A paper list about diffusion models for natural language processing.

Update News:

• Nov. 9, 2022, Add 3 pre-print papers.
• Oct. 18, 2022, Add papers that attempt to apply diffusion models for NLP from scratch.

1. Diffusion-LM Improves Controllable Text Generation. Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B. Hashimoto. In NeuralPS 2022. [pdf] [code]
2. DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models. Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, LingPeng Kong. ICLR 2023. [pdf] [code]
3. Latent Diffusion Energy-Based Model for Interpretable Text Modeling. Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu. [pdf] [code]
4. Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning. Ting Chen, Ruixiang Zhang, Geoffrey Hinton. [pdf] [code]
5. Structured Denoising Diffusion Models in Discrete State-Spaces. Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg. [pdf]
6. Composable Text Controls in Latent Space with ODEs. Guangyi Liu, Zeyu Feng, Yuan Gao, Zichao Yang, Xiaodan Liang, Junwei Bao, Xiaodong He, Shuguang Cui, Zhen Li, Zhiting Hu. [pdf]
7. DiffusER: Discrete Diffusion via Edit-based Reconstruction. Machel Reid, Vincent J. Hellendoorn, Graham Neubig. ICLR 2023. [pdf]
8. Self-conditioned Embedding Diffusion for Text Generation. Robin Strudel, Corentin Tallec, Florent Altché, Yilun Du, Yaroslav Ganin, Arthur Mensch, Will Grathwohl, Nikolay Savinov, Sander Dieleman, Laurent Sifre, Rémi Leblond. [pdf]
9. DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models. Zhengfu He, Tianxiang Sun, Kuanning Wang, Xuanjing Huang, Xipeng Qiu. [pdf] [code]

1. DIFFUSION MODELS: A COMPREHENSIVE SURVEY OFMETHODS AND APPLICATIONS. Ling Yang, Zhilong Zhang, Shenda Hong, Wentao Zhang, Bin Cui. [pdf]

1. YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy