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Related Questions
- How does task-specific fine-tuning improve the performance of pre-trained language models in NLP tasks?
- What are the key differences between task-specific fine-tuning and transfer learning in NLP?
- Can you explain the concept of adapter modules in task-specific fine-tuning and their role in adapting pre-trained models to new tasks?
- What are some common challenges that arise when fine-tuning pre-trained language models on specific NLP tasks, and how can they be addressed?
- How does task-specific fine-tuning affect the interpretability of NLP models, and what are some strategies for improving model interpretability?
- What are some best practices for selecting the optimal fine-tuning parameters for a given NLP task, and how can they be tuned for better performance?
- Can you discuss the trade-offs between task-specific fine-tuning and multi-task learning in NLP, and when might one approach be preferred over the other?
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