Evaluating GPT-3.5 and GPT-4 on Grammatical Error Correction for Brazilian Portuguese
This work addresses the need for practical grammatical error correction tools for Brazilian Portuguese, but it is incremental as it applies existing models to a new language and dataset.
The study tackled the problem of grammatical error correction for Brazilian Portuguese by evaluating GPT-3.5 and GPT-4 against Microsoft Word and Google Docs, finding that GPT-4 had higher recall but lower precision, leading to overcorrection.
We investigate the effectiveness of GPT-3.5 and GPT-4, two large language models, as Grammatical Error Correction (GEC) tools for Brazilian Portuguese and compare their performance against Microsoft Word and Google Docs. We introduce a GEC dataset for Brazilian Portuguese with four categories: Grammar, Spelling, Internet, and Fast typing. Our results show that while GPT-4 has higher recall than other methods, LLMs tend to have lower precision, leading to overcorrection. This study demonstrates the potential of LLMs as practical GEC tools for Brazilian Portuguese and encourages further exploration of LLMs for non-English languages and other educational settings.