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Related Questions
- What are the key differences between pre-training datasets for natural language processing tasks?
- How does the choice of pre-training task affect the downstream performance of a language model?
- Can you explain the concept of 'dataset bias' in the context of pre-training and fine-tuning?
- In what ways can a pre-training dataset that is too narrow or too broad impact fine-tuning requirements?
- How does the level of supervision during pre-training (e.g., supervised, unsupervised, semi-supervised) influence fine-tuning needs?
- What is the relationship between pre-training dataset size and the amount of fine-tuning required?
- Can you discuss the trade-offs between using a pre-training dataset with a specific domain or task versus a more general-purpose dataset?
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