2026-06-17T17:16:50Zhttps://iris.cnr.it/oai/request

oai:iris.cnr.it:20.500.14243/5704462026-03-04T01:28:17Zcom_20.500.14243_46com_20.500.14243_21col_20.500.14243_47ou_ou239

From Human Reading to NLM Understanding: Evaluating the Role of Eye-Tracking Data in Encoder-Based Models Dini L. Domenichelli L. Brunato D. Dell'Orletta F. Dini, L. Domenichelli, L. Brunato, D. Dell'Orletta, F. Large Language Models (LLMs) Eye-tracking Interpretability 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 eyetracking 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. 2025 info:eu-repo/semantics/conferenceObject https://hdl.handle.net/20.500.14243/570446 10.18653/v1/2025.acl-long.870 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-105021066891 eng ispartofbook:Proceedings of the Annual Meeting of the Association for Computational Linguistics 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025 volume:1 firstpage:17796 lastpage:17813 numberofpages:18 serie:PROCEEDINGS OF THE CONFERENCE - ASSOCIATION FOR COMPUTATIONAL LINGUISTICS. MEETING info:eu-repo/semantics/openAccess Association for Computational Linguistics (ACL) license:Creative commons license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/