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DELA framework enables controllable editing for diffusion language models

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced DELA, a novel framework designed to enhance controllable editing capabilities for diffusion language models (DLMs). DELA operates as a plug-in, allowing for explicit draft refinement through actions like insertion, deletion, and remasking, while keeping the core DLM frozen. This approach separates high-level edit control from token-level generation, addressing computational inefficiencies by employing a two-phase decoding process that optimizes KV-cache reuse during drafting and allows for targeted non-monotonic edits in a subsequent refinement phase. Experiments demonstrate that DELA can improve performance on tasks such as mathematical reasoning and code generation by up to 5.5 percentage points. AI

IMPACT Enhances control over text generation in diffusion models, potentially improving performance in tasks like code generation and mathematical reasoning.

RANK_REASON The cluster contains a research paper detailing a new framework for diffusion language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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DELA framework enables controllable editing for diffusion language models

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The cluster contains a research paper detailing a new framework for diffusion language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xinsong Feng, Chenan Wang, Leshu Tang, Johnny Liu, Dong Li, Da Li, Haipeng Chen ·

    DELA: Learning Controllable Edits for Diffusion Language Models

    arXiv:2610.32096v1 Announce Type: new Abstract: Diffusion language models (DLMs) support bidirectional denoising and iterative refinement, but generation remains largely controlled by token-level masking schedules. This leaves draft-level refinement implicit and limits explicit c…

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