Eye Movement Features Can Predict Human Preferences on Machine-Generated Texts (2026.acl-srw)
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| Challenge: | Existing studies on eye movement in text quality assessment are limited . eye-movement features are important predictors of human judgments of text quality, but are costly and inconsistent. |
| Approach: | They propose to capture eye-movement features during screen reading of LLM-generated text using a dataset that includes eye-motion recordings, reading-time measurements, and post-reading evaluations. |
| Outcome: | The proposed dataset shows that eye-movement features can significantly improve models over other probabilistic metrics, including negative log-likelihood (NLL). |
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Sandeep Mathias, Diptesh Kanojia, Kevin Patel, Samarth Agrawal, Abhijit Mishra, Pushpak Bhattacharyya
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| Challenge: | a new study attempts to assess reading comprehension from eye movements in reading . eye movements provide small improvements over a text-only baseline, the authors argue . |
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Eye Tracking and NLP (2025.acl-tutorials)
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| Challenge: | tutorial combines eye tracking during reading with NLP . outlines how eye movements in reading can be leveraged for NLP methods . |
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| Challenge: | integrating eye-tracking features into Neural Language Models does not degrade downstream task performance, enhances alignment between model attention and human attention patterns, and compresses the embedding space. |
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| Challenge: | Using a gaze-based model, we generate texts with controllable reading ease. |
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| Challenge: | Existing work to predict gaze patterns during naturalistic reading has not been conducted on general text characteristics. |
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| Challenge: | Document AI models that can read visually rich documents have a long way to go before they can read them as accurately, continuously, and flexibly as humans do. |
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| Challenge: | Prior work on eye tracking and NLP reveals that human scanpaths can aid in understanding and performance of NLP models. |
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| Challenge: | Existing studies have shown that the linguistic properties of a speaker’s native language affect the cognitive processing of other languages. |
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Assessing Language Proficiency from Eye Movements in Reading (N18-1)
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| Challenge: | a novel approach to determine second language proficiency uses behavioral traces of eye movements during reading . over 1.5 billion people are learning English as a second language worldwide . traditional approaches to language proficiency testing have several drawbacks, including the fact that they are typically prepared manually and require extensive resources for test development . |
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