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Related Questions
- What are the primary factors that contribute to overfitting in LLMs, and how do gradient-based optimization techniques address these issues?
- How do gradient-based optimization techniques, such as stochastic gradient descent (SGD) and Adam, impact the learning dynamics of LLMs in terms of convergence speed and stability?
- Can you explain the concept of regularization in the context of gradient-based optimization techniques for LLMs, and how it helps to prevent overfitting?
- In what ways do gradient-based optimization techniques influence the exploration-exploitation trade-off in LLMs, particularly in scenarios where the training data is limited or noisy?
- How do gradient-based optimization techniques, such as gradient clipping and gradient normalization, impact the robustness of LLMs to adversarial attacks and outliers?
- Can you discuss the relationship between gradient-based optimization techniques and the concept of 'regularization by noise' in LLMs, and how it can be used to improve generalization?
- What are some common pitfalls or challenges associated with using gradient-based optimization techniques for LLMs, and how can they be mitigated?
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