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Experiments show that GUIDE improves robustness under targeted evidence perturbations and enables controllable modulation across diverse multimodal settings, suggesting that multimodal instruction following can extend beyond output control toward regulating how different evidence sources contribute to model predictions.
FiLoRA is introduced, an instruction-conditioned, parameter-efficient adaptation framework that enables controllable modulation of feature reliance while keeping the task and predictive objective fixed and suggests that instruction-conditioned parameter adaptation can serve as a practical mechanism for intervening on internal model behavior.
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