Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic Forgetting

• URL: http://arxiv.org/abs/2402.12220v2
• Date: Mon, 16 Sep 2024 19:31:47 GMT
• Title: Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic Forgetting
• Authors: Haolin Chen, Philip N. Garner,
• Abstract summary: We show that catastrophic forgetting can be overcome by our methods without degrading the fine-tuning performance. Our results demonstrate that using the Kronecker-factored approximation produces a better preservation of the pre-training knowledge than the diagonal ones.
• Score: 10.559392015748989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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