Exploring the Feasibility of Multilingual Grammatical Error Correction with a Single LLM up to 9B parameters: A Comparative Study of 17 Models
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Abstract (arXiv)
Recent language models can successfully solve various language-related tasks, and many understand inputs stated in different languages. In this paper, we explore the performance of 17 popular models used to correct grammatical issues in texts stated in English, German, Italian, and Swedish when using a single model to correct texts in all those languages. We analyze the outputs generated by these models, focusing on decreasing the number of grammatical errors while keeping the changes small. The conclusions drawn help us understand what problems occur among those models and which models can be recommended for multilingual grammatical error correction tasks. We list six models that improve grammatical correctness in all four languages and show that Gemma 9B is currently the best performing one for the languages considered.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 40 references that are in the map. First posted on arXiv 9 May 2025.
Builds on
20 of 40 references.
References · 20
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8 more
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.