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.

创新的方法使用多语言大型语言模型计算句子级别的度量标准，预测并阐明读者在理解句子时遇到的处理困难，提供了实质性的可解释性和高准确性，为将大型语言模型和认知科学整合的未来研究提供了有希望的途径。