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.
Extending Multilingual BERT to Low-Resource Languages
3 Pith papers cite this work, alongside 86 external citations. Polarity classification is still indexing.
3
Pith papers citing it
86
external citations · OpenAlex
fields
cs.CL 3years
2026 3representative citing papers
Low-resource languages are structurally more different from English in LLMs than high- or mid-resource ones, and language-specific post-training alters structures while preserving inter-language relationships.
citing papers explorer
-
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.
-
Multilinguality of Large Language Models From a Structural Perspective
Low-resource languages are structurally more different from English in LLMs than high- or mid-resource ones, and language-specific post-training alters structures while preserving inter-language relationships.
- Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages