arXiv Machine Learning By Reza Rahimi Azghan, Gautham Krishna Gudur, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh

Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning

Read the original on arXiv Machine Learning →

arXiv:2607. 23837v1 Announce Type: new Abstract: Large language models generalize well to individual tasks but lack an inherent mechanism for learning them sequentially, leading to catastrophic forgetting.

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arXiv Machine Learning
Aug 28

Unifying Detection and Adaptation in Task-Free Continual Learning

The paper introduces FiUni, a Fisher-guided unified framework that performs batch-level task detection and parameter-efficient continual adaptation for large language models. By exploiting orthogonality in the Fisher information matrix’s Kronecker-Factored Approximate Curvature subspaces, FiUni constructs frozen subspaces to guide low-rank adaptation and matches incoming batch windows to historical subspaces. This approach allows the model to decide whether to reuse, expand, or create new subspaces, balancing knowledge sharing and task isolation while achieving competitive performance with fewer trainable parameters.

By Dezheng Han, Anbang Zhang, Zhihao Zhu, Shuaishuai Guo
arXiv AI
2d ago

Local Support Learning

arXiv:2610.02126v1 Announce Type: cross Abstract: We explore catastrophic forgetting in the context of large pre-trained models. By considering forgetting as a geometric problem in the input space of...

By Assaf Ben-Kish, Akarsh Kumar, James Glass, Raja Giryes