Unlocking Prompt Infilling Capability for Diffusion Language Models
📰 ArXiv cs.AI
Researchers unlock prompt infilling capability for diffusion language models by extending full-sequence masking during supervised fine-tuning
Action Steps
- Apply full-sequence masking to both prompts and responses during supervised fine-tuning
- Extend the current supervised fine-tuning convention to unlock prompt infilling capability
- Evaluate the performance of the model on infilling tasks to measure its effectiveness
Who Needs to Know This
NLP researchers and AI engineers on a team can benefit from this research as it enables more flexible and effective text generation capabilities, and can be applied to various language modeling tasks
Key Insight
💡 Extending full-sequence masking during supervised fine-tuning can unlock prompt infilling capability for diffusion language models
Share This
💡 Unlock prompt infilling for diffusion language models with full-sequence masking!
Key Takeaways
Researchers unlock prompt infilling capability for diffusion language models by extending full-sequence masking during supervised fine-tuning
Full Article
Title: Unlocking Prompt Infilling Capability for Diffusion Language Models
Abstract:
arXiv:2604.03677v1 Announce Type: cross Abstract: Masked diffusion language models (dLMs) generate text through bidirectional denoising, yet this capability remains locked for infilling prompts. This limitation is an artifact of the current supervised finetuning (SFT) convention of applying response-only masking. To unlock this capability, we extend full-sequence masking during SFT, where both prompts and responses are masked jointly. Once unlocked, the model infills masked portions of a prompt
Abstract:
arXiv:2604.03677v1 Announce Type: cross Abstract: Masked diffusion language models (dLMs) generate text through bidirectional denoising, yet this capability remains locked for infilling prompts. This limitation is an artifact of the current supervised finetuning (SFT) convention of applying response-only masking. To unlock this capability, we extend full-sequence masking during SFT, where both prompts and responses are masked jointly. Once unlocked, the model infills masked portions of a prompt
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