arXiv AI By Kamil Faber, Katarzyna Filus, {\L}ukasz Pawela

Sparse Autoencoders Can Induce Local Geometry for Mitigating Catastrophic Forgetting

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arXiv AI
3d ago

Drift-adaptive Representation Preservation for Domain-Incremental Learning

The paper proposes a drift‑adaptive method for Domain‑Incremental Learning that tracks representation drift for each past domain‑class pair. By measuring how much a concept’s current representation deviates from a fixed anchor, the method assigns stronger preservation to high‑drift pairs and down‑weights highly drifted domains during prototype construction. Experiments on DomainNet, Office‑Home, CORe50, and CDDB‑Hard show that this approach maintains a strong accuracy‑forgetting balance while using less training memory and the smallest storage compared to other methods.

By Hyunmin Kong, Gyutae Oh, Jitae Shin
arXiv Machine Learning
Oct 5

Fisher-Guided Submodular Data Selection for Continual Pre-Training of Large Language Models

The paper introduces a Fisher-guided submodular data selection method for continual pre‑training of large language models, addressing the forgetting problem that arises when a target‑domain corpus overwrites pretrained knowledge. By decomposing candidate gradients into anchor and frontier components and optimizing a log‑determinant submodular objective, the method selects data that both improves target‑domain performance and limits forgetting. Experiments on TinyLlama‑1.1B and Llama‑3.1‑8B show that the selector achieves superior adaptation and forgetting control while using ten times fewer tokens than traditional replay strategies.

By Zhenghao Zhao, Gaowen Liu, Zhiling Lan, Yan Yan