Tokens serve as compressed, meaningful representations of data, allowing transformers to excel in processing. The effectiveness of attention mechanisms hinges on the abstraction level of these tokens, with proper tokenization being crucial for optimal model performance. Historically, distinctions like word-level versus character-level modeling were significant, but modern approaches treat tokenization as a hyperparameter, emphasizing the importance of meaningful representation in language models.
