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Related Questions
- How do regularization techniques, such as L2 and L1 regularization, help prevent catastrophic forgetting in large language models (LLMs)?
- Can you explain the importance of weight decay in neural networks and its relation to preventing catastrophic forgetting in LLMs?
- What is the role of early stopping in preventing model overfitting and what are its implications for minimizing catastrophic forgetting in LLMs?
- Do regularization techniques, such as dropout, help prevent or mitigate catastrophic forgetting in deep learning models, including LLMs?
- How do LLM developers use transfer learning and adaptive learning to minimize catastrophic forgetting and improve model performance in real-world applications?
- Can you discuss the trade-off between model simplicity and performance, and how regularization techniques can help to prevent catastrophic forgetting in underdetermined models?
- What is the recent research on novel regularization strategies, such as knowledge graphs and meta-learning, designed to mitigate catastrophic forgetting and improve the performance of modern LLMs?
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