TokAlign++ learns token alignments between LLM vocabularies from monolingual representations to enable faster adaptation, better text compression, and effective token-level distillation across 15 languages with minimal steps.
UNK s everywhere: A dapting multilingual language models to new scripts
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First TrOCR adaptation for Tigrinya achieves 0.22% CER and 97.2% exact match using tokenizer extension plus Word-Aware Loss Weighting on 5000 synthetic GLOCR images.
Four MAFT-based PLMs for Angolan languages report 12.3-point gains over AfroXLMR-base and 3.8-point gains over OFA baselines on downstream tasks.
citing papers explorer
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TokAlign++: Advancing Vocabulary Adaptation via Better Token Alignment
TokAlign++ learns token alignments between LLM vocabularies from monolingual representations to enable faster adaptation, better text compression, and effective token-level distillation across 15 languages with minimal steps.
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Adapting TrOCR for Printed Tigrinya Text Recognition: Word-Aware Loss Weighting for Cross-Script Transfer Learning
First TrOCR adaptation for Tigrinya achieves 0.22% CER and 97.2% exact match using tokenizer extension plus Word-Aware Loss Weighting on 5000 synthetic GLOCR images.
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ANGOFA: Leveraging OFA Embedding Initialization and Synthetic Data for Angolan Language Model
Four MAFT-based PLMs for Angolan languages report 12.3-point gains over AfroXLMR-base and 3.8-point gains over OFA baselines on downstream tasks.