A Bayesian Interpretation of Adaptive Low-Rank Adaptation
This work provides a faster, theoretically grounded alternative to existing adaptive low-rank adaptation methods for efficient fine-tuning of large models, though it appears incremental in nature.
The paper tackles the problem of adaptive parameter budget allocation in low-rank adaptation by proposing a Bayesian approach using signal-to-noise ratio and IVON optimizer, which matches or surpasses sensitivity-based methods while being faster than AdaLoRA with Adam. The result includes theoretical insights linking sensitivity and SNR metrics, and identifies parameter magnitude as the key importance indicator.
Motivated by the sensitivity-based importance score of the adaptive low-rank adaptation (AdaLoRA), we utilize more theoretically supported metrics, including the signal-to-noise ratio (SNR), along with the Improved Variational Online Newton (IVON) optimizer, for adaptive parameter budget allocation. The resulting Bayesian counterpart not only has matched or surpassed the performance of using the sensitivity-based importance metric but is also a faster alternative to AdaLoRA with Adam. Our theoretical analysis reveals a significant connection between the two metrics, providing a Bayesian perspective on the efficacy of sensitivity as an importance score. Furthermore, our findings suggest that the magnitude, rather than the variance, is the primary indicator of the importance of parameters.