方法对比

	多语言扩散模型	多语言 Transformer
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2020–2023	2019–2020
提出者	Ho, J., Jain, A., & Abbeel, P. (diffusion foundation); multilingual NLP extensions by various authors (2022–2024)	Devlin et al. (mBERT); Conneau et al. (XLM-R)
类型	Generative model (denoising diffusion process, multilingual extension)	Pre-trained cross-lingual language model
开创性文献	Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186. Association for Computational Linguistics.
别名	Multilingual DiffuSeq, Cross-lingual Diffusion Model, Multilingual DDPM, Multilingual Denoising Diffusion	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
相关	5	4
摘要	A Multilingual Diffusion Model adapts the denoising diffusion probabilistic framework to work across multiple languages, enabling cross-lingual text generation, translation, and language-agnostic content synthesis. By conditioning on multilingual representations, the diffusion process learns a shared latent space that spans linguistic boundaries, producing high-quality outputs for low- and high-resource languages alike.	A multilingual transformer is a pre-trained language model built on the transformer architecture and trained jointly on text from dozens to over one hundred languages. Models such as mBERT and XLM-RoBERTa learn shared cross-lingual representations, enabling zero-shot or few-shot transfer: a model fine-tuned on English data can often be applied directly to French, German, Arabic, or Chinese without language-specific labels.