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Related Questions
- How does the process of gradient-based optimization in LLMs lead to overwriting of previously learned information?
- Can you explain the concept of catastrophic forgetting in the context of LLMs and how it relates to gradient-based optimization?
- What are the conditions under which LLMs are more prone to forgetting previously learned knowledge during gradient-based optimization?
- How does the gradient-based optimization algorithm update the model's weights and what are the implications for the stored context?
- Can you provide a scenario where an LLM's gradient-based optimization leads to a loss of previously acquired knowledge?
- What strategies can be employed to mitigate the issue of forgetfulness in LLMs during gradient-based optimization?
- In what ways can the trade-off between learning new information and retaining existing knowledge be optimized in LLMs during gradient-based optimization?
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