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Related Questions
- How does the quality of pre-training data impact a language model's ability to learn abstract concepts and relationships?
- Can a language model trained on a smaller dataset still achieve good performance on out-of-distribution tasks if it is fine-tuned on a related task?
- What are the implications of using a limited pre-training dataset on a language model's ability to capture nuances of human language?
- Does the size of the pre-training dataset directly affect a language model's capacity for multitasking and adaptability?
- How does the diversity of the pre-training dataset influence a language model's ability to generalize to new, unseen data?
- Can a language model trained on a large dataset but with a narrow focus (e.g., only on a specific domain) still generalize well to other domains?
- What are the trade-offs between increasing the size of the pre-training dataset and improving the model's ability to generalize versus overfitting to the training data?
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