TCFM: New Framework for Multilingual Text Embedding Adaptation via Flow Matching
LingoIITGN · hf · 2026-08-07
Current multilingual text embedding models often use a single training objective across diverse tasks, ignoring the need for fundamentally different optimization strategies. To address this, the authors introduce the TCFM (Task-Conditional Flow Matching) framework.
- Core Mechanism: It selectively applies Flow Matching to translation tasks while optimizing retrieval and classification tasks with objectives better aligned to their learning dynamics.
- Stable Adaptation: TCFM combines teacher-guided representation preservation with a three-stage curriculum.
- Performance: It establishes a new state-of-the-art on the Indic Massive Text Embedding Benchmark, consistently improving embedding quality across multilingual tasks and generalizing well across model families.
The codebase and datasets will be publicly released upon paper acceptance.
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