UAM-CSI at MultiGEC-2025: Parameter-efficient LLM Fine-tuning for Multilingual Grammatical Error Correction

Abstract

This paper describes the solution of the UAMCSI team to the shared task on Multilingual Grammatical Error Correction (MultiGEC-2025), which is part of the workshop on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL). The shared task covers 12 languages: Czech, English, Estonian, German, Greek, Icelandic, Italian, Latvian, Russian, Slovene, Swedish and Ukrainian. The aim of the task is to correct errors in the provided texts. Our system is a google/gemma-2-9b-it model with 2 QLoRA adapters, one for the minimal-edit track and another for the fluency-edit track. Our solution achieves the best performance on the test sets on GLEU and F0.5 metrics for all languages and the best performance on the Scribendi Score metric except for the Greek language in the minimal-edit track.