Generative AI models like GPT-4o use tokenization to process text by breaking it down into smaller pieces called tokens.
Tokenization can introduce biases and limitations, such as odd spacing and differences in how case is treated.
Tokenization methods vary across languages, impacting model performance and cost, especially for non-English languages.
Tokenization challenges also affect mathematical tasks, anagram problems, and word reversals in AI models.
Research is exploring alternatives like byte-level models to overcome tokenization limitations in generative AI.
Source: https://techcrunch.com/2024/07/06/tokens-are-a-big-reason-todays-generative-ai-falls-short/
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