@inproceedings{kuiper-etal-2025-lad,
    title = "{LAD}: {L}o{RA}-Adapted Diffusion",
    author = "Kuiper, Ruurd Jan Anthonius  and
      de Groot, Lars  and
      van Es, Bram  and
      van Smeden, Maarten  and
      Bagheri, Ayoub",
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-demos.8/",
    pages = "97--110",
    ISBN = "979-8-89176-334-0",
    abstract = "Autoregressive models dominate text generation but suffer from left-to-right decoding constraints that limit efficiency and bidirectional reasoning. Diffusion-based models offer a flexible alternative but face challenges in adapting to discrete text efficiently. We propose LAD (LoRA-Adapted Diffusion), a framework for non-autoregressive generation that adapts LLaMA models for iterative, bidirectional sequence refinement using LoRA adapters. LAD employs a structural denoising objective combining masking with text perturbations (swaps, duplications and span shifts), enabling full sequence editing during generation. We aim to demonstrate that LAD could be a viable and efficient alternative to training diffusion models from scratch, by providing both validation results as well as two interactive demos directly available online:https://ruurdkuiper.github.io/tini-lad/https://huggingface.co/spaces/Ruurd/tini-ladInference and training code:https://github.com/RuurdKuiper/lad-code"
}