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English(EN) BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion

新方法通过高效转换和长度外插增强扩散语言模型

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了两种改进扩散语言模型（DLM）的新方法。第一种方法 BYOD 提供了一种参数高效的方式，仅使用 LoRA 适配器即可将现有的自回归模型转换为 DLM，使 Gemma 2 9B 和 Llama 3.1 8B 等模型能够生成具有扩散能力的文本，同时保留其原始知识。第二种方法 RangeSWA 解决了 DLM 中的长度外插问题，通过滑动窗口注意力（sliding-window attention）和动态内容检索（dynamic content retrieval）等技术，减少了长文本生成中的重复和语义漂移。这两种方法都旨在使 DLM 在各种文本生成任务中更加实用和有效。 AI

影响 这些进展可能带来更通用、更强大的文本生成模型，提高效率并改善长篇内容的处理能力。

排序理由 arXiv 上发表了两篇研究论文，详细介绍了扩散语言模型的新方法。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新方法通过高效转换和长度外插增强扩散语言模型

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报道来源 [3]

  1. arXiv cs.CL TIER_1 English(EN) · Ruurd J. A. Kuiper, Robert A. Bagheri ·

    BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion

    arXiv:2610.41574v1 Announce Type: new Abstract: Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible generation, and bidirectional attention, but leading systems rely on costly pretraining or full-model fine-tuning. We presen…

  2. arXiv cs.CL TIER_1 English(EN) · Guangfu Guo, Xiaoqian Lu, Linsey Pang, Weiran Yao, Haolin Chen, Long Cheng, Kunpeng Liu ·

    RangeSWA: Training-Free Length Extrapolation for Continuous Diffusion Language Models

    arXiv:2610.42084v1 Announce Type: new Abstract: Continuous diffusion language models show promising potential in text generation tasks, but are prone to problems such as text repetition, semantic drift, and generation quality degradation when the generation length exceeds the tra…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion

    BYOD converts autoregressive language models into diffusion models via parameter-efficient LoRA adapters, enabling parallel generation with minimal training cost while preserving base-model knowledge.

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