Introduces the first benchmark for multicultural text-to-image generation across five countries and a MosAIG multi-agent framework, showing that richer prompts improve quality but disparities persist across languages and demographics.
In ECAI 2024, pages 930–937
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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UNVERDICTED 2representative citing papers
Diversity efforts in NLP concentrate on fairness areas because of systemic incentives and barriers that disenfranchise researchers in non-fairness subfields, as shown by demographic analysis with recommendations to broaden inclusion.
citing papers explorer
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When Cultures Meet: Multicultural Text-to-Image Generation
Introduces the first benchmark for multicultural text-to-image generation across five countries and a MosAIG multi-agent framework, showing that richer prompts improve quality but disparities persist across languages and demographics.
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NLP needs Diversity outside of 'Diversity'
Diversity efforts in NLP concentrate on fairness areas because of systemic incentives and barriers that disenfranchise researchers in non-fairness subfields, as shown by demographic analysis with recommendations to broaden inclusion.