# TCFM：面向多语言文本嵌入平衡适配的任务条件流匹配

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-06 08:00
- AIHOT 分数：38
- AIHOT 链接：https://aihot.virxact.com/items/cmsirrsoq1qosronkz6mkzlti
- 原文链接：https://arxiv.org/abs/2608.05785

## AI 摘要

研究者提出任务条件流匹配（TCFM）框架，针对不同任务采用差异化训练目标：翻译任务使用流匹配，检索、分类等任务则用更契合其学习动态的目标，并结合教师引导与三阶段课程实现稳定适配。在 Indic Massive Text Embedding Benchmark 上，TCFM 取得新 SOTA，并泛化至不同嵌入模型家族，代码与数据集将在论文接收后公开。

## 正文

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.