Rethinking Tokenization

Albert discusses the challenges posed by traditional tokenization methods in language models, highlighting issues like arithmetic and spelling difficulties. He advocates for a shift towards end-to-end learning approaches that minimize preprocessing artifacts, suggesting that as computational tools improve, the reliance on structured tokenization should decrease to enhance model performance.