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This arXiv preprint proposes using diffusion language models for few-shot "editing by example," where demonstrated changes, such as an API migration, are applied to several masked regions at once. The authors add a likelihood-ratio reward based on selective masking to favor infills supported by the examples. The reported gains come from benchmarks and software refactoring case studies, so readers should check how well those results transfer to real codebases.
Multi-Target Few-Shot Prompting with Diffusion Language Models
arXiv:2610.22294v1 Announce Type: new Abstract: Few-shot prompting is useful when instructions and rewards are hard to specify. One such application is editing by example, where a demonstrated formatting change or API migration may need to be applied across multiple sites. However, such edits often depend on both left and right context, making generation with causal autoregressive models awkward. We therefore introduce multi-target few-shot prompting with diffusion language models, where examples guide the infilling of multiple masked regions within a shared canvas. To address the known challenge of reliably following demonstrations in few-shot prompting, we further leverage selective masking of the canvas to derive a likelihood-ratio reward that favors example-supported infills. Across few-shot prompting benchmarks and software refactoring case studies, we find that demonstrations improve infilling accuracy, with example guidance yielding further gains in several settings.
Abstract:Few-shot prompting is useful when instructions and rewards are hard to specify. One such application is editing by example, where a demonstrated formatting change or API migration may need to be applied across multiple sites. However, such edits often depend on both left and right context, making generation with causal autoregressive models awkward. We therefore introduce multi-target few-shot prompting with diffusion language models, where examples guide the infilling of multiple masked regions within a shared canvas. To address the known challenge of reliably following demonstrations in few-shot prompting, we further leverage selective masking of the canvas to derive a likelihood-ratio reward that favors example-supported infills. Across few-shot prompting benchmarks and software refactoring case studies, we find that demonstrations improve infilling accuracy, with example guidance yielding further gains in several settings.
Computation and Language (cs.CL)
arXiv:2610.22294 [cs.CL]
(or arXiv:2610.22294v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.22294 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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