FiLoRA: Focus-and-Ignore LoRA for Controllable Feature Reliance
This addresses the need for controllable feature reliance in multimodal AI, offering a principled mechanism beyond correlation-driven learning, though it is incremental as it builds on existing LoRA methods.
The paper tackled the problem of controlling how multimodal foundation models rely on specific internal feature groups, introducing FiLoRA to enable explicit control over feature reliance while keeping the task fixed, resulting in consistent and causal shifts in internal computation and improved robustness under spurious feature interventions.
Multimodal foundation models integrate heterogeneous signals across modalities, yet it remains poorly understood how their predictions depend on specific internal feature groups and whether such reliance can be deliberately controlled. Existing studies of shortcut and spurious behavior largely rely on post hoc analyses or feature removal, offering limited insight into whether reliance can be modulated without altering task semantics. We introduce FiLoRA (Focus-and-Ignore LoRA), an instruction-conditioned, parameter-efficient adaptation framework that enables explicit control over internal feature reliance while keeping the predictive objective fixed. FiLoRA decomposes adaptation into feature group-aligned LoRA modules and applies instruction-conditioned gating, allowing natural language instructions to act as computation-level control signals rather than task redefinitions. Across text--image and audio--visual benchmarks, we show that instruction-conditioned gating induces consistent and causal shifts in internal computation, selectively amplifying or suppressing core and spurious feature groups without modifying the label space or training objective. Further analyses demonstrate that FiLoRA yields improved robustness under spurious feature interventions, revealing a principled mechanism to regulate reliance beyond correlation-driven learning.