AdvCL repurposes adversarial perturbations into geometric control signals for continual learning using Intra-Smooth, Proto-Clip, and Inter-Align modules, reporting gains in performance, robustness, lower forgetting, and stronger transfer.
SAPT : A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models
2 Pith papers cite this work. Polarity classification is still indexing.
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Paraphrased training prompts induce correlated cross-task differences in forgetting and generalization during LLM fine-tuning; superior prompts can be identified via pre-learning task loss and used in a state-adaptive optimization method (SAPO) to improve robustness.
citing papers explorer
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Repurposing Adversarial Perturbations for Continual Learning: From Defense to Active Alignment
AdvCL repurposes adversarial perturbations into geometric control signals for continual learning using Intra-Smooth, Proto-Clip, and Inter-Align modules, reporting gains in performance, robustness, lower forgetting, and stronger transfer.
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Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning
Paraphrased training prompts induce correlated cross-task differences in forgetting and generalization during LLM fine-tuning; superior prompts can be identified via pre-learning task loss and used in a state-adaptive optimization method (SAPO) to improve robustness.