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    <abstract>The choice between large-scale, multilingual, foundation models and specialized monolingual models for languages like Brazilian Portuguese (PT-BR) presents a complex trade-off between generalization and specialization. This paper investigates this trade-off through an empirical study across a diverse suite of tasks. We evaluate multiple families of language models under both linear probing and fine-tuning regimes. We find that monolingual encoders exhibit greater “adaptation plasticity” during fine-tuning, improving on both classification and semantic similarity, where global (multilingual) models degrade. However, this plasticity comes at a cost: our tokenization analysis suggests that monolingual models struggle with foreign terms, whereas modern multilingual tokenizers show surprising morphological competence, challenging a long-standing assumption in the field. We conclude that the optimal model choice is a task-dependent trade-off between vocabulary coverage and adaptation flexibility.</abstract>
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%0 Conference Proceedings
%T Global vs. Local Sentence Embeddings for Brazilian Portuguese: Revisiting Monolingual Models in the Age of Foundation Models
%A Peixoto, Matheus
%A Silva, Guilherme
%A Figueredo, Giacomo
%A Silva, Pedro
%A Luz, Eduardo J. S.
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F peixoto-etal-2026-global
%X The choice between large-scale, multilingual, foundation models and specialized monolingual models for languages like Brazilian Portuguese (PT-BR) presents a complex trade-off between generalization and specialization. This paper investigates this trade-off through an empirical study across a diverse suite of tasks. We evaluate multiple families of language models under both linear probing and fine-tuning regimes. We find that monolingual encoders exhibit greater “adaptation plasticity” during fine-tuning, improving on both classification and semantic similarity, where global (multilingual) models degrade. However, this plasticity comes at a cost: our tokenization analysis suggests that monolingual models struggle with foreign terms, whereas modern multilingual tokenizers show surprising morphological competence, challenging a long-standing assumption in the field. We conclude that the optimal model choice is a task-dependent trade-off between vocabulary coverage and adaptation flexibility.
%U https://aclanthology.org/2026.propor-1.52/
%P 529-539

Markdown (Informal)

[Global vs. Local Sentence Embeddings for Brazilian Portuguese: Revisiting Monolingual Models in the Age of Foundation Models](https://aclanthology.org/2026.propor-1.52/) (Peixoto et al., PROPOR 2026)

• Global vs. Local Sentence Embeddings for Brazilian Portuguese: Revisiting Monolingual Models in the Age of Foundation Models (Peixoto et al., PROPOR 2026)

ACL

• Matheus Peixoto, Guilherme Silva, Giacomo Figueredo, Pedro Silva, and Eduardo J. S. Luz. 2026. Global vs. Local Sentence Embeddings for Brazilian Portuguese: Revisiting Monolingual Models in the Age of Foundation Models. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1, pages 529–539, Salvador, Brazil. Association for Computational Linguistics.