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Related Questions
- Can fine-tuning a pre-trained language model on a specific domain improve its performance on out-of-domain tasks?
- How does the degree of domain specificity affect the model's ability to generalize to out-of-domain tasks?
- What are the potential risks of over-specializing a pre-trained model on a specific domain, potentially limiting its performance on other tasks?
- How does the choice of pre-training tasks and datasets impact the model's ability to adapt to new domains?
- Can transfer learning from a pre-trained model be used to improve performance on out-of-domain tasks, and if so, how?
- What techniques can be used to mitigate the effects of domain drift and improve the model's performance on out-of-domain tasks?
- Can pre-trained models be used as a starting point for multi-task learning, and what are the benefits and drawbacks of this approach?
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