文本生成中的扩散模型:一项调查调查
Qiuhua Yi1, Xiangfan Chen1, Chenwei Zhang2
1College of Computer Science and Technology, Zhejiang University of Technology, HangZhou, China.
PeerJ. Computer science
|March 4, 2024
概括
扩散模型,最初用于图像,现在在自然语言生成 (NLG) 中表现出色. 本调查探讨了它们在文本生成中的使用,并将它们与预训练语言模型 (PLM) 进行比较.
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.
背景情况:
- 最初用于图像生成的扩散模型正在自然语言处理 (NLP) 中获得引力.
- 它们能够产生多样化和高质量的文本输出,这使得它们成为一个有希望的研究领域.
研究的目的:
- 提供文本生成中扩散模型的全面调查.
- 将扩散模型与自回归预训练语言模型 (PLM) 进行比较.
- 识别文本生成中扩散模型的挑战和未来研究方向.
主要方法:
- 将文本生成分类为有条件的,不受约束的和多模式的.
- 在各种维度上进行扩散模型和PLM之间的详细比较.
- 分析优势,局限性和潜在的整合策略.
主要成果:
- 扩散模型显示了对多样化和高质量的文本生成的巨大潜力.
- 对比强调了扩散模型与PLM的明显优点和弱点.
- 将PLM集成到扩散模型中被确定为一个有前途的研究途径.
结论:
- 扩散模型为文本生成任务提供了强大的替代方案.
- 需要进一步的研究来应对采样速度等挑战,并探索多模式应用.
- 这项调查是该领域的研究人员和从业人员的参考.
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