SERENGETI: Massively Multilingual Language Models for Africa (2023.findings-acl)
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| Challenge: | Pretrained models acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. |
| Approach: | They develop a set of massively multilingual language models that covers 517 African languages and language varieties. |
| Outcome: | The proposed models outperform 4 models that cover 4-23 African languages on eight natural language understanding tasks, achieving 82.27 average F_1. |
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