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Related Questions
- What are the primary mechanisms underlying catastrophic forgetting in LLMs, and how do they contribute to a loss of performance on previous tasks?
- How does the stability-plasticity dilemma in LLMs influence their ability to generalize and adapt to new tasks while retaining existing knowledge?
- What are some strategies that LLM developers use to mitigate catastrophic forgetting and promote long-term knowledge retention and adaptation?
- Can you provide examples of tasks or scenarios where catastrophic forgetting is particularly problematic in LLMs, and how it impacts their performance?
- How do the memory and representation capabilities of LLMs contribute to catastrophic forgetting, and what can be done to improve their memory mechanisms?
- What role does overfitting play in catastrophic forgetting, and how can LLM developers balance model capacity with regularization techniques to prevent overfitting?
- Are there any notable applications or industries where catastrophic forgetting is a significant concern, and how are researchers and practitioners addressing this challenge?
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