Year: 2023
Type: conference-paper
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 nonuniformity 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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: Collaborative Research: RI: Small: NL(V)P: Natural Language (Variety) Processing, FAI: Quantifying and Mitigating Disparities in Language Technologies, CAREER: Language Technologies Against the Language of Social Discrimination, NSF-BSF: RI: Small: Efficient Transformers via Formal and Empirical Analysis, NSF-BSF: Collaborative Research: RI: Small: Multilingual Language Generation via Understanding of Code Switching, G6671297155
OpenAlex ID: W4389519066