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Related Questions
- Can data augmentation techniques be applied to pre-trained language models to improve their performance on downstream tasks?
- How does data augmentation impact the generalizability of pre-trained language models to new tasks or domains?
- Can data augmentation techniques be used to create synthetic data for pre-trained language models that are more diverse and representative of real-world data?
- How does the choice of data augmentation technique affect the performance of pre-trained language models on specific tasks?
- Can pre-trained language models be fine-tuned using data augmentation techniques to adapt to new tasks or domains?
- What are some common data augmentation techniques used for pre-trained language models, such as token insertion, token substitution, or paraphrasing?
- Can data augmentation techniques be used to mitigate the effects of overfitting in pre-trained language models?
- How does data augmentation impact the interpretability of pre-trained language models, and are there any techniques to improve interpretability while still using data augmentation?
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