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arXiv Computation and Language By Anton Xue, Alan Li, Rahul Saha

Multi-Target Few-Shot Prompting with Diffusion Language Models

Read the original on arXiv Computation and Language →

The paper introduces multi-target few-shot prompting using diffusion language models, enabling the infilling of multiple masked regions within a shared canvas guided by examples. It addresses challenges in few-shot prompting by selectively masking the canvas to derive a likelihood-ratio reward that favors example-supported infills. Experiments on prompting benchmarks and software refactoring case studies show that demonstrations improve infilling accuracy and provide additional gains in several settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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