针对多语言文本嵌入，按任务类型选择性优化，提升跨任务性能。
Task-Conditional Flow Matching for Balanced Multilingual Text Embedding Adaptation
- 按任务特性分别采用流匹配或更适配的目标函数
- 在印地语大规模文本嵌入基准上达到新最优表现
- 适合需要稳定多任务适应的多语言模型研究者
多语言文本嵌入模型通常使用单一训练目标适应多种任务，但不同任务本质需要不同的优化策略。本文提出任务条件流匹配（TCFM）框架，对翻译任务选择性应用流匹配，而对检索、分类和成对分类任务则采用更契合其学习动态的目标函数。TCFM结合教师指导的表征保留与三阶段课程学习，实现稳定适应。在Indic Massive Text Embedding Benchmark上评估，TCFM在多样化多语言任务中持续提升嵌入质量，并在不同嵌入模型族间实现良好泛化，建立新基准。论文接受后将公开代码与数据集。
原文摘要 · Abstract (English)
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.
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