An empirical analysis of large language models for automated cross-prompt essay trait scoring in Brazilian Portuguese (2025)
- Authors:
- USP affiliated authors: MAUÁ, DENIS DERATANI - IME ; BARBOSA, ANDRÉ - IME ; SILVEIRA, IGOR CATANEO - IME
- Unidade: IME
- DOI: 10.5753/jbcs.2025.5817
- Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL; REDAÇÃO; INTELIGÊNCIA ARTIFICIAL; APRENDIZAGEM PROFUNDA
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Porto Alegre
- Date published: 2025
- Source:
- Título: Journal of the Brazilian Computer Society
- ISSN: 1678-4804
- Volume/Número/Paginação/Ano: v. 31, n. 1, p. 858-871, 2025
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
BARBOSA, André e SILVEIRA, Igor Cataneo e MAUÁ, Denis Deratani. An empirical analysis of large language models for automated cross-prompt essay trait scoring in Brazilian Portuguese. Journal of the Brazilian Computer Society, v. 31, n. 1, p. 858-871, 2025Tradução . . Disponível em: https://doi.org/10.5753/jbcs.2025.5817. Acesso em: 10 fev. 2026. -
APA
Barbosa, A., Silveira, I. C., & Mauá, D. D. (2025). An empirical analysis of large language models for automated cross-prompt essay trait scoring in Brazilian Portuguese. Journal of the Brazilian Computer Society, 31( 1), 858-871. doi:10.5753/jbcs.2025.5817 -
NLM
Barbosa A, Silveira IC, Mauá DD. An empirical analysis of large language models for automated cross-prompt essay trait scoring in Brazilian Portuguese [Internet]. Journal of the Brazilian Computer Society. 2025 ; 31( 1): 858-871.[citado 2026 fev. 10 ] Available from: https://doi.org/10.5753/jbcs.2025.5817 -
Vancouver
Barbosa A, Silveira IC, Mauá DD. An empirical analysis of large language models for automated cross-prompt essay trait scoring in Brazilian Portuguese [Internet]. Journal of the Brazilian Computer Society. 2025 ; 31( 1): 858-871.[citado 2026 fev. 10 ] Available from: https://doi.org/10.5753/jbcs.2025.5817 - A new benchmark for automatic essay scoring in portuguese
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Informações sobre o DOI: 10.5753/jbcs.2025.5817 (Fonte: oaDOI API)
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