Where is the signal in tokenization space?
This addresses a fundamental assumption in LLM tokenization for researchers and practitioners, revealing hidden signal that can enhance model performance, though it is incremental in refining existing methods.
The paper tackles the problem that LLMs assume a single canonical tokenization for text probability, but tokenization is not unique, and shows that computing the most likely or marginal tokenization is hard. It empirically demonstrates that aggregating probabilities from non-canonical tokenizations leads to improvements on LLM benchmarks, with gains across various architectures.
Large Language Models (LLMs) are typically shipped with tokenizers that deterministically encode text into so-called canonical token sequences, to which the LLMs assign probability values. One common assumption is that the probability of a piece of text is the probability of its canonical token sequence. However, the tokenization of a string is not unique: e.g., the Llama2 tokenizer encodes Tokens as [Tok,ens], but [Tok,en,s] also represents the same text. In this paper, we study non-canonical tokenizations. We prove that, given a string, it is computationally hard to find the most likely tokenization for an autoregressive LLM, as well as to compute the marginal probability over all possible tokenizations. We then show how the marginal is, in most cases, indistinguishable from the canonical probability. Surprisingly, we then empirically demonstrate the existence of a significant amount of signal hidden within tokenization space. Notably, by simply aggregating the probabilities of non-canonical tokenizations, we achieve improvements across a range of LLM evaluation benchmarks for a variety of architectures, including transformers and state space models.