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.
Fast Vocabulary Transfer for Language Model Compression
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Interpretability-based selection of vocabulary items plus FragMend initialization reduces token over-fragmentation and improves performance for non-Latin script languages by roughly 20 points over baselines.
BPE tokenization creates gibberish bias in CLLMs, causing secrets with high character entropy but low token entropy to be preferentially memorized due to training data distribution shifts.
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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Defragmenting Language Models: An Interpretability-based Approach for Vocabulary Expansion
Interpretability-based selection of vocabulary items plus FragMend initialization reduces token over-fragmentation and improves performance for non-Latin script languages by roughly 20 points over baselines.
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Understanding Secret Leakage Risks in Code LLMs: A Tokenization Perspective
BPE tokenization creates gibberish bias in CLLMs, causing secrets with high character entropy but low token entropy to be preferentially memorized due to training data distribution shifts.