Task-Conditional Flow Matching for Balanced Multilingual Text Embedding Adaptation
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Computer Science > Computation and Language
Title: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.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.05785 [cs.CL] |
| (or arXiv:2608.05785v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.05785
arXiv-issued DOI via DataCite (pending registration)
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