---
id: 20260305-T0-18
title: "扩散语言模型的记忆提取与采样研究"
title_en: "Memory Extraction in Diffusion Language Models"
url: https://ai.daily.yangsir.net/daily/20260305-T0-18
issue_date: 2026-03-05
publish_date: 2026-03-04T05:00:00.000Z
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2603.02333
---

# 扩散语言模型的记忆提取与采样研究

扩散语言模型（DLMs）与传统自回归模型（ARMs）在记忆数据方面表现不同。研究显示，DLMs在再现训练数据时更难直接提取，但采样过程可能暴露记忆信息。该发现对模型版权和隐私保护有启示，开发者需注意数据清洗。

## English Version

**Memory Extraction in Diffusion Language Models**

Diffusion language models (DLMs) differ from autoregressive models (ARMs) in memory handling. Research shows DLMs struggle with direct memory extraction but may expose memorized information during sampling. This finding has implications for model copyright and privacy, requiring developers to focus on data cleaning.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2603.02333)

**详情页**：https://ai.daily.yangsir.net/daily/20260305-T0-18

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