Brief news summary

Generative AI models, such as the popular transformer models, process text differently from humans. They work with smaller units of text called tokens, which can be words, syllables, or even individual characters. Tokenization allows models to take in more information before reaching a limit called the context window. However, tokenization also introduces biases and can lead to misunderstandings. Tokenizers treat spacing and case differently, which can affect the model's understanding. Tokenization methods designed for English may not work well for languages without spaces to separate words, leading to slower completion times and higher costs for non-English tasks. Tokenization can also pose challenges in math-related tasks and linguistic features such as anagrams and word reversals. Some researchers are exploring alternative models, such as byte-level state space models, which work directly with raw bytes of text. Finding solutions to the challenges of tokenization may require new model architectures.