arXiv:2606.13647v1 Announce Type: new Abstract: We introduce SkMTEB, the first comprehensive MTEB-style text embedding benchmark for Slovak, a low-resource West Slavic language, comprising 31 datasets across 7 task types -- nearly 4$\times$ the depth of existing multilingual benchmark coverage for Slovak. Our evaluation of 31 embedding models reveals that large instruction-tuned multilingual models achieve the strongest performance, while existing Slovak-specific models trained for NLU tasks transfer poorly to embedding tasks. To address the need for efficient, locally-deployable Slovak embedd
The proliferation of AI models necessitates robust benchmarks for less-resourced languages, and the release of SkMTEB fulfills this for Slovak, highlighting gaps in current multilingual models.
This development underscores the global effort to make AI universally applicable and the challenges low-resource languages face in achieving parity with high-resource counterparts.
A specific, high-quality benchmark now exists for Slovak text embeddings, enabling targeted development and evaluation of AI models for West Slavic languages, and potentially other low-resource languages.
- · Slovak AI developers
- · NLP researchers in low-resource languages
- · Multilingual AI model providers prioritizing comprehensive coverage
- · General-purpose multilingual models without specific low-resource tuning
- · Slovak-specific NLU models that don't adapt to embedding tasks
SkMTEB directly provides a necessary tool for benchmarking and improving AI tools for the Slovak language.
This will spur the development of more efficient and accurate AI models tailored for Slovak, improving NLP applications and digital inclusion.
It may serve as a template or inspiration for similar comprehensive benchmarks in other low-resource languages, fostering a more equitable global AI landscape.
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Read at arXiv cs.CL