@inproceedings{ahia-etal-2023-languages,
title = "Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models",
author = "Ahia, Orevaoghene and
Kumar, Sachin and
Gonen, Hila and
Kasai, Jungo and
Mortensen, David and
Smith, Noah and
Tsvetkov, Yulia",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.614",
doi = "10.18653/v1/2023.emnlp-main.614",
pages = "9904--9923",
abstract = "Language models have graduated from being research prototypes to commercialized products offered as web APIs, and recent works have highlighted the multilingual capabilities of these products. The API vendors charge their users based on usage, more specifically on the number of {``}tokens{''} processed or generated by the underlying language models. What constitutes a token, however, is training data and model dependent with a large variance in the number of tokens required to convey the same information in different languages. In this work, we analyze the effect of this non-uniformity on the fairness of an API{'}s pricing policy across languages. We conduct a systematic analysis of the cost and utility of OpenAI{'}s language model API on multilingual benchmarks in 22 typologically diverse languages. We show evidence that speakers of a large number of the supported languages are overcharged while obtaining poorer results. These speakers tend to also come from regions where the APIs are less affordable, to begin with. Through these analyses, we aim to increase transparency around language model APIs{'} pricing policies and encourage the vendors to make them more equitable.",
}
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<abstract>Language models have graduated from being research prototypes to commercialized products offered as web APIs, and recent works have highlighted the multilingual capabilities of these products. The API vendors charge their users based on usage, more specifically on the number of “tokens” processed or generated by the underlying language models. What constitutes a token, however, is training data and model dependent with a large variance in the number of tokens required to convey the same information in different languages. In this work, we analyze the effect of this non-uniformity on the fairness of an API’s pricing policy across languages. We conduct a systematic analysis of the cost and utility of OpenAI’s language model API on multilingual benchmarks in 22 typologically diverse languages. We show evidence that speakers of a large number of the supported languages are overcharged while obtaining poorer results. These speakers tend to also come from regions where the APIs are less affordable, to begin with. Through these analyses, we aim to increase transparency around language model APIs’ pricing policies and encourage the vendors to make them more equitable.</abstract>
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%0 Conference Proceedings
%T Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models
%A Ahia, Orevaoghene
%A Kumar, Sachin
%A Gonen, Hila
%A Kasai, Jungo
%A Mortensen, David
%A Smith, Noah
%A Tsvetkov, Yulia
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ahia-etal-2023-languages
%X Language models have graduated from being research prototypes to commercialized products offered as web APIs, and recent works have highlighted the multilingual capabilities of these products. The API vendors charge their users based on usage, more specifically on the number of “tokens” processed or generated by the underlying language models. What constitutes a token, however, is training data and model dependent with a large variance in the number of tokens required to convey the same information in different languages. In this work, we analyze the effect of this non-uniformity on the fairness of an API’s pricing policy across languages. We conduct a systematic analysis of the cost and utility of OpenAI’s language model API on multilingual benchmarks in 22 typologically diverse languages. We show evidence that speakers of a large number of the supported languages are overcharged while obtaining poorer results. These speakers tend to also come from regions where the APIs are less affordable, to begin with. Through these analyses, we aim to increase transparency around language model APIs’ pricing policies and encourage the vendors to make them more equitable.
%R 10.18653/v1/2023.emnlp-main.614
%U https://aclanthology.org/2023.emnlp-main.614
%U https://doi.org/10.18653/v1/2023.emnlp-main.614
%P 9904-9923
Markdown (Informal)
[Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models](https://aclanthology.org/2023.emnlp-main.614) (Ahia et al., EMNLP 2023)
ACL