An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning
Abstract: Incrementally fine-tuning foundational models on new tasks or domains is now the de facto approach in NLP.
A known pitfall of this approach is the \emph{catastrophic forgetting} of prior knowledge that happens during fine-tuning.
A common approach to alleviate such forgetting is to rehearse samples from prior tasks during fine-tuning.
Several existing works assume a fixed memory buffer to store prior task examples, while relying on inferences (forward passes) with the model at hand for choosing examples for rehearsal from the buffer.
However, given the increasing computational cost of model inference, and decreasing cost of data storage, we focus on the setting to rehearse samples with a fixed computational budget instead of a fixed memory budget.
We propose a sampling scheme, \texttt{\bf mix-cd}, that prioritizes rehearsal of ``collateral damage'' samples, which are samples predicted correctly by the prior model but forgotten by the incrementally tuned one.
The crux of our scheme is a procedure to efficiently estimate the density of collateral damage samples without incurring additional model inferences.
Our approach is computationally efficient, easy to implement, and outperforms several leading continual learning methods in compute constrained settings.
Paper Type: Long
Research Area: Machine Learning for NLP
Research Area Keywords: NLP in resource-constrained settings, multi-task learning, continual learning
Contribution Types: Approaches low compute settings-efficiency
Languages Studied: English
Submission Number: 1652
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