arXiv:2503.05500v3 Announce Type: replace Abstract: General-purpose multilingual vector representations, used in retrieval, regression and classification, are traditionally obtained from bidirectional encoder models. Despite their wide applicability, encoders have been recently overshadowed by advances in generative decoder-only models. However, many innovations driving this progress are not inherently tied to decoders. In this paper, we revisit the development of multilingual encoders through the lens of these advances, and introduce EuroBERT, a family of multilingual encoders covering Europe
The development of EuroBERT reflects a growing trend towards localized and specialized AI models, driven by geopolitical considerations and the increasing maturity of foundational AI research beyond generic large models.
This development indicates a strategic move towards linguistic sovereignty in AI for European languages, potentially reducing reliance on models primarily trained on English or mixed global datasets.
The availability of EuroBERT could lead to more accurate and culturally nuanced AI applications within Europe, while also potentially fragmenting the global AI model landscape.
- · European AI developers
- · European language users
- · European startups
- · Monopolistic global AI model providers
- · English-centric AI applications
EuroBERT enables improved performance for AI applications tailored to European languages.
This could foster greater innovation in European AI sectors and potentially accelerate the adoption of AI in public services and enterprises across Europe.
The success of EuroBERT might inspire similar localized efforts in other linguistic and cultural blocs, leading to a more diverse and fragmented global AI ecosystem.
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Read at arXiv cs.CL