| Challenge: | Vision-language models struggle on culturally situated inputs, study shows . despite impressive performance, many VLMs struggle on such culturally grounded inputs . |
| Approach: | They propose a new margin-based selector to identify neurons associated with cultural selectivity . they also introduce a model-dependent decoder to identify such neurons . |
| Outcome: | The proposed model outperforms probability- and entropy-based methods in identifying neurons associated with cultural selectivity. |
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| Challenge: | Using large vision-language models to understand cultural contexts is a critical area of research. |
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Bryan Chen Zhengyu Tan, Weihua Zheng, Zhengyuan Liu, Nancy F. Chen, Hwaran Lee, Kenny Tsu Wei Choo, Roy Ka-Wei Lee
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CROPE: Evaluating In-Context Adaptation of Vision and Language Models to Culture-Specific Concepts (2025.naacl-long)
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| Challenge: | Recent Vision and Language models have shown impressive performance across benchmarks . however, frontier models lack cultural awareness and can affect global cultural diversity . |
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Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration (2025.acl-long)
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| Challenge: | Existing approaches to creating inclusive vision-language models rely on human annotators, making it labor-intensive and creating cognitive burdens. |
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