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Related Questions
- What are the primary challenges of catastrophic forgetting in incremental learning, and how do embedding perturbation techniques address these challenges?
- Can you explain how embedding perturbation techniques, such as Jacobian-based methods, modify the model's weights to prevent forgetting of previously learned tasks?
- How do embedding perturbation techniques, like weight perturbation and input perturbation, interact with language models to prevent catastrophic forgetting in incremental learning?
- What is the relationship between embedding perturbation techniques and the concept of 'interference' in incremental learning, and how do they mitigate it?
- How do embedding perturbation techniques affect the dynamic adjustment of the model's capacity to adapt to new tasks while preserving knowledge of previous tasks?
- Can you discuss the role of embedding perturbation techniques in reducing the effect of catastrophic forgetting in language models, particularly in the context of few-shot learning?
- What are the key differences between embedding perturbation techniques and other methods, such as regularization and knowledge distillation, in preventing catastrophic forgetting in language models?
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