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Related Questions
- How does the quality of training data affect the performance of a fine-tuned language model?
- What are the key factors that influence the relationship between task-specific data quality and model performance?
- Can you provide examples of how poor data quality can lead to biased or inaccurate model outputs?
- How does the size and diversity of the training dataset impact the model's ability to generalize to new tasks?
- What are some common pitfalls to avoid when collecting and preprocessing task-specific data for fine-tuning a language model?
- Can you discuss the role of data augmentation and data curation in improving the quality of task-specific data?
- How can we measure and evaluate the quality of task-specific data to ensure it is suitable for fine-tuning a language model?
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