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Related Questions
- What are the common issues that occur when fine-tuning pre-trained models for specific downstream tasks?
- How does the choice of pre-training corpus affect the performance of the fine-tuned model?
- What are some strategies for adapting pre-trained models to handle out-of-distribution data?
- How does the level of fine-tuning (e.g., few-shot, meta-learning) impact the model's performance?
- What are some common pitfalls to avoid when fine-tuning pre-trained models for specific tasks?
- How can we ensure that the fine-tuned model generalizes well to unseen data?
- What are some techniques for handling the trade-off between task-specific knowledge and generalizability in fine-tuned models?
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