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Annotation reporting in NLP is improving, but critical details that affect validity are still frequently overlooked, especially in model evaluations.
LLMs can now be automatically audited for cultural insensitivity with 80% accuracy, thanks to a new metric that pinpoints and explains cultural errors in generated text.
MLLMs struggle to ground cultural values in visual scenes, losing ~7% accuracy compared to text-only inputs, even when they understand the visual content.
