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Related Questions
- How do different pre-training objectives, such as masked language modeling or next sentence prediction, affect a model's ability to generalize to out-of-domain data?
- Can you explain the trade-offs between pre-training objectives that focus on language modeling versus those that focus on other tasks, such as sentiment analysis or question answering?
- In what ways do pre-training objectives influence the model's ability to adapt to new domains and tasks, and how can this be measured?
- How do the pre-training objectives used in large language models impact their ability to capture domain-specific knowledge and relationships?
- Can you discuss the role of pre-training objectives in determining the model's ability to generalize to unseen data and handle out-of-vocabulary words?
- What are some common pitfalls or challenges associated with using pre-training objectives that may hinder a model's ability to generalize to out-of-domain data?
- How can the choice of pre-training objectives be optimized to improve a model's ability to generalize to new domains and tasks?
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