Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic Forgetting
<|reference_start|>Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic Forgetting: We are motivated primarily by the adaptation of text-to-speech synthesis models; however we argue that more generic parameter-efficient fine-tuning (PEFT) is an appropriate framework to do such adaptation. Nevertheless, catastrophic forgetting remains an issue with PEFT, damaging the pre-trained model's inherent capabilities. We demonstrate that existing Bayesian learning techniques can be applied to PEFT to prevent catastrophic forgetting as long as the parameter shift of the fine-tuned layers can be calculated differentiably. In a principled series of experiments on language modeling and speech synthesis tasks, we utilize established Laplace approximations, including diagonal and Kronecker-factored approaches, to regularize PEFT with the low-rank adaptation (LoRA) and compare their performance in pre-training knowledge preservation. Our results demonstrate that catastrophic forgetting can be overcome by our methods without degrading the fine-tuning performance, and using the Kronecker-factored approximation produces a better preservation of the pre-training knowledge than the diagonal ones.<|reference_end|>
@article{chen2024bayesian,
    title={Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic
  Forgetting},
    author={Haolin Chen, Philip N. Garner},
    journal={arXiv preprint arXiv:2402.12220},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12220},
    primaryClass={eess.AS cs.LG}
}