Task-Conditional Flow Matching for Balanced Multilingual Text Embedding Adaptation

Tirth Bhatt, Naren Kumar S, Mayank Singh

Task-Conditional Flow Matching for Balanced Multilingual Text Embedding Adaptation: 10 upvotes on Hugging Face Daily Papers, #30 of 30 papers on 2026-08-07. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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