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Related Questions
- How does the diversity and representativeness of pre-training data impact a language model's ability to generalize to new tasks?
- Can you explain the relationship between pre-training data quality and a language model's performance on out-of-distribution tasks?
- What are some common pitfalls in pre-training data that can lead to poor generalizability of language models?
- How does the size and scope of pre-training data influence a language model's ability to adapt to new tasks and domains?
- Can you discuss the importance of data augmentation and noise injection in pre-training data for improving generalizability?
- What role do domain adaptation and transfer learning play in mitigating the impact of limited pre-training data on generalizability?
- How can developers assess and evaluate the quality of pre-training data to ensure it supports a language model's generalizability?
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