Language Models for Portuguese: A Systematic Mapping Study
Jhessica Silva, Carlos Caetano, Helena Maia, Breno Bernard Nicolau de França, Sandra Avila, Helio Pedrini
Abstract
In recent years, the rapid development of language models has transformed the field of Natural Language Processing through a wide range of applications. However, the development of language models has not progressed uniformly across all languages. In the case of the Portuguese language, there has recently been a growing effort by academia and companies to develop language models and create data resources for Portuguese. These efforts have resulted in the rise of an increasingly diverse ecosystem of language models for Portuguese. However, information on these models remains dispersed in scientific publications, technical reports, model repositories, and project documentation. This survey presents a systematic mapping study of language models developed for Portuguese, providing a comprehensive overview of the current state of the field. We map a total of 46 models, characterizing them by various aspects, including base model, architecture, computational resources, training datasets, licensing, code availability, data, and model weights. Furthermore, we analyzed the evolution and relationships among these models through a phylogenetic perspective, identified current research gaps and opportunities, and discussed future directions for the development of language models for Portuguese.
Create a lesson
Related papers
Monitoring and Discovering Reward Hacking with Internal Representations during LLM Evaluations
Leon Bergen, Usha Bhalla, Andrew Lee et al.
Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation
Daniel P. Jeong, Charles Q. Li, Hossein Hosseiny et al.
Long-Lived Characters, Local Inference: Incremental Memory Maintenance for Game NPCs
Zimu Xu
Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking
Xinshuai Guo, Junjie Wu, Dolly Deng et al.
How Much is a Human Right Worth? ECtHR-NPD: A Benchmark for Predicting Non-Pecuniary Damage Awards
Yanyi Pu, Damian A. Gonzalez-Salzberg, Zheng Yuan et al.
Structured Claim-Level Discourse Representations for Dense Health Narratives
Farnoushsadat Nilizadeh, Elham Pourabbas Vafa, Shirin Nilizadeh et al.