---
title: "A Comparative Study of Multilingual Fine-tuning and Prompting"
description: "The authors compare ways to build four-level readability classifiers for Galician. 45 references."
authors: ["Sandra Rodríguez Rey", "Marcos Garcia"]
year: 2026
venue: "Proceedings of the Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC 2026"
source: "https://aclanthology.org/2026.readi-1.8/"
doi: "10.63317/4tnwhe3r9579"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/rey-2026-comparative-study-multilingual-fine-tuning/"
---
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# A Comparative Study of Multilingual Fine-tuning and Prompting for Automatic Text Readability Classification in Galician

**Authors:** Sandra Rodríguez Rey and [Marcos Garcia](https://md.research.elamur.ai/free/en/authors/marcos-garcia/) · **Year:** 2026 · **Venue:** [Proceedings of the Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC 2026](https://md.research.elamur.ai/free/en/venues/readi/) ([Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC 2026](https://md.research.elamur.ai/free/en/venues/readi/2026/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.readi-1.8/) · [PDF](https://aclanthology.org/2026.readi-1.8.pdf) · [DOI 10.63317/4tnwhe3r9579](https://doi.org/10.63317/4tnwhe3r9579) · [OpenAlex](https://openalex.org/W7166560170)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Automatic readability assessment has advanced mainly for well-resourced languages, while Galician has very little annotated material. The authors compare ways to build four-level readability classifiers for Galician. Training data come from the iRead4Skills Dataset 1 (Spanish, Portuguese, French), used both in its original form and after machine translation into Galician with SalamandraTA. Evaluation uses the native Galician Corlega corpus. Three strategies are tested:

- fine-tuning monolingual BERT-style encoders on the translated material;
- fine-tuning multilingual encoders on original versus translated data, to measure the value of translation-based augmentation;
- zero-shot and few-shot prompting of several LLMs.

Prompted generative models still trail fine-tuned encoders on this task. Machine-translated data helps monolingual Galician models but adds little for multilingual ones, which already cover the source languages. The study offers practical guidance for readability modelling in low-resource settings. It also supports educational tools that could help teachers and learners find Galician reading material at the right level. The best models are released openly on Hugging Face.

## Key points
- **Corlega corpus:** 480 Galician texts (145,854 tokens) from genres such as social media, literature, ads, political and legal texts. One expert labelled them into four levels aligned with Celga, CEFR and the iRead4Skills scheme. It is released on Zenodo under CC BY-NC-ND 4.0.
- **Training data:** iRead4Skills Dataset 1 contains roughly 2,000–3,000 texts per language (Spanish, Portuguese, French) on four complexity levels. A Galician version was produced with SalamandraTA.
- **Data split:** Corlega served only for validation (25%) and evaluation (75%), with level-balanced random selection. Class imbalance in the training sets was handled through oversampling.
- **Main findings:** Fine-tuned encoders outperform zero-/few-shot LLM prompting for Galician. Translation-based augmentation benefits monolingual models but has a minimal effect on multilingual ones.
- **Released models:** The best-performing models are published on Hugging Face (huggingface.co/sandrarrey).

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

## Institutions

- [Universidade de Santiago de Compostela](https://md.research.elamur.ai/free/en/universities/universidade-de-santiago-de-compostela/) — University, Santiago de Compostela, Spain · 13 works · 364 citations

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## Cite

Cite as: Sandra Rodríguez Rey and Marcos Garcia. 2026. A Comparative Study of Multilingual Fine-tuning and Prompting for Automatic Text Readability Classification in Galician. Proceedings of the Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC 2026.

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

```bibtex
@inproceedings{rey2026comparative,
  title = {{A Comparative Study of Multilingual Fine-tuning and Prompting for Automatic Text Readability Classification in Galician}},
  author = {Sandra Rodríguez Rey and Marcos Garcia},
  booktitle = {Proceedings of the Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC 2026},
  year = {2026},
  doi = {10.63317/4tnwhe3r9579},
  url = {https://doi.org/10.63317/4tnwhe3r9579}
}
```

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44. Language Resource References Alice Pintard, Thomas François, Justine Nagant de Deuxchaisnes, Sílvia Barbosa, Maria Leonor Reis, Michell Moutinho, Ricardo Monteiro, Raquel Amaro, Susana Correia, Sandra Rodríguez Rey, Marcos Garcia González, Keran Mu, and Xavier Blanco Escoda. 2024. iRead4Skills Datas… [find](https://scholar.google.com/scholar?q=Language+Resource+References+Alice+Pintard%2C+Thomas+Fran%C3%A7ois%2C+Justine+Nagant+de+Deuxchaisnes%2C+S%C3%ADlvia+Barbosa%2C+Maria+Leonor+Reis%2C+Michell+Moutinho%2C+Ricardo+Monteiro%2C+Raquel+Amaro%2C+Susana+Correia%2C+Sandra)
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*Updated 1 November 2026*