@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://aclanthology.org/2025.bea-1.61/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T EyeLLM: Using Lookback Fixations to Enhance Human-LLM Alignment for Text Completion
%A Singh, Astha
%A Torrance, Mark
%A Chukharev, Evgeny
%Y Kochmar, Ekaterina
%Y Alhafni, Bashar
%Y Bexte, Marie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%S Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-270-1
%F singh-etal-2025-eyellm
%X 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.
%R 10.18653/v1/2025.bea-1.61
%U https://aclanthology.org/2025.bea-1.61/
%U https://doi.org/10.18653/v1/2025.bea-1.61
%P 841-849
Markdown (Informal)
[EyeLLM: Using Lookback Fixations to Enhance Human-LLM Alignment for Text Completion](https://aclanthology.org/2025.bea-1.61/) (Singh et al., BEA 2025)
ACL