Controlling Reading Ease with Gaze-Guided Text Generation
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Abstract (arXiv)
The way our eyes move while reading can tell us about the cognitive effort required to process the text. In the present study, we use this fact to generate texts with controllable reading ease. Our method employs a model that predicts human gaze patterns to steer language model outputs towards eliciting certain reading behaviors. We evaluate the approach in an eye-tracking experiment with native and non-native speakers of English. The results demonstrate that the method is effective at making the generated texts easier or harder to read, measured both in terms of reading times and perceived difficulty of the texts. A statistical analysis reveals that the changes in reading behavior are mostly due to features that affect lexical processing. Possible applications of our approach include text simplification for information accessibility and generation of personalized educational material for language learning.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 65 references that are in the map. First posted on arXiv 25 Jan 2026.
Builds on
18 of 65 references.
References · 18
- 2019Plug and Play Language Models: A Simple Approach to Controlled Text Generationcited by 799
- 2019CTRL: A Conditional Transformer Language Model for Controllable Generationcited by 762
- 2022Contrastive Decoding: Open-ended Text Generation as Optimizationcited by 609
- 2021DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Expertscited by 441
- 2020GeDi: Generative Discriminator Guided Sequence Generationcited by 378
- 2021FUDGE: Controlled Text Generation With Future Discriminatorscited by 376
- 2025Harnessing Multiple Large Language Models: A Survey on LLM Ensemblecited by 86
- 2023Evaluating Large Language Models on Controlled Generation Taskscited by 73
- 2022DIRECTOR: Generator-Classifiers For Supervised Language Modelingcited by 38
- 2023BLESS: Benchmarking Large Language Models on Sentence Simplificationcited by 35
- 2023Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Readingcited by 19
- 2023Flesch or Fumble? Evaluating Readability Standard Alignment of Instruction-Tuned Language Modelscited by 17
6 more
- 2023ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Textscited by 14
- 2024Fine-Grained Prediction of Reading Comprehension from Eye Movementscited by 9
- 2024Decoding Reading Goals from Eye Movementscited by 6
- 2023pymovements: A Python Package for Eye Movement Data Processingcited by 5
- 2024EMTeC: A Corpus of Eye Movements on Machine-Generated Textscited by 5
- 2025Progressive Document-level Text Simplification via Large Language Modelscited by 5
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Citing papers per year: 2026: 3.
3 papers.