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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:2609.38099v1 Announce Type: cross Abstract: Turning decoder-only large language models (LLMs) into strong dense retrievers typically requires some form of retriever training. In this paper, we...
arXiv:2610.02875v1 Announce Type: cross Abstract: Multilingual encoders can exhibit reduced retrieval effectiveness when queries and relevant documents differ in language, despite strong same-languag...
arXiv:2608.21714v1 Announce Type: new Abstract: Recent page-image retrievers such as ColPali have improved retrieval over visually rich documents, yet little is known about how they behave in cross-l...
SEA-CLIP-Tiny is a compact multilingual text‑vision embedding model designed for Southeast Asian languages, containing fewer than 50 million parameters. It adapts a CLIP‑KD framework with region‑specific data curation and multilingual teacher guidance. Across seven languages, it outperforms other student models, achieving R@1 = 12.9%, R@5 = 31.5%, and R@10 = 42.2%, and surpasses MobileCLIP2 by 12.1 points in R@10 while using 38.4% fewer parameters and lower CPU latency.
With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for global information access. MLIR enables users to retrieve semantically relevant documents from multilingual text collections using a single-language query.