FiUni framework for continual learning
Unifying Detection and Adaptation in Task-Free Continual Learning
FiUni enables realistic task-free continual learning without explicit task boundaries, reducing computational costs while maintaining performance.
FiUni framework addresses catastrophic forgetting in large language models through batch-level task detection and parameter-efficient adaptation. The method utilizes Fisher information matrix orthogonality to determine task similarity from small downstream samples. FiUni constructs frozen subspaces to guide low-rank adaptation while matching Fisher principal subspaces across batch windows. Experiments show competitive performance against advanced task-aware continual learning methods with fewer trainable parameters.
Unifying Detection and Adaptation in Task-Free Continual Learning
To mitigate catastrophic forgetting in downstream continual learning (CL) for large language models (LLMs), existing methods typically constrain parameter updates or introduce task-specific adaptation modules. However, these methods often rely on explicit task boundaries during training, limiting their applicability to realistic task-free scenarios. In this paper, we propose a \textbf{Fi}sher-guided \textbf{uni}fied (\textbf{FiUni}) framework for batch-level task detection and parameter-efficient continual adaptation. FiUni is motivated by a key observation about the Fisher information matrix (FIM) of pre-trained models: the orthogonality among the principal subspaces of its Kronecker-Factored Approximate Curvature (K-FAC) approximation, estimated from a small number of downstream task samples, can reflect the similarity between different tasks. Based on this observation, FiUni constructs FIM-derived frozen subspaces to guide low-rank adaptation (LoRA), while matching the Fisher principal subspace of each incoming batch window with historical subspaces. This enables FiUni to adaptively determine whether to reuse existing knowledge, expand a related subspace, or create a new subspace, dynamically balancing knowledge sharing and task isolation. Experiments show that FiUni can effectively infer latent batch-level task affiliations and achieve competitive performance against advanced task-aware CL methods with fewer trainable parameters.
