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Related Questions
- What are the key factors that influence the relationship between training dataset size and out-of-distribution detection accuracy in large language models?
- How does the quality of the training dataset impact the model's ability to generalize and avoid overfitting?
- Can you explain the concept of overfitting in the context of machine learning and how it relates to out-of-distribution detection?
- What are some techniques for improving the quality and size of the training dataset to enhance out-of-distribution detection accuracy?
- How does the balance between dataset size and quality affect the trade-off between out-of-distribution detection accuracy and overfitting?
- What are the implications of overfitting on the performance of out-of-distribution detection models, and how can it be mitigated?
- Can you discuss the role of data augmentation and transfer learning in improving the quality and size of the training dataset for out-of-distribution detection?
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