| Challenge: | Using a gaze-based model, we generate texts with controllable reading ease. |
| Approach: | They propose a method that predicts gaze patterns to steer language model outputs towards eliciting certain reading behaviors by predicting eye-tracking measures. |
| Outcome: | The proposed method generates texts with controllable reading ease using eye-tracking with native and non-native speakers of English. |
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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). |
Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (2024.acl-tutorials)
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| Challenge: | In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria. |
| Approach: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks that takes a piece of text as input and generates a revision that is improved according to some specific criteria. |
| Outcome: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and generates a revision that is improved according to some specificcriteria. |
From Human Reading to NLM Understanding: Evaluating the Role of Eye-Tracking Data in Encoder-Based Models (2025.acl-long)
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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. |
| Approach: | They used eye-gaze data from the Ghent Eye-Tracking Corpus to investigate how integrating knowledge of human reading behavior impacts Neural Language Models. |
| Outcome: | The proposed approach does not degrade downstream task performance, enhances alignment between model attention and human attention patterns, and compresses the embedding space. |
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 . |
| Approach: | The tutorial combines eye tracking during reading with NLP . it covers eye movements in reading, integrating eye movement data in NLP models . |
| Outcome: | The tutorial outlines how eye movements in reading can be leveraged for NLP . it provides the essential background for conducting research on joint modeling of eye movements and text. |
Synthesizing Human Gaze Feedback for Improved NLP Performance (2023.eacl-main)
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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. |
| Approach: | They propose a model for generating human scanpaths over text that approximates meaningful cognitive signals in human gaze patterns. |
| Outcome: | The proposed model can approximate meaningful cognitive signals in human gaze patterns. |
Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding (2023.emnlp-main)
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| Challenge: | Existing models for augmenting language models with human scanpaths have been developed, but the potential of synthetic gaze data across NLP tasks remains unexplored. |
| Approach: | They propose to combine synthetic scanpath generation with a scanpath-augmented language model, eliminating the need for human gaze data. |
| Outcome: | The proposed model outperforms the underlying language model and achieves comparable performance to a language model augmented with real human gaze data. |
Attribute Alignment: Controlling Text Generation from Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Large language models can generate text with sentiment polarity or specific topics without changing the original model parameters. |
| Approach: | They propose a method for controlling text generation by aligning disentangled attribute representations. |
| Outcome: | The proposed method shows large performance gains while maintaining diversity and fluency. |
Fine-Tuning Pre-Trained Language Models with Gaze Supervision (2024.acl-short)
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| Challenge: | Existing pre-trained language models lack a gaze module to exploit cognitive signals. |
| Approach: | They propose to integrate a gaze module into pre-trained language models at the fine-tuning stage to exploit cognitive signals. |
| Outcome: | The proposed model improves performance on the GLUE benchmark and standard fine-tuning and text augmentation baselines. |
Fine-Grained Prediction of Reading Comprehension from Eye Movements (2024.emnlp-main)
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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 . |
| Approach: | They propose to use eyetracking data to predict reading comprehension of a single participant . they use a battery of recent models and three new multimodal language models . |
| Outcome: | The proposed model can predict reading comprehension of a single participant from eye movements over a paragraph. |
Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour (P18-1)
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Sandeep Mathias, Diptesh Kanojia, Kevin Patel, Samarth Agrawal, Abhijit Mishra, Pushpak Bhattacharyya
| Challenge: | Existing methods to predict text quality include estimating subjective aspects of text, like structure, clarity, etc. |
| Approach: | They propose to capture gaze behaviour to help predict text quality by reporting improvements obtained by adding gaze features to traditional textual features for score prediction. |
| Outcome: | The proposed model shows that capturing gaze behaviour improves the accuracy of score prediction when the reader has fully understood the text. |