Retrofitting Large Language Models with Dynamic Tokenization

Current language models (LMs) use a fixed, static subword tokenizer.This default choice typically results in degraded efficiency and language capabilities, especially in languages other than English.To address this issue, we challenge the static design and propose retrofitting LMs with dynamic tokenization: a way to dynamically decide on token boundaries based on the input text via a subwordmerging algorithm inspired by byte-pair encoding.We merge frequent subword sequences in a batch, then apply a pre-trained embeddingprediction hypernetwork to compute the token embeddings on-the-fly.For encoder-style models (e.g., XLM-R), this on average reduces token sequence lengths by >20% across 14 languages while degrading performance by less than 2%.The same method applied to prefilling and scoring in decoder-style models (e.g., Mistral-7B) results in minimal performance degradation at up to 17% reduction in sequence length.Overall, we find that dynamic tokenization can mitigate the limitations of static tokenization by substantially improving inference speed and promoting fairness across languages, enabling more equitable and adaptable LMs.* Now at Google.used to obtain embeddings -fixed-size vectors that serve as the model's representation of a token.