- Generative AI models face limitations due to tokenization, which breaks text into smaller pieces called tokens
- Tokenization introduces biases and challenges, such as odd spacing and differences in case treatment
- Tokenization poses difficulties in non-English languages, as some languages do not use spaces to separate words
- Tokenization affects mathematical tasks in AI models, leading to confusion in handling digits and numerical patterns
- Potential solutions to tokenization challenges include exploring new model architectures and models like MambaByte that work directly with raw bytes
https://techcrunch.com/2024/07/06/tokens-are-a-big-reason-todays-generative-ai-falls-short/
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