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Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning

NeurIPS 2021 · Continual learning › Continual fine-tuning of LLMs · approach: Low-rank and model-merging adaptation · 43 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add CTR's KSM and TSM instead of using Adapter-BERT alone (ablation)CTR with BERT · ASCMF1: 0.4481 -> 0.8362Helped
Add TSM for task-specific protection (ablation)CTR with BERT · ASCMF1: 0.7107 -> 0.8362Helped
Add KSM for cross-task knowledge sharing (ablation)CTR with BERT · ASCMF1: 0.7852 -> 0.8362Helped
Add the task router to KSM (ablation)CTR with BERT · ASCMF1: 0.8155 -> 0.8362Helped
Use transfer routing rather than dynamic routing (ablation)CTR with BERT · ASCMF1: 0.8140 -> 0.8362Helped
Place the CL-plugin within BERT rather than on top (ablation)CTR with BERT · ASCMF1: 0.6390 -> 0.8362Helped
Insert paired CL-plugins rather than only after the first FF layer (ablation)CTR with BERT · ASCMF1: 0.8014 -> 0.8362Helped
Insert paired CL-plugins rather than only after the other two FF layers (ablation)CTR with BERT · ASCMF1: 0.7863 -> 0.8362Helped
Add TSM for task-specific protection (ablation)CTR with BERT · 20NewsMF1: 0.8064 -> 0.9523Helped
Add KSM for knowledge transfer (ablation)CTR with BERT · 20NewsMF1: 0.9521 -> 0.9523Helped
Add the task router to KSM (ablation)CTR with BERT · 20NewsMF1: 0.9469 -> 0.9523Helped
Use transfer routing rather than dynamic routing (ablation)CTR with BERT · 20NewsMF1: 0.9504 -> 0.9523Helped

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