---
id: 20260621-T0-01
title: "Diffusion Language Models：新研究探索扩散模型在语言任务中的潜力"
title_en: "Diffusion Language Models: An Experimental Analysis"
url: https://ai.daily.yangsir.net/daily/20260621-T0-01
issue_date: 2026-06-21
publish_date: 2026-06-20T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2606.19475
---

# Diffusion Language Models：新研究探索扩散模型在语言任务中的潜力

arXiv新论文探讨了扩散语言模型（DLMs）作为自回归生成替代方案的潜力。实验表明，DLMs通过迭代去噪过程进行生成，是LLM的一种新范式。

## English Version

**Diffusion Language Models: An Experimental Analysis**

A new arXiv paper explores Diffusion Language Models (DLMs) as an alternative to autoregressive generation. It analyzes this emerging paradigm through experimental study.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2606.19475)

**详情页**：https://ai.daily.yangsir.net/daily/20260621-T0-01

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*智语观潮 · Daily — https://ai.daily.yangsir.net/llms.txt*