Model Card for tartuNLP/Llammas-base-p1-GPT-4o-human-error-mix-paragraph-GEC
The user’s input text, i.e., a paragraph, is passed to the first model M1 (this model) as a whole, which then outputs the corrected text.
Citation
https://aclanthology.org/2025.bea-1.72/
BibTeX:
@inproceedings{vainikko-etal-2025-paragraph,
title = "Paragraph-level Error Correction and Explanation Generation: Case Study for {E}stonian",
author = "Vainikko, Martin and
Kamarik, Taavi and
Kert, Karina and
Liin, Krista and
Maine, Silvia and
Allkivi, Kais and
Kaivapalu, Annekatrin and
Fishel, Mark",
editor = {Kochmar, Ekaterina and
Alhafni, Bashar and
Bexte, Marie and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Tack, Ana{\"i}s and
Yaneva, Victoria and
Yuan, Zheng},
booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.bea-1.72/",
doi = "10.18653/v1/2025.bea-1.72",
pages = "953--967",
ISBN = "979-8-89176-270-1",
abstract = "We present a case study on building task-specific models for grammatical error correction and explanation generation tailored to learners of Estonian. Our approach handles whole paragraphs instead of sentences and leverages prompting proprietary large language models for generating synthetic training data, addressing the limited availability of error correction data and the complete absence of correction justification/explanation data in Estonian. We describe the chosen approach and pipeline and provide technical details for the experimental part. The final outcome is a set of open-weight models, which are released with a permissive license along with the generated synthetic error correction and explanation data."
}