arXiv:2607.04071v1 Announce Type: cross Abstract: Portuguese remains underrepresented in text embedding evaluation, despite being one of the most widely spoken languages in the world. As a result, embedding models are often selected based on English or multilingual metrics, while their effectiveness in Portuguese remains unclear. We present MTEB-PT, a Portuguese benchmark constructed from a subset of MMTEB, comprising 14 existing datasets across Semantic Textual Similarity (STS), classification, retrieval, and reranking. We use this benchmark to evaluate 17 open- and closed-source embedding mo
The proliferation of AI models has highlighted the limitations of English-centric evaluation, and the global push for AI adoption necessitates better performance in other widely spoken languages.
Improved language model benchmarks for non-English languages like Portuguese are crucial for developing more equitable and effective global AI applications, reducing linguistic bias, and fostering localized AI innovation.
The availability of MTEB-PT provides a standardized, robust evaluation framework for Portuguese text encoders, enabling developers to select and improve models specifically for this language, rather than relying on generalized multilingual metrics.
- · Portuguese-speaking AI developers
- · Companies targeting Portuguese markets with AI products
- · Open-source AI communities
- · Researchers in multilingual NLP
- · AI models exhibiting strong English but weak Portuguese performance
- · Monolingual English NLP development paradigms
More accurate and reliable AI applications will emerge for Portuguese-speaking users.
Increased investment and development of AI tailored for other underrepresented languages, following the precedent set by MTEB-PT.
Local economies and cultures in Portuguese-speaking nations could experience greater digital inclusion and innovation, fostering localized AI ecosystems.
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Read at arXiv cs.AI