AMALIA Technical Report: A Fully Open Source Large Language Model for European Portuguese
📰 ArXiv cs.AI
AMALIA is a fully open source large language model for European Portuguese, prioritizing high-quality data for the language variant
Action Steps
- Collect and preprocess high-quality European Portuguese data
- Use the data to fine-tune a large language model during mid- and post-training stages
- Evaluate the model using native benchmarks to ensure faithful representation of linguistic and cultural nuances
- Release the model and evaluation benchmarks as open source to promote community engagement and improvement
Who Needs to Know This
NLP researchers and engineers working on language models for underrepresented languages can benefit from AMALIA's approach, as it provides a fully open source solution for European Portuguese
Key Insight
💡 Prioritizing high-quality data for underrepresented languages can improve the accuracy and cultural sensitivity of large language models
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🇵🇹 AMALIA: A fully open source large language model for European Portuguese! 🤖
Key Takeaways
AMALIA is a fully open source large language model for European Portuguese, prioritizing high-quality data for the language variant
Full Article
Title: AMALIA Technical Report: A Fully Open Source Large Language Model for European Portuguese
Abstract:
arXiv:2603.26511v1 Announce Type: cross Abstract: Despite rapid progress in open large language models (LLMs), European Portuguese (pt-PT) remains underrepresented in both training data and native evaluation, with machine-translated benchmarks likely missing the variant's linguistic and cultural nuances. We introduce AMALIA, a fully open LLM that prioritizes pt-PT by using more high-quality pt-PT data during both the mid- and post-training stages. To evaluate pt-PT more faithfully, we release a
Abstract:
arXiv:2603.26511v1 Announce Type: cross Abstract: Despite rapid progress in open large language models (LLMs), European Portuguese (pt-PT) remains underrepresented in both training data and native evaluation, with machine-translated benchmarks likely missing the variant's linguistic and cultural nuances. We introduce AMALIA, a fully open LLM that prioritizes pt-PT by using more high-quality pt-PT data during both the mid- and post-training stages. To evaluate pt-PT more faithfully, we release a
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