扩散二元性是一个问题
Subham Sekhar Sahoo1, Justin Deschenaux2, Aaron Gokaslan1
1Computer and Information Science, Cornell Tech, NYC, USA.
概括
对于离散扩散模型的新方法Duo,提高了训练速度的两倍,并使短步文本生成成为可能. 它通过调整高斯扩散技术来弥合与自回归模型的性能差距.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 统一状态离散扩散模型提供快速的文本生成,但通常被自回归和掩盖扩散模型所超越.
- 现有的离散扩散模型在匹配连续高斯扩散模型的性能方面面临挑战.
研究的目的:
- 为了缩小均状态离散扩散模型和自回归模型之间的性能差距.
- 提高离散扩散模型的训练和采样效率.
主要方法:
- 利用一种洞察力,即均状态扩散过程是从底层的高斯扩散中产生的.
- 引入由高斯过程指导的课程学习策略,以减少差异和加速培训.
- 通过离散一致性蒸调整一致性蒸从连续到离散的设置,以加速采样.
主要成果:
- 课程学习使训练速度翻了一番,提高了零射击困难度,在7个基准中的3个中超过了自动回归模型.
- 离散一致性蒸使扩散语言模型中的少数步骤生成成为可能,将采样速度加快了两倍.
- 杜奥方法有效地将技术从高斯扩散转移到离散设置,提高模型性能.
结论:
- 杜奥方法显著改善了均状态离散扩散模型,使它们与自回归模型竞争.
- 高效的培训和加速的少数步骤采样是扩散语言模型的关键进步.
- 提出的技术为开发更快,更有效的文本生成模型提供了一个有希望的方向.
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