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Related Questions
- What are the key differences between prompt priming and data augmentation in improving LLMs on common sense tasks?
- How do the limitations of prompt priming, such as the need for high-quality prompts, impact its effectiveness compared to data augmentation?
- Can you explain the potential challenges of using data augmentation, such as the risk of overfitting, and how they compare to the challenges of prompt priming?
- In what ways can the quality of training data affect the performance of LLMs using prompt priming versus data augmentation?
- How do the computational resources required for prompt priming and data augmentation compare, and what are the implications for large-scale LLM training?
- What are some potential strategies for mitigating the limitations of prompt priming, such as using multi-task learning or transfer learning?
- Can you discuss the role of human evaluation in assessing the effectiveness of prompt priming versus data augmentation for improving LLMs on common sense tasks?
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