Academic

Unlocking Prompt Infilling Capability for Diffusion Language Models

arXiv:2604.03677v1 Announce Type: new 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 template conditioned on few-shot examples. We show that such model-infilled prompts match or surpass manually designed templates, transfer effectively across models, and are complementary to existing prompt optimization methods. Our results suggest that training practices, not architectural limitations, are the primary bottleneck preventing masked diffusion language models from infilling effective prompts

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Yoshinari Fujinuma, Keisuke Sakaguchi · · 1 min read · 5 views
#cs.CL #cs.AI

arXiv:2604.03677v1 Announce Type: new 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 template conditioned on few-shot examples. We show that such model-infilled prompts match or surpass manually designed templates, transfer effectively across models, and are complementary to existing prompt optimization methods. Our results suggest that training practices, not architectural limitations, are the primary bottleneck preventing masked diffusion language models from infilling effective prompts

Executive Summary

This article presents a novel approach to unlocking the prompt infilling capability of masked diffusion language models (dLMs). The authors extend the conventional supervised finetuning convention by applying full-sequence masking, where both prompts and responses are masked jointly. The proposed method enables the model to infill masked portions of a prompt template conditioned on few-shot examples. The results demonstrate that model-infilled prompts match or surpass manually designed templates, transfer effectively across models, and are complementary to existing prompt optimization methods. The study suggests that training practices, rather than architectural limitations, are the primary bottleneck preventing dLMs from infilling effective prompts. This breakthrough has significant implications for natural language processing (NLP) and language model research.

Key Points

  • ▸ The proposed method unlocks the prompt infilling capability of dLMs by extending full-sequence masking during supervised finetuning
  • ▸ Model-infilled prompts match or surpass manually designed templates
  • ▸ The method enables effective transfer across models and complements existing prompt optimization methods

Merits

Strength in NLP Applications

The proposed method has the potential to revolutionize NLP applications, such as text summarization, question answering, and dialogue systems, by enabling the use of more effective and diverse prompts.

Demerits

Limited Generalizability

The study's findings may not generalize to other language models or tasks, and further research is needed to determine the method's effectiveness in more diverse scenarios.

Expert Commentary

The proposed method represents a significant breakthrough in the field of NLP and language model research. By unlocking the prompt infilling capability of dLMs, the authors have opened up new possibilities for the development of more effective and diverse prompts. The study's findings have important implications for the field, and further research is needed to fully explore the method's potential. In particular, it will be essential to investigate the method's effectiveness in more diverse scenarios and to develop strategies for scaling the approach to larger language models and more complex tasks.

Recommendations

  • ✓ Further research is needed to investigate the method's effectiveness in more diverse scenarios and to develop strategies for scaling the approach to larger language models and more complex tasks.
  • ✓ The proposed method should be integrated into existing NLP applications to evaluate its practical impact and to identify areas for improvement.

Sources

Original: arXiv - cs.CL

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