This article discusses the process of tokenization in language models, highlighting the disparities that exist across different languages. It explains how language models like ChatGPT use tokenization to split text into smaller units called tokens. However, this tokenization process is not uniform across languages, leading to variations in the number of tokens produced for equivalent expressions. The article explores the impact of this language disparity, including limitations in prompt information, increased costs, and longer processing times. It also emphasizes the importance of addressing these disparities to ensure equitable language representation and performance in AI-driven technologies.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Tokenization Disparity Across Languages | Difference in token lengths between languages | Improved tokenization algorithms and models | Equity and inclusivity in AI-driven technologies |
| Language Disparity in NLP | Disparity in NLP representation and performance | More inclusive and diverse linguistic landscape in NLP | Addressing digital divide, equity, and inclusivity |
| Historical Telecommunication Disparity | Inequities in telegraphy across languages | Improved telegraphy systems for different languages | Design limitations and costs |
| Font Representation Inequity | Challenges in rendering fonts for different languages | Improved font rendering and compatibility | Lack of universally compatible fonts |
| Language Disparity in AI Models | Inequalities in language representation and processing | More equitable and accessible AI models | Increasing global usage and diverse linguistic communities |