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Related Questions
- How does the choice of pre-training corpus impact the robustness of large language models to out-of-distribution data?
- Can you elaborate on the role of masked language modeling tasks in pre-training and its effect on generalizability?
- How do the pre-training objectives, such as masked language modeling and next sentence prediction, contribute to the model's ability to generalize to unseen data?
- In what ways do pre-training on a diverse set of languages affect the model's ability to generalize to new languages?
- How does the size and quality of the pre-training dataset impact the model's generalizability to real-world data?
- Can you discuss the relationship between pre-training on large datasets and the model's ability to adapt to new tasks and domains?
- What are the trade-offs between pre-training on a large dataset and fine-tuning on a smaller dataset in terms of generalizability and computational resources?
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