---
id: 20260716-T0-09
title: "TAKE：轨迹感知知识估计方法，将文本数据集压缩至1%仍保持性能"
title_en: "TAKE: Trajectory-Aware Method Compresses Text Datasets to 1% While Retaining Performance"
url: https://ai.daily.yangsir.net/daily/20260716-T0-09
issue_date: 2026-07-16
publish_date: 2026-07-15T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2607.11898
---

# TAKE：轨迹感知知识估计方法，将文本数据集压缩至1%仍保持性能

arXiv新研究提出TAKE（轨迹感知知识估计）框架，用于文本数据集蒸馏。该方法通过分析训练轨迹，将大规模语料库压缩至原来的极小比例（低至1%），大幅降低存储和训练成本，同时在下游任务中保持接近原始数据集的性能。

## English Version

**TAKE: Trajectory-Aware Method Compresses Text Datasets to 1% While Retaining Performance**

A new study introduces TAKE (Trajectory-Aware Knowledge Estimation), a text dataset distillation framework. By analyzing training trajectories, it compresses large corpora to as little as 1% of their original size, significantly reducing storage and training costs while maintaining near-original performance on downstream tasks.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2607.11898)

**详情页**：https://ai.daily.yangsir.net/daily/20260716-T0-09

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