Bench, a multicultural art-critique benchmark for evaluating Vision-Language Models' (VLMs) cultural understanding beyond surface-level visual perception. Existing VLM benchmarks predominantly measure L1-L2 capabilities (object recognition, scene description, and factual question answering) while under-evaluate higher-order cultural interpretation. VULCA-Bench contains 7,410 matched image-critique pairs spanning eight cultural traditions, with Chinese-English bilingual coverage. We operationalise cultural understanding using a five-layer framework (L1-L5, from Visual Perception to Philosophical Aesthetics), instantiated as 225 culture-specific dimensions and supported by expert-written bilingual critiques. Our pilot results indicate that higher-layer reasoning (L3-L5) is consistently more challenging than visual and technical analysis (L1-L2). The dataset, evaluation scripts, and annotation tools are available under CC BY 4.0 at https://github.com/yha9806/VULCA-Bench.

cs.CL↗

arXiv2025

A Structured Framework for Evaluating and Enhancing Interpretive Capabilities of Multimodal LLMs in Culturally Situated Tasks

This study aims to test and evaluate the capabilities and characteristics of current mainstream Visual Language Models (VLMs) in generating critiques for traditional Chinese painting. To achieve this, we first developed a quantitative framework for Chinese painting critique. This framework was constructed by extracting multi-dimensional evaluative features covering evaluative stance, feature focus, and commentary quality from human expert critiques using a zero-shot classification model. Based on these features, several representative critic personas were defined and quantified. This framework was then employed to evaluate selected VLMs such as Llama, Qwen, or Gemini. The experimental design involved persona-guided prompting to assess the VLM's ability to generate critiques from diverse perspectives. Our findings reveal the current performance levels, strengths, and areas for improvement of VLMs in the domain of art critique, offering insights into their potential and limitations in complex semantic understanding and content generation tasks. The code used for our experiments can be publicly accessed at: https://github.com/yha9806/VULCA-EMNLP2025.

cs.CL↗

arXiv2025

Seeing Symbols, Missing Cultures: Probing Vision-Language Models' Reasoning on Fire Imagery and Cultural Meaning

Vision-Language Models (VLMs) often appear culturally competent but rely on superficial pattern matching rather than genuine cultural understanding. We introduce a diagnostic framework to probe VLM reasoning on fire-themed cultural imagery through both classification and explanation analysis. Testing multiple models on Western festivals, non-Western traditions, and emergency scenes reveals systematic biases: models correctly identify prominent Western festivals but struggle with underrepresented cultural events, frequently offering vague labels or dangerously misclassifying emergencies as celebrations. These failures expose the risks of symbolic shortcuts and highlight the need for cultural evaluation beyond accuracy metrics to ensure interpretable and fair multimodal systems.

cs.CV↗