  Recent studies have shown that as Transformer-based language models become
larger and are trained on very large amounts of data, the fit of their
surprisal estimates to naturalistic human reading times degrades. The current
work presents a series of analyses showing that word frequency is a key
explanatory factor underlying these two trends. First, residual errors from
four language model families on four corpora show that the inverse correlation
between model size and fit to reading times is the strongest on the subset of
least frequent words, which is driven by excessively accurate predictions of
larger model variants. Additionally, training dynamics reveal that during later
training steps, all model variants learn to predict rare words and that larger
model variants do so more accurately, which explains the detrimental effect of
both training data amount and model size on fit to reading times. Finally, a
feature attribution analysis demonstrates that larger model variants are able
to accurately predict rare words based on both an effectively longer context
window size as well as stronger local associations compared to smaller model
variants. Taken together, these results indicate that Transformer-based
language models' surprisal estimates diverge from human-like expectations due
to the superhumanly complex associations they learn for predicting rare words.
