Papers by Rifat Rafiuddin
Context-Conditioned Masked LoRA: Dynamic Rank Routing for Compute-Efficient Parameter-Efficient Fine-Tuning (2026.findings-acl)
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| Challenge: | Large pretrained language models (LMs) are commonly adapted via fine-tuning, but full updates are costly at scale. |
| Approach: | They propose a lightweight router that activates an input-dependent subset of LoRA rank directions and turns it into dynamic rank routing. |
| Outcome: | The proposed method improves accuracy–efficiency Pareto frontier versus static-rank LoRA and adaptive-rank baselines, while preserving memory and reducing overhead. |
MaskLoRA: Low-Rank Subspace–Induced Token Masking for Efficient and Faithful Language Models (2026.findings-eacl)
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| Challenge: | MASKLORA is a plug-and-play masking mechanism that can be used to mask lowrank subspaces. |
| Approach: | They propose a plug-and-play masking mechanism that transforms PEFT's lowrank subspace into a faithful token selector. |
| Outcome: | The proposed masking mechanism matches full-model accuracy while yielding 1.3-2.6 speedups. |