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    <abstract>Eye movement offers valuable insights into human visual attention during assessment of machine-generated texts, yet existing research and resources in this area are limited. To bridge this gap, we introduce Gaze Responses for Evaluating AI Texts (GREAT), a comprehensive dataset capturing human eye-movement features during screen reading of passages generated by large language models (LLMs). The dataset includes raw eye-movement recordings, reading-time measurements, and post-reading evaluations for LLM-generated passage pairs, alongside rigorous validation metrics. The collected eye-movement features demonstrate strong explanatory power in predicting text quality. When integrated with negative log-likelihood (NLL), a commonly used metric for evaluating text quality, it substantially enhances model performance across all standard statistical criteria. These findings demonstrate that eye-movement can act as an effective source of information that complements probabilistic metrics, for the task of automatic text quality assessment. The full dataset and some processing code are publicly available at https://github.com/qwurd231/GREAT.</abstract>
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%0 Conference Proceedings
%T Eye Movement Features Can Predict Human Preferences on Machine-Generated Texts
%A He, Xiaoshan
%A Liu, Xiaoqun
%A He, Haodong
%A Wang, Yu
%A Xu, Yang
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F he-etal-2026-eye
%X Eye movement offers valuable insights into human visual attention during assessment of machine-generated texts, yet existing research and resources in this area are limited. To bridge this gap, we introduce Gaze Responses for Evaluating AI Texts (GREAT), a comprehensive dataset capturing human eye-movement features during screen reading of passages generated by large language models (LLMs). The dataset includes raw eye-movement recordings, reading-time measurements, and post-reading evaluations for LLM-generated passage pairs, alongside rigorous validation metrics. The collected eye-movement features demonstrate strong explanatory power in predicting text quality. When integrated with negative log-likelihood (NLL), a commonly used metric for evaluating text quality, it substantially enhances model performance across all standard statistical criteria. These findings demonstrate that eye-movement can act as an effective source of information that complements probabilistic metrics, for the task of automatic text quality assessment. The full dataset and some processing code are publicly available at https://github.com/qwurd231/GREAT.
%U https://aclanthology.org/2026.acl-srw.42/
%P 470-486

Markdown (Informal)

[Eye Movement Features Can Predict Human Preferences on Machine-Generated Texts](https://aclanthology.org/2026.acl-srw.42/) (He et al., ACL 2026)

• Eye Movement Features Can Predict Human Preferences on Machine-Generated Texts (He et al., ACL 2026)

ACL

• Xiaoshan He, Xiaoqun Liu, Haodong He, Yu Wang, and Yang Xu. 2026. Eye Movement Features Can Predict Human Preferences on Machine-Generated Texts. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 470–486, San Diego, California, United States. Association for Computational Linguistics.