Teaching Old Tokenizers New Words: Efficient Tokenizer Adaptation for Pre-trained Models

This article likely discusses methods to update or expand the vocabulary of existing tokenizers used in pre-trained language models (LLMs). The focus is on efficiency, suggesting the authors are addressing computational or resource constraints associated with this process. The title implies a focus on practical improvements to existing systems rather than entirely novel tokenizer architectures.