Should you marginalize over possible tokenizations?
Nadezhda Chirkova Germán Kruszewski Jos Rozen Marc Dymetman
Autoregressive language models (LMs) map token sequences to probabilities.
The usual practice for computing the probability of any character string (e.g.
English sentences) is to first transform it into a sequence of tokens that is
scored by the model. However, there are exponentially many token sequences that
represent any given string. To truly compute the probability of a string one
should marginalize over all tokenizations, which is typically intractable.
Here, we analyze whether the practice of ignoring the marginalization is
justified. To this end, we devise an importance-sampling-based algorithm that
allows us to compute estimates of the marginal probabilities and compare them
to the default procedure in a range of state-of-the-art models and datasets.
Our results show that the gap in log-likelihood is no larger than 0.5% in most
cases, but that it becomes more pronounced for data with long complex words.