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arXiv:2607.00171v2 Announce Type: replace Abstract: Text embeddings are standard for semantic similarity tasks, yet their evaluation remains an open challenge. Current benchmarks are static, cover on...
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.
arXiv:2610.23807v1 Announce Type: new Abstract: While deriving text embeddings directly from frozen LLMs has emerged as a promising direction, existing methods focus almost exclusively on English, le...
TRACE is a new framework that reorganizes 168 tasks for text‑vision embedding models into General, Adaptability, and Challenge categories. Experiments on two models show that adding general and adaptation data boosts Adaptability scores by 7.67 and 7.09 points while keeping General performance largely unchanged, yet Challenge performance stays below 5.0 with minimal gains from more data. The study highlights that data adaptation alone cannot overcome weaknesses in multilingual and visual‑document tasks, indicating a need for methodological advances.
arXiv:2606. 13647v1 Announce Type: cross 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.
arXiv:2607. 07669v1 Announce Type: cross Abstract: Large language models increasingly \emph{understand} dialectal English, yet still \emph{produce} only standard, US-leaning English, leaving dialectal generation, the harder half of the problem, largely unaddressed.