The Bode Family of Large Language Models: Investigating the Frontiers of LLMs in Brazilian Portuguese
Year: 2025
Type: article
Abstract: The rapid advancement of Large Language Models (LLMs) has significantly impacted Natural Language Processing, yet their effectiveness remains uneven across languages. Most state-of-the-art models are trained predominantly in English, leading to performance disparities in lower-resource languages such as Brazilian Portuguese (BP). This paper explores fine-tuning strategies for adapting open-weight LLMs to BP, focusing on dataset translation techniques, linguistic adaptation challenges, and parameter-efficient fine-tuning methods, such as LoRA and Q-LoRA. We present a benchmark analysis evaluating multiple fine-tuning approaches across various open models, establishing a guiding framework for future BP-specific adaptations. Our results showcase the importance of specialized fine-tuning in improving cross-lingual transfer and NLP performance in BP, contributing to the broader goal of enhancing multilingual language model accessibility.
Authors: Pedro Henrique Paiola, Gabriel Lino Garcia, João Vitor Mariano Correia, João Renato Ribeiro Manesco, Ana Lara Alves Garcia, João Paulo Papa
Institution: Universidade Estadual Paulista (Unesp)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,390
OpenAlex ID: W4415195230