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Related Questions
- What are the advantages of using a masking technique in pre-training language models compared to other techniques like cloze tasks or predicting the next word in a sequence?
- How does masking help to prevent overfitting in pre-trained language models, and what are the potential consequences of not using masking?
- Can you explain how masking affects the performance of language models on tasks that require understanding the context of a sentence or passage, such as question answering or text classification?
- What are some common challenges or limitations of using masking in pre-training language models, and how can these be addressed?
- How does the choice of masking technique (e.g., random masking, pattern-based masking, or context-aware masking) impact the performance of the pre-trained model?
- What are the key differences between using masking in pre-training and using it as a fine-tuning technique for specific downstream tasks, and when is each approach most effective?
- Can you discuss the trade-offs between using masking, such as the balance between reducing overfitting and maintaining model expressiveness?
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