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Language Model Tokenizers Introduce Unfairness Between Languages

Neural Information Processing Systems

Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs across different languages. In this paper, we show how disparity in the treatment of different languages arises at the tokenization stage, well before a model is even invoked. The same text translated into different languages can have drastically different tok-enization lengths, with differences up to 15 times in some cases. These disparities persist even for tokenizers that are intentionally trained for multilingual support.




Humanoid robots are getting smaller, safer and closer

FOX News

Fauna Robotics has introduced Sprout, a 3.5-foot humanoid robot designed for homes, schools and offices. The startup built the robot with safety-first features.



JASON CHAFFETZ: 2028 election will be a referendum on our AI-dominated future

FOX News

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The AI wars begin with new Super Bowl commercials

FOX News

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Flying car now for sale for 190,000

FOX News

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .