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Related Questions
- How does the use of prompt-optimized models affect the explainability of the learned representations in transfer learning?
- Can you elaborate on the challenges of interpreting the decision-making processes of transfer-learned models when using prompt-optimized models?
- What are the potential trade-offs between improving model performance and maintaining interpretability when applying transfer learning with prompt-optimized models?
- How does the process of fine-tuning prompt-optimized models impact their interpretability in comparison to training from scratch?
- Can you discuss the relationship between the size of the fine-tuning dataset and the interpretability of transfer-learned models in the context of prompt-optimized models?
- In what ways do prompt-optimized models complicate the task of feature attribution and importance in transfer learning?
- What are some strategies for increasing the interpretability of transfer-learned models when using prompt-optimized models, such as feature selection or dimensionality reduction?
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