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Related Questions
- How does fine-tuning a large language model on a specific task impact its ability to generalize to other tasks?
- What are the key differences in the performance of a model fine-tuned on a specific task versus one fine-tuned on a general language understanding task?
- Can you provide examples of tasks that benefit from fine-tuning on a specific task versus general language understanding?
- How does the level of domain knowledge and task-specific data impact the performance of a fine-tuned language model?
- What are the trade-offs between fine-tuning on a specific task versus general language understanding in terms of model size, training time, and computational resources?
- How does the choice of task and fine-tuning data affect the model's ability to adapt to new, unseen tasks?
- Can you explain the concept of 'task-specific knowledge' and how it differs from general language understanding in the context of fine-tuning language models?
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