Introducing Bode: A Fine-Tuned Large Language Model for Portuguese
  Prompt-Based Task
Gabriel Lino Garcia Pedro Henrique Paiola Luis Henrique Morelli Giovani Candido Arnaldo Cândido Júnior Danilo Samuel Jodas Luis C. S. Afonso Ivan Rizzo Guilherme Bruno Elias Penteado João Paulo Papa
Large Language Models (LLMs) are increasingly bringing advances to Natural
Language Processing. However, low-resource languages, those lacking extensive
prominence in datasets for various NLP tasks, or where existing datasets are
not as substantial, such as Portuguese, already obtain several benefits from
LLMs, but not to the same extent. LLMs trained on multilingual datasets
normally struggle to respond to prompts in Portuguese satisfactorily,
presenting, for example, code switching in their responses. This work proposes
a fine-tuned LLaMA 2-based model for Portuguese prompts named Bode in two
versions: 7B and 13B. We evaluate the performance of this model in
classification tasks using the zero-shot approach with in-context learning, and
compare it with other LLMs. Our main contribution is to bring an LLM with
satisfactory results in the Portuguese language, as well as to provide a model
that is free for research or commercial purposes.