@inproceedings{ali-etal-2024-tokenizer,
title = "Tokenizer Choice For {LLM} Training: Negligible or Crucial?",
author = {Ali, Mehdi and
Fromm, Michael and
Thellmann, Klaudia and
Rutmann, Richard and
L{\"u}bbering, Max and
Leveling, Johannes and
Klug, Katrin and
Ebert, Jan and
Doll, Niclas and
Buschhoff, Jasper and
Jain, Charvi and
Weber, Alexander and
Jurkschat, Lena and
Abdelwahab, Hammam and
John, Chelsea and
Ortiz Suarez, Pedro and
Ostendorff, Malte and
Weinbach, Samuel and
Sifa, Rafet and
Kesselheim, Stefan and
Flores-Herr, Nicolas},
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.247",
pages = "3907--3924",
abstract = "The recent success of large language models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot.Shedding light on this underexplored area, we conduct a comprehensive study on the influence of tokenizer choice on LLM downstream performance by training 24 mono- and multilingual LLMs at a 2.6B parameter scale, ablating different tokenizer algorithms and parameterizations. Our studies highlight that the tokenizer choice can significantly impact the model{'}s downstream performance and training costs. In particular, we find that the common tokenizer evaluation metrics fertility and parity are not always predictive of model downstream performance, rendering these metrics a questionable proxy for the model{'}s downstream performance. Furthermore, we show that multilingual tokenizers trained on the five most frequent European languages require vocabulary size increases of factor three in comparison to English. While English-centric tokenizers have been applied to the training of multi-lingual LLMs in the past, we find that this approach results in a severe downstream performance degradation and additional training costs of up to 68{\%}, due to an inefficient tokenization vocabulary.",
}
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<abstract>The recent success of large language models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot.Shedding light on this underexplored area, we conduct a comprehensive study on the influence of tokenizer choice on LLM downstream performance by training 24 mono- and multilingual LLMs at a 2.6B parameter scale, ablating different tokenizer algorithms and parameterizations. Our studies highlight that the tokenizer choice can significantly impact the model’s downstream performance and training costs. In particular, we find that the common tokenizer evaluation metrics fertility and parity are not always predictive of model downstream performance, rendering these metrics a questionable proxy for the model’s downstream performance. Furthermore, we show that multilingual tokenizers trained on the five most frequent European languages require vocabulary size increases of factor three in comparison to English. While English-centric tokenizers have been applied to the training of multi-lingual LLMs in the past, we find that this approach results in a severe downstream performance degradation and additional training costs of up to 68%, due to an inefficient tokenization vocabulary.</abstract>
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%0 Conference Proceedings
%T Tokenizer Choice For LLM Training: Negligible or Crucial?
%A Ali, Mehdi
%A Fromm, Michael
%A Thellmann, Klaudia
%A Rutmann, Richard
%A Lübbering, Max
%A Leveling, Johannes
%A Klug, Katrin
%A Ebert, Jan
%A Doll, Niclas
%A Buschhoff, Jasper
%A Jain, Charvi
%A Weber, Alexander
%A Jurkschat, Lena
%A Abdelwahab, Hammam
%A John, Chelsea
%A Ortiz Suarez, Pedro
%A Ostendorff, Malte
%A Weinbach, Samuel
%A Sifa, Rafet
%A Kesselheim, Stefan
%A Flores-Herr, Nicolas
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F ali-etal-2024-tokenizer
%X The recent success of large language models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot.Shedding light on this underexplored area, we conduct a comprehensive study on the influence of tokenizer choice on LLM downstream performance by training 24 mono- and multilingual LLMs at a 2.6B parameter scale, ablating different tokenizer algorithms and parameterizations. Our studies highlight that the tokenizer choice can significantly impact the model’s downstream performance and training costs. In particular, we find that the common tokenizer evaluation metrics fertility and parity are not always predictive of model downstream performance, rendering these metrics a questionable proxy for the model’s downstream performance. Furthermore, we show that multilingual tokenizers trained on the five most frequent European languages require vocabulary size increases of factor three in comparison to English. While English-centric tokenizers have been applied to the training of multi-lingual LLMs in the past, we find that this approach results in a severe downstream performance degradation and additional training costs of up to 68%, due to an inefficient tokenization vocabulary.
%U https://aclanthology.org/2024.findings-naacl.247
%P 3907-3924
Markdown (Informal)
[Tokenizer Choice For LLM Training: Negligible or Crucial?](https://aclanthology.org/2024.findings-naacl.247) (Ali et al., Findings 2024)
ACL
- Mehdi Ali, Michael Fromm, Klaudia Thellmann, Richard Rutmann, Max Lübbering, Johannes Leveling, Katrin Klug, Jan Ebert, Niclas Doll, Jasper Buschhoff, Charvi Jain, Alexander Weber, Lena Jurkschat, Hammam Abdelwahab, Chelsea John, Pedro Ortiz Suarez, Malte Ostendorff, Samuel Weinbach, Rafet Sifa, et al.. 2024. Tokenizer Choice For LLM Training: Negligible or Crucial?. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3907–3924, Mexico City, Mexico. Association for Computational Linguistics.