任务条件流匹配实现多语言文本嵌入的平衡适配
原标题：Task-Conditional Flow Matching for Balanced Multilingual Text Embedding Adaptation
AI 摘要
研究人员提出任务条件流匹配（TCFM）框架，用于多语言文本嵌入模型的适配。该框架针对不同任务采用不同的优化策略，仅在翻译任务上应用流匹配，同时结合教师引导的表示保留和三阶段课程学习。在Indic Massive Text Embedding Benchmark上，TCFM取得了新的最先进结果，提升了多语言任务的嵌入质量。论文接受后，代码库和数据集将公开。
正文节选
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-cl