We present the MultiplEYE Text Corpus, a large-scale, document-level, multi-parallel resource designed to advance cross-linguistic research on reading and language processing. The corpus provides paragraph-level alignment for texts in 39 languages spanning seven language families and seven scripts. Unlike many existing multilingual corpora, a substantial number of documents were originally written in languages other than English, reducing English-centric bias and supporting more typologically diverse investigations. The texts are carefully selected to balance linguistic richness with experimental feasibility, particularly for eye-tracking-while-reading studies. Developed within a multi-lab initiative, the MultiplEYE Text Corpus follows unified translation, alignment, and experimental design guidelines to ensure cross-linguistic comparability. Its inclusion of texts varying in type and difficulty enables research on discourselevel processing, genre effects, and individual differences across a wide range of languages. The text corpus and accompanying metadata provide a robust foundation for multilingual psycholinguistic and computational modeling research. Data and materials are publicly available at https://doi.org/10.23668/psycharchives.21750.

Direct Preference Optimisation (DPO) has emerged as an effective approach for aligning large language models (LLMs) with human preferences. However, its reliance on binary feedback restricts its ability to capture nuanced human judgements. To address this limitation, we introduce a gaze-informed extension that incorporates implicit, fine-grained signals from eye-tracking-while-reading into the DPO framework. Eye movements, reflecting real-time human cognitive processing, provide fine-grained signals about the linguistic characteristics of the text that is being read. We leverage these signals and modify DPO by introducing a gaze-based additional loss term, that quantifies the differences between the model’s internal sentence representations and cognitive (i.e., gaze-based) representations derived from the readers’ gaze patterns. We explore the use of both human and synthetic gaze signals, employing a generative model of eye movements in reading to generate supplementary training data, ensuring the scalability of our approach. We apply the proposed approach to modelling linguistic acceptability. Experiments conducted on the CoLA dataset demonstrate performance gains in grammatical acceptability classification tasks when the models are trained in the gaze-augmented setting. These results demonstrate the utility of leveraging gaze data to align language models with human preferences. All code and data are available from Github.