Toward a Theory of Tokenization in LLMs

arXiv:2404.08335v1 Announce Type: cross
Abstract: While there has been a large body of research attempting to circumvent tokenization for language modeling (Clark et al., 2022; Xue et al., 2022), the current consensus is that it is a necessary initial step for designing state-of-the-art performant language models. In this paper, we investigate tokenization from a theoretical point of view by studying the behavior of transformers on simple data generating processes. When trained on data drawn from certain simple $k^{\text{th}}$-order Markov processes …

abstract art arxiv consensus cs.cl cs.lg current designing language language models llms modeling paper research state theory tokenization type