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Related Questions
- What are the primary goals of prompt priming and data augmentation in knowledge acquisition?
- How do prompt priming and data augmentation differ in terms of the type of information they provide to the model?
- Can you explain the role of prompt priming in fine-tuning language models for specific tasks?
- How does data augmentation impact the diversity of the training data, and what are its implications for model performance?
- What are the potential risks and limitations of relying solely on data augmentation for knowledge acquisition?
- Can you discuss the relationship between prompt priming and data augmentation in terms of their interaction with the model's underlying knowledge graph?
- How do prompt priming and data augmentation contribute to the development of more robust and generalizable language models?
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