Vision-language models exhibit literal superiority bias on noun compounds, with photorealistic visuals linked to poorer idiomatic grounding via new DIVA benchmark and Δ metric.
InProceedings of the 2nd Workshop on Uncertainty-Aware NLP (UncertaiNLP 2025), pages 89–102, Suzhou, China
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More Than Meets the Eye: Measuring the Semiotic Gap in Vision-Language Models via Semantic Anchorage
Vision-language models exhibit literal superiority bias on noun compounds, with photorealistic visuals linked to poorer idiomatic grounding via new DIVA benchmark and Δ metric.