CrossCult-KIBench is a new benchmark for evaluating cross-cultural knowledge insertion in MLLMs, paired with the MCKI baseline method, showing current approaches fail to balance adaptation and preservation.
Cvqa: Culturally-diverse multilingual visual question answering benchmark
4 Pith papers cite this work. Polarity classification is still indexing.
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ProMQA-Assembly is a new multimodal procedural QA dataset with 646 pairs on assembly activities, built via LLM-generated candidates verified by humans plus 81 task graphs, and used to benchmark multimodal models.
EduArt is a new benchmark of 871 educational questions that reveals multimodal LLMs perform near ceiling on multiple-choice art history items but drop sharply on open completion and error identification tasks.
Case studies with blind UK residents and people from Kerala and Tamil Nadu demonstrate that community input at the systematization stage produces culturally grounded definitions of appropriateness for text-to-image model outputs.
citing papers explorer
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CrossCult-KIBench: A Benchmark for Cross-Cultural Knowledge Insertion in MLLMs
CrossCult-KIBench is a new benchmark for evaluating cross-cultural knowledge insertion in MLLMs, paired with the MCKI baseline method, showing current approaches fail to balance adaptation and preservation.
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ProMQA-Assembly: Multimodal Procedural QA Dataset on Assembly
ProMQA-Assembly is a new multimodal procedural QA dataset with 646 pairs on assembly activities, built via LLM-generated candidates verified by humans plus 81 task graphs, and used to benchmark multimodal models.
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EduArt: An educational-level benchmark for evaluating art history knowledge in large language models
EduArt is a new benchmark of 871 educational questions that reveals multimodal LLMs perform near ceiling on multiple-choice art history items but drop sharply on open completion and error identification tasks.
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Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics
Case studies with blind UK residents and people from Kerala and Tamil Nadu demonstrate that community input at the systematization stage produces culturally grounded definitions of appropriateness for text-to-image model outputs.