Masked language models directly encode linguistic uncertainty

Cassandra L Jacobs, Ryan J Hubbard

Abstract:

Recent advances in human language processing
research have suggested that the predictive power of
large language models (LLMs) can serve as cognitive
models of human language processing. Evidence
for this comes from LLMs’ close fit to human
psychophysical data, such as reaction times or brain
responses in language comprehension experiments.
Those adopting LLM architectures as models of
human language processing frame the problem of
language comprehension as prediction of the next
linguistic event (Goodkind and Bicknell, 2018; Eisape
et al., 2020), in particular focusing on lexical or
syntactic surprisal. However, this approach fails
to consider that comprehenders make predictions
using some representation of the content of an
utterance. That is, in contrast to surprisal, readers
make use of a mental model that creates an evolving
understanding of who is doing what to whom and
how. In contrast to comprehenders, surprisal measures
do not make predictions about the content, as surprisal
simply measures the conditional probability of some
linguistic event given the surrounding context.