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SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages

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arxiv 2406.10118 v5 pith:KQVP2LR6 submitted 2024-06-14 cs.CL

Holy Lovenia , Rahmad Mahendra , Salsabil Maulana Akbar , Lester James V. Miranda , Jennifer Santoso , Elyanah Aco , Akhdan Fadhilah , Jonibek Mansurov
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Joseph Marvin Imperial Onno P. Kampman Joel Ruben Antony Moniz Muhammad Ravi Shulthan Habibi Frederikus Hudi Railey Montalan Ryan Ignatius Joanito Agili Lopo William Nixon Börje F. Karlsson James Jaya Ryandito Diandaru Yuze Gao Patrick Amadeus Bin Wang Jan Christian Blaise Cruz Chenxi Whitehouse Ivan Halim Parmonangan Maria Khelli Wenyu Zhang Lucky Susanto Reynard Adha Ryanda Sonny Lazuardi Hermawan Dan John Velasco Muhammad Dehan Al Kautsar Willy Fitra Hendria Yasmin Moslem Noah Flynn Muhammad Farid Adilazuarda Haochen Li Johanes Lee R. Damanhuri Shuo Sun Muhammad Reza Qorib Amirbek Djanibekov Wei Qi Leong Quyet V. Do Niklas Muennighoff Tanrada Pansuwan Ilham Firdausi Putra Yan Xu Ngee Chia Tai Ayu Purwarianti Sebastian Ruder William Tjhi Peerat Limkonchotiwat Alham Fikri Aji Sedrick Keh Genta Indra Winata Ruochen Zhang Fajri Koto Zheng-Xin Yong Samuel Cahyawijaya
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classification cs.CL
keywords languagesmodelsresourceseacrowdacrossculturaldatadatasets
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Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA, compromising the quality of AI models for SEA languages. Evaluating models for SEA languages is challenging due to the scarcity of high-quality datasets, compounded by the dominance of English training data, raising concerns about potential cultural misrepresentation. To address these challenges, we introduce SEACrowd, a collaborative initiative that consolidates a comprehensive resource hub that fills the resource gap by providing standardized corpora in nearly 1,000 SEA languages across three modalities. Through our SEACrowd benchmarks, we assess the quality of AI models on 36 indigenous languages across 13 tasks, offering valuable insights into the current AI landscape in SEA. Furthermore, we propose strategies to facilitate greater AI advancements, maximizing potential utility and resource equity for the future of AI in SEA.

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  1. SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia

    cs.CL 2025-02 conditional novelty 6.0 of 10

    SeaExam and SeaBench are locally sourced benchmarks for Indonesian, Thai, and Vietnamese that show larger cross-model performance differences than translated English benchmarks, though the evidence for 'better discrim...

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