MOSAIC: Masked Objective with Selective Adaptation for In-domain Contrastive Learning

Vera Pavlova; Mohammed Makhlouf

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arXiv · 2510.16797

MOSAIC: Masked Objective with Selective Adaptation for In-domain Contrastive Learning

Vera Pavlova·Mohammed Makhlouf

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Abstract

We introduce MOSAIC (Masked Objective with Selective Adaptation for In-domain Contrastive learning), a multi-stage framework for domain adaptation of text embedding models that incorporates joint domain-specific masked supervision. Our approach addresses the challenges of adapting large-scale general-domain text embedding models to specialized domains. By jointly optimizing masked language modeling (MLM) and contrastive objectives within a unified training pipeline, our method enables effective learning of domain-relevant representations while preserving the robust semantic discrimination properties of the original model. We empirically validate our approach on both high-resource and low-resource domains, achieving improvements up to 13.4% in NDCG@10 (Normalized Discounted Cumulative Gain) over strong general-domain baselines. Comprehensive ablation studies further demonstrate the effectiveness of each component, highlighting the importance of balanced joint supervision and staged adaptation.

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Vera Pavlova, Mohammed Makhlouf. 2026-01-29. MOSAIC: Masked Objective with Selective Adaptation for In-domain Contrastive Learning. https://arxiv.org/abs/2510.16797

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