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.

本文研究利用大型语言模型来预测人类阅读行为，并比较了针对特定语言和多语言的预训练变压器模型在预测荷兰语、英语、德语和俄语文本上反映自然人类句子处理的阅读时间测量方面的表现。结果显示，变压器模型可以准确地模拟人类阅读行为，预测出了各种眼动特征，表明变压器模型可以以类似于人类处理机制的方式隐式地编码语言的相对重要性。作者还分析了这些模型的跨领域和跨语言能力，并展示了它们如何反映人类句子处理。