  We analyze if large language models are able to predict patterns of human
reading behavior. We compare the performance of language-specific and
multilingual pretrained transformer models to predict reading time measures
reflecting natural human sentence processing on Dutch, English, German, and
Russian texts. This results in accurate models of human reading behavior, which
indicates that transformer models implicitly encode relative importance in
language in a way that is comparable to human processing mechanisms. We find
that BERT and XLM models successfully predict a range of eye tracking features.
In a series of experiments, we analyze the cross-domain and cross-language
abilities of these models and show how they reflect human sentence processing.
