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Author

Fyodor Minkin

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Preprint Jul 2026

GigaAM Multilingual: Foundation Model for Underrepresented Languages

This work addresses the challenge of building robust foundation models for underrepresented Central Asian languages by presenting GigaAM Multilingual, a Conformer encoder pre-trained on 2M hours of audio using a HuBERT-style objective, and introduces a cluster-level data balancing strategy during pre-training and a domain-aware sampling method during fine-tuning to mitigate head-language dominance.

Andrei Kuzmenko, A. Maximenko, Aleksandr Kutsakov et al. · 0 citations
Preprint Aug 2026

Giga-Embeddings: Mixture-of-Experts Encoders for High-Throughput Text Embeddings

This work introduces Giga-Embeddings, a family of text embedding models designed to combine strong retrieval quality with efficient serving, and trains the compact model using a dimension-agnostic objective that aligns teacher and student similarity distributions.

Egor Kolodin, Egor Krasnoperov, Evgeniy Kosarev et al. · 0 citations