David Anugraha - ACL Anthology
This work bridges the representation gap in SEA, establishes a foundation for developing culturally aware AI systems for this region, and provides a replicable framework for addressing representation gaps in other underrepresented regions.
Jan Christian Blaise Cruz - ACL Anthology
We evaluated each method’s effectiveness in capturing cultural relevance. We found that image crawling achieves approximately ~85% cultural relevance while being more cost- and time-efficient than crowdsourcing, whereas synthetic image…
Frederikus Hudi - ACL Anthology
Despite Southeast Asia’s (SEA) extraordinary linguistic and cultural diversity, the region remains significantly underrepresented in vision-language (VL) research, resulting in AI models that inadequately capture SEA cultural nuances.
Peerat Limkonchotiwat - ACL Anthology
Pdf bib abs Crowdsource, Crawl, or Generate? Creating SEA- VL, a Multicultural Vision-Language Dataset for Southeast Asia Samuel Cahyawijaya
Alham Fikri Aji - ACL Anthology
Through extensive experiments with a 2-layer BERT student model, we show how this approach can achieve substantial improvements in benchmark accuracy (up to 75% on GPQA) without developing genuine reasoning capabilities.
Samuel Cahyawijaya - ACL Anthology
Pdf bib abs Crowdsource, Crawl, or Generate? Creating SEA- VL, a Multicultural Vision-Language Dataset for Southeast Asia Samuel Cahyawijaya
Holy Lovenia - ACL Anthology
In addition, we discuss the shortcomings of parameter-efficient tunings, such as LoRA, for language adaptation. Alternatively, we propose the usage of vocabulary adaptation to enhance efficiency.
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