Continual Learning

Continual learning (also called lifelong learning) enables deployed language models to learn from new data streams while retaining previously acquired knowledge. Approaches include regularization-based methods that penalize changes to old knowledge, replay-based methods that mix old and new data, and architectural methods that dedicate separate parameters to distinct domains.

Key papers

• Zhang et al. (2023) — Survey comparing continual learning with knowledge editing and explicit methods, examining catastrophic forgetting and scalability trade-offs

Related topics

• Large Language Models
• Knowledge Editing
• Knowledge Retrieval

Notes

Continual learning enables larger-scale knowledge acquisition than knowledge editing but remains computationally expensive due to training overhead. Key open challenges include understanding how to effectively balance old and new knowledge, ensuring edited knowledge generalizes to related facts, and developing unified evaluation benchmarks across method categories.