Unverified50% confidenceFactExact time
The decoding order of masked diffusion language models (dLLMs) has a significant impact on generation quality.
1
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50%
Confidence
Long-term
Relevance
10/30/2026
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Related Claims
UnverifiedContinuous diffusion language models (dLLMs) are a class of models that apply diffusion models to text generation, generating text in a continuous latent space through a denoising process, and their latent-space-to-text mapping is learned rather than random.76% similarUnverifiedDiffusion language models (DLM) generate entire passages of text through bidirectional attention and iterative denoising, and control information can be introduced from any position in the sequence.75% similarUnverifiedCommon dLLM decoders typically use a single fixed revealing rule throughout generation.75% similarUnverified近年来基于LLM的APR方法（如AlphaRepair、ChatRepair）因语言模型天然理解代码语义而展现出显著优势74% similarUnverifiedFor encoder-decoder models, direct bilingual fine-tuning achieves the strongest performance on the raw WER metric.73% similar
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