BPE Gets Picky: Efficient Vocabulary Refinement During Tokenizer Training
Pavel Chizhov, Catherine Arnett, Elizaveta Korotkova, Ivan P. Yamshchikov
Abstract
Language models can greatly benefit from efficient tokenization. However, they still mostly utilize the classical Byte-Pair Encoding (BPE) algorithm, a simple and reliable method. BPE has been shown to cause such issues as under-trained tokens and sub-optimal compression that may affect the downstream performance. We introduce PickyBPE, a modified BPE algorithm that carries out vocabulary refinement during tokenizer training by removing merges that leave intermediate “junk” tokens. Our method improves vocabulary efficiency, eliminates under-trained tokens, and does not compromise text compression. Our experiments show that this method either improves downstream performance or does not harm it.
BibTeX
@inproceedings{chizhov-etal-2024-bpe,
title = "{BPE} Gets Picky: Efficient Vocabulary Refinement During Tokenizer Training",
author = "Chizhov, Pavel and
Arnett, Catherine and
Korotkova, Elizaveta and
Yamshchikov, Ivan P.",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.925/",
doi = "10.18653/v1/2024.emnlp-main.925",
pages = "16587--16604"
}