SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models
This work addresses the challenge of enabling large language models to learn continuously without forgetting, which is crucial for real-world deployment, though it appears incremental as it builds on existing parameter-efficient tuning methods.
The paper tackles the problem of catastrophic forgetting and knowledge transfer in continual learning for large language models by proposing SAPT, a shared attention framework that aligns learning and selection modules, achieving superior performance on two benchmarks and scaling effectively across model sizes and architectures.
The continual learning (CL) ability is vital for deploying large language models (LLMs) in the dynamic world. Existing methods devise the learning module to acquire task-specific knowledge with parameter-efficient tuning (PET) block and the selection module to pick out the corresponding one for the testing input, aiming at handling the challenges of catastrophic forgetting and knowledge transfer in CL. However, these methods tend to address only one of the challenges, ignoring the potential of aligning the two modules to effectively address catastrophic forgetting and knowledge transfer simultaneously. To this end, we propose a novel Shared Attention Framework (SAPT), to align the PET learning and selection via the Shared Attentive Learning \& Selection module. Extensive Experiments on two CL benchmarks demonstrate the superiority of SAPT. Moreover, SAPT consistently demonstrates its superiority when we scale it to different model sizes (from 770M to 13B), different model architectures (T5 and LLaMA-2) and unseen tasks.