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Related Questions
- What is the primary mechanism by which transfer learning enhances the generalizability of large language models?
- How does the pre-training of large language models on a wide range of tasks and domains contribute to their ability to adapt to new tasks?
- What are some key differences between fine-tuning and re-training large language models, and how do these differences impact generalizability?
- Can you explain the concept of 'domain shift' and how transfer learning helps to mitigate its effects?
- In what ways do the pre-trained weights and biases of large language models influence their ability to generalize to new tasks and domains?
- How does the use of transfer learning affect the need for large amounts of labeled training data for new tasks?
- What are some potential risks or challenges associated with relying on transfer learning for generalizability, and how can they be mitigated?
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