The majority of research in computational psycholinguistics has concentrated on the processing of words. This study introduces innovative methods for computing sentence-level metrics using multilingual large language models. The metrics developed sentence surprisal and sentence relevance and then are tested and compared to validate whether they can predict how humans comprehend sentences as a whole across languages. These metrics offer significant interpretability and achieve high accuracy in predicting human sentence reading speeds. Our results indicate that these computational sentence-level metrics are exceptionally effective at predicting and elucidating the processing difficulties encountered by readers in comprehending sentences as a whole across a variety of languages. Their impressive performance and generalization capabilities provide a promising avenue for future research in integrating LLMs and cognitive science.

翻译：绝大多数计算心理语言学的研究集中在词汇处理层面。本研究引入了利用多语言大语言模型计算句子级指标的新型方法。我们开发了句子惊讶度和句子相关性这两个指标，并通过跨语言实验验证它们能否预测人类对整体句子的理解程度。这些指标具备显著的可解释性，且在预测人类句子阅读速度方面达到了高精度。研究结果表明，这些计算层面的句子级指标能够极其有效地预测和阐释读者在理解不同语言整体句子时遇到的加工困难。其卓越的性能与泛化能力为未来将大语言模型与认知科学相结合的研究提供了富有前景的方向。