Unlocking Prompt Infilling Capability for Diffusion Language Models

1. 1Apply full-sequence masking to both prompts and responses during supervised fine-tuning
2. 2Extend the current supervised fine-tuning convention to unlock prompt infilling capability
3. 3Evaluate the performance of the model on infilling tasks to measure its effectiveness

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

💡 Extending full-sequence masking during supervised fine-tuning can unlock prompt infilling capability for diffusion language models

💡 Unlock prompt infilling for diffusion language models with full-sequence masking!