In-Place Instruction Following in Diffusion Language Models

Abstract:Diffusion Large Language Models (dLLMs) generate text via bidirectional iterative denoising, naturally supporting user-specified constraints anchored at arbitrary output positions, a paradigm known as In-place Prompting (IPP). We formalize this as the In-place Instruction Following (IIF) task and construct IIF-Bench, a hierarchical benchmark spanning literal, style, and discourse-function constraints, paired with a rubric-based local-global evaluation protocol. An inference-time attention-bias probe suggests that vanilla dLLMs often under-prioritize constraint spans during denoising. We then propose GRAFT, an IPP-oriented post-training framework combining constraint-aware SFT and preference optimization. On four representative dLLMs, GRAFT raises the average IIF score from 57.75 to 73.10 (+15.35 points), with absolute gains of 15.91 and 15.57 points on literal and discourse-function constraints, while preserving general generation ability.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.07160 [cs.CL]

(or arXiv:2609.07160v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.07160 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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