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Related Questions
- How can I ensure that my dataset is diverse and representative of the real-world scenarios for effective data augmentation in LLMs?
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- What are the key factors to consider when deciding on the type of data to augment, such as text, images, or audio?
- How can I balance the need for diverse data with the risk of overfitting or introducing noise in the augmented data?
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- How can I use active learning and human-in-the-loop approaches to augment data and improve the quality of the augmented data?
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