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Related Questions
- How does fine-tuning a pre-trained language model affect its ability to generalize to out-of-distribution data?
- What are the potential trade-offs between task-specific performance and generalizability when fine-tuning a pre-trained language model?
- Can fine-tuning a pre-trained language model for a specific task lead to overfitting, and if so, how can it be mitigated?
- How does the quality and quantity of the fine-tuning dataset impact the pre-trained language model's ability to generalize to new data?
- Are there any strategies for fine-tuning a pre-trained language model that can help maintain its generalizability while still improving its performance on a specific task?
- What role does the choice of pre-training objectives and architectures play in determining the generalizability of a fine-tuned language model?
- Can fine-tuning a pre-trained language model for a specific task lead to a loss of semantic meaning or contextual understanding, and if so, how can it be avoided?
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