@inproceedings{dini-etal-2025-human,
    title = "From Human Reading to {NLM} Understanding: Evaluating the Role of Eye-Tracking Data in Encoder-Based Models",
    author = "Dini, Luca  and
      Domenichelli, Lucia  and
      Brunato, Dominique  and
      Dell{'}Orletta, Felice",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.870/",
    pages = "17796--17813",
    ISBN = "979-8-89176-251-0",
    abstract = "Cognitive signals, particularly eye-tracking data, offer valuable insights into human language processing. Leveraging eye-gaze data from the Ghent Eye-Tracking Corpus, we conducted a series of experiments to examine how integrating knowledge of human reading behavior impacts Neural Language Models (NLMs) across multiple dimensions: task performance, attention mechanisms, and the geometry of their embedding space. We explored several fine-tuning methodologies to inject eye-tracking features into the models. Our results reveal that incorporating these features does not degrade downstream task performance, enhances alignment between model attention and human attention patterns, and compresses the geometry of the embedding space."
}