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Related Questions
- What are the key differences between fine-tuning objectives such as masked language modeling and next sentence prediction?
- How does the choice of fine-tuning objective impact the model's ability to generalize to out-of-distribution data?
- Can you explain the concept of 'domain adaptation' in the context of fine-tuning objectives and how it relates to novel tasks and domains?
- What are some common pitfalls to avoid when selecting a fine-tuning objective for a novel task or domain?
- How does the fine-tuning objective influence the model's capacity to learn task-specific features and ignore irrelevant information?
- Can you provide examples of fine-tuning objectives that are well-suited for adapting to novel tasks and domains, such as few-shot learning or meta-learning?
- What are some strategies for selecting a fine-tuning objective that balances the trade-off between task-specific knowledge and domain-invariant representations?
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