@inproceedings{singh-etal-2025-eyellm,
    title = "{E}ye{LLM}: Using Lookback Fixations to Enhance Human-{LLM} Alignment for Text Completion",
    author = "Singh, Astha  and
      Torrance, Mark  and
      Chukharev, Evgeny",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bea-1.61/",
    pages = "841--849",
    ISBN = "979-8-89176-270-1",
    abstract = "Recent advances in LLMs offer new opportunities for supporting student writing, particularly through real-time, composition-level feedback. However, for such support to be effective, LLMs need to generate text completions that align with the writer{'}s internal representation of their developing message, a representation that is often implicit and difficult to observe. This paper investigates the use of eye-tracking data, specifically lookback fixations during pauses in text production, as a cue to this internal representation. Using eye movement data from students composing texts, we compare human-generated completions with LLM-generated completions based on prompts that either include or exclude words and sentences fixated during pauses. We find that incorporating lookback fixations enhances human-LLM alignment in generating text completions. These results provide empirical support for generating fixation-aware LLM feedback and lay the foundation for future educational tools that deliver real-time, composition-level feedback grounded in writers' attention and cognitive processes."
}