LoRA-DA derives an optimal data-aware LoRA initialization by solving an optimization problem from asymptotic analysis of parameter discrepancy using Fisher-gradient bias and Fisher-information variance terms.
Notably, for LoRA-DA and LoRA-One, we pre-sampled 256 examples to estimate both the gradient and the Fisher information matrix during initialization
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LoRA-DA: Data-Aware Initialization for Low-Rank Adaptation via Asymptotic Analysis
LoRA-DA derives an optimal data-aware LoRA initialization by solving an optimization problem from asymptotic analysis of parameter discrepancy using Fisher-gradient bias and Fisher-information variance terms.