Retrieving in-context demonstrations by matching natural-language grammatical error explanations beats input-text similarity for few-shot multilingual GEC.
Evaluating GPT-3.5 and GPT-4 on Grammatical Error Correction for Brazilian Portuguese
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abstract
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.
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cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction
Retrieving in-context demonstrations by matching natural-language grammatical error explanations beats input-text similarity for few-shot multilingual GEC.