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Related Questions
- What are the common techniques used to fine-tune pre-trained LLMs for domain-specific tasks?
- How can you evaluate the performance of a fine-tuned LLM model to determine if it has overfit or underfit?
- What are the trade-offs between adding more domain-specific knowledge and increasing the risk of overfitting?
- Can you explain the concept of 'curse of knowledge' in the context of fine-tuning LLMs and how to mitigate it?
- How do you determine the optimal amount of domain-specific data to use for fine-tuning an LLM model?
- What are the differences between transfer learning, few-shot learning, and fine-tuning in the context of LLMs?
- Can you discuss the role of data augmentation in preventing overfitting when fine-tuning LLMs for domain-specific tasks?
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