Papers with low-resource LM training
Efficient Low-Resource Language Models Using Tokenizer Transfer (2026.eacl-srw)
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| Challenge: | Tokenizer transfer allows training a model for low-resource languages without full retraining . a study of pre-trained tokenizers shows that they are more efficient than traditional training methods. |
| Approach: | They evaluate tokenizer transfer on models trained on language-specific corpora, Orthogonal Mapping Pursuit and Fast Vocabulary Transfer. |
| Outcome: | The proposed model adapts to a pre-trained model without full retraining and improves cross-lingual applicability. |