---
title: "Controlling Reading Ease with Gaze-Guided Text Generation"
description: "LLM prompting can adjust text readability, but the outputs often miss the target difficulty and give little… 72 references, cited by 1 paper here."
authors: ["Andreas Säuberli", "Darja Jepifanova", "Diego Frassinelli", "Barbara Plank"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.eacl-long.107/"
doi: "10.18653/v1/2026.eacl-long.107"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sauberli-2026-controlling-reading-ease-gaze-guided/"
---
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# Controlling Reading Ease with Gaze-Guided Text Generation

**Authors:** [Andreas Säuberli](https://md.research.elamur.ai/free/en/authors/andreas-sauberli/), Darja Jepifanova, [Diego Frassinelli](https://md.research.elamur.ai/free/en/authors/diego-frassinelli/) and [Barbara Plank](https://md.research.elamur.ai/free/en/authors/barbara-plank/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-long.107/) · [PDF](https://aclanthology.org/2026.eacl-long.107.pdf) · [DOI 10.18653/v1/2026.eacl-long.107](https://doi.org/10.18653/v1/2026.eacl-long.107) · [OpenAlex](https://openalex.org/W7140082562)

**Citations:** 2 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/sauberli-2026-controlling-reading-ease-gaze-guided/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLM prompting can adjust text readability, but the outputs often miss the target difficulty and give little control over how generation works. The authors use eye-tracking signals to steer generation instead. A small gaze model predicts how long readers would look at each word. During decoding, it rescores the language model's top candidate continuations. The predicted reading time is multiplied by a user-chosen gaze weight and added to the token log-probabilities. Beam search then picks the output. A negative weight favours text that should be faster to read, and a positive weight favours harder text. Neither model has to be retrained when the other is swapped. The authors generated short English stories at different gaze weights and tested them in an eye-tracking study with L1 and L2 English readers. The method shifted both measured reading times and how difficult readers judged the texts to be. Most of the effect came from lexical properties such as word length and word prevalence. The work suggests a path toward reading-adaptive text simplification and personalised language-learning material.

## Key points
- **Models:** The base generator was the off-the-shelf instruction-tuned Llama 3.2 (3B). The gaze model was GPT-2 (124M), fine-tuned to predict z-normalised word-level first-pass reading time (FPRT). Its training data was EMTeC, which contains eye movements from 107 L1 English readers.
- **Scoring:** The gaze score is the sum of predicted FPRTs across a candidate sequence. A gaze weight of 0 turns the gaze model off. Beam search helps reduce mismatches between the two models' tokenizers.
- **Stimuli:** The texts were roughly 500-word fictional stories, generated from six prompts drafted with GPT-4o and then manually selected.
- **Released resources:** The new eye-tracking dataset is on OSF (osf.io/rhgbk) and is integrated into the pymovements library. The code is at github.com/mainlp/gaze-guided-text-generation.
- **Limitations:** The study covers English only and tests a single pairing of language model and gaze model. FPRT mainly captures lexical effects, not syntactic or discourse-level ones. No primary accessibility user groups took part.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Ludwig-Maximilians-Universität München](https://md.research.elamur.ai/free/en/universities/ludwig-maximilians-universitat-munchen/) — University, Munich, Germany · 107 works · 56,478 citations
- [Munich Center for Machine Learning](https://md.research.elamur.ai/free/en/universities/munich-center-for-machine-learning/) — University, Munich, Germany · 57 works · 1,094 citations

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## Cite

Cite as: Andreas Säuberli, Darja Jepifanova, Diego Frassinelli and Barbara Plank. 2026. Controlling Reading Ease with Gaze-Guided Text Generation. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{sauberli2026controlling,
  title = {{Controlling Reading Ease with Gaze-Guided Text Generation}},
  author = {Andreas Säuberli, Darja Jepifanova, Diego Frassinelli and Barbara Plank},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.107},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.107}
}
```

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*Updated 31 October 2026*