@inproceedings{rafiuddin-abdullah-2026-context,
    title = "Context-Conditioned Masked {L}o{RA}: Dynamic Rank Routing for Compute-Efficient Parameter-Efficient Fine-Tuning",
    author = "Rafiuddin, Rifat  and
      Abdullah, Rafae",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1329/",
    pages = "26670--26689",
    ISBN = "979-8-89176-395-1",
    abstract = "Parameter-efficient fine-tuning methods such as LoRA reduce trainable parameters, but still apply dense low-rank updates per token, leaving adaptation compute largely fixed once rank is set. We propose Context-Conditioned Masked LoRA (CCM-LoRA), which learns a lightweight router that activates an input-dependent subset of LoRA rank directions, turning LoRA into dynamic rank routing and enabling contextual sparsity in fine-tuning and inference. CCM-LoRA is trained with a budget-constrained objective that targets an expected effective rank (or FLOPs) while regularizing routing to avoid degenerate always-on/off masks. Across public NLU and multilingual benchmarks, CCM-LoRA improves the accuracy{--}efficiency Pareto frontier versus static-rank LoRA and adaptive-rank baselines, matching or improving task performance at lower inference-time effective rank. We also provide a reproducible profiling protocol and analyses of rank usage, router overhead, and robustness under domain and language shift."
}