Task-Conditional Flow Matching for Balanced Multilingual Text Embedding Adaptation

Abstract:Multilingual text embedding models are commonly adapted using a single training objective across diverse tasks, despite different tasks requiring fundamentally different optimization strategies. We introduce Task-Conditional Flow Matching (TCFM), a multilingual embedding adaptation framework that selectively applies Flow Matching to translation tasks while optimizing retrieval, classification, and pair-classification tasks with objectives better aligned to their learning dynamics. TCFM further combines teacher-guided representation preservation with a three-stage curriculum to enable stable adaptation. Evaluated on the Indic Massive Text Embedding Benchmark, TCFM establishes a new state-of-the-art, consistently improving embedding quality across a diverse set of multilingual tasks and generalizing across embedding model families. We will publicly release the codebase and datasets upon acceptance of the paper.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.05785 [cs.CL]

(or arXiv:2608.05785v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.05785 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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