Researchers are exploring alternatives to tokenization, such as byte-level state space models like MambaByte, which can process more data without a performance penalty by working directly with raw bytes representing text. However, these models are still in the early stages of research. Until a breakthrough in tokenization is achieved, it seems that new model architectures will be the key to improving generative AI's understanding and processing of text.
Key takeaways:
- Generative AI models use a process called tokenization to break down text into smaller pieces called tokens, which can introduce biases and limitations due to odd spacing, case sensitivity, and language differences.
- Tokenization can create problems in languages other than English, particularly those that do not use spaces to separate words or those with logographic and agglutinative systems of writing, leading to high token counts and less efficient model performance.
- Tokenization can also cause issues with numerical data, as tokenizers might not consistently represent numbers, leading to confusion in understanding numerical patterns and context.
- Alternative models like MambaByte, which work directly with raw bytes representing text and other data, may offer a solution by eliminating tokenization, but they are currently in the early research stages.