SEA-BED: How Do Embedding Models Represent Southeast Asian Languages? (2026.acl-long)
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Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Raymond Ng, Jann Railey Montalan, Thura Aung, Jian Gang Ngui, Yosephine Susanto, William Chandra Tjhi, Panuthep Tasawong, Erik Cambria, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | SEA-BED examines how multilingual text embeddings perform across tasks and languages . performance gaps arise from data coverage, training objectives, and architectural design, authors say . |
| Approach: | They propose a large-scale benchmark covering 10 SEA languages and diverse embedding tasks. |
| Outcome: | The proposed model performs poorly across languages and tasks, but language-task analyses reveal inconsistencies . the results suggest that performance gaps arise from limitations in data coverage, training objectives, and architectural design. |
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