Evaluating LLMs for Portuguese Sentence Simplification with Linguistic Insights

Arthur Mariano Rocha De Azevedo Scalercio, Elvis A. De Souza, Maria José Bocorny Finatto, Aline Paes


Abstract
Sentence simplification (SS) focuses on adapting sentences to enhance their readability and accessibility. While large language models (LLMs) match task-specific baselines in English SS, their performance in Portuguese remains underexplored. This paper presents a comprehensive performance comparison of 26 state-of-the-art LLMs in Portuguese SS, alongside two simplification models trained explicitly for this task and language. They are evaluated under a one-shot setting across scientific, news, and government datasets. We benchmark the models with our newly introduced Gov-Lang-BR corpus (1,703 complex-simple sentence pairs from Brazilian government agencies) and two established datasets: PorSimplesSent and Museum-PT. Our investigation takes advantage of both automatic metrics and large-scale linguistic analysis to examine the transformations achieved by the LLMs. Furthermore, a qualitative assessment of selected generated outputs provides deeper insights into simplification quality. Our findings reveal that while open-source LLMs have achieved impressive results, closed-source LLMs continue to outperform them in Portuguese SS.
Anthology ID:
2025.acl-long.1193
Volume:
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
24452–24477
Language:
URL:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1193/
DOI:
Bibkey:
Cite (ACL):
Arthur Mariano Rocha De Azevedo Scalercio, Elvis A. De Souza, Maria José Bocorny Finatto, and Aline Paes. 2025. Evaluating LLMs for Portuguese Sentence Simplification with Linguistic Insights. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 24452–24477, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
Evaluating LLMs for Portuguese Sentence Simplification with Linguistic Insights (Scalercio et al., ACL 2025)
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PDF:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1193.pdf