The core challenge in continual learning for Large Language Models (LLMs) is catastrophic forgetting, where models degrade performance on old tasks when trained on new data[2][3][4]. The massive scale of LLMs introduces a huge computational burden for frequent retraining, requiring efficient adaptation to evolving data while balancing general capabilities with new task learning[2][4]. Handling non-IID data and avoiding destructive gradient updates from external data are critical[3].
Additional challenges arise from multi-stage training, including task heterogeneity, inaccessible upstream data, long task sequences, and abrupt distributional shifts[2]. There is a need for practical evaluation benchmarks, computationally efficient methods, controllable forgetting, and history tracking[2][4]. Theoretical understanding of LLM forgetting and memory interpretability remain significant hurdles[2][4].
Get more accurate answers with Super Pandi, upload files, personalized discovery feed, save searches and contribute to the PandiPedia.
Let's look at alternatives:
- Modify the query.
- Start a new thread.
- Remove sources (if manually added).
Curated by Joan
