FiLoRA: Focus-and-Ignore LoRA for Controllable Feature Reliance

FiLoRA is an instruction‑conditioned, parameter‑efficient adaptation framework that allows controllable modulation of feature reliance in multimodal foundation models. It decomposes adaptation into feature‑aligned low‑rank modules and uses instruction‑conditioned gating so that natural language instructions can act as computation‑level control signals over internal representations. Experiments on classification and generative multimodal tasks show that FiLoRA consistently and interpretable shifts feature reliance, amplifying or suppressing feature groups per instruction without changing task semantics.

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• llms
• fine-tuning
• multimodal