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Related Questions
- Can you explain the concept of pre-training and fine-tuning in transfer learning and how it applies to large language models?
- How does transfer learning enable large language models to adapt to new text classification tasks without requiring extensive retraining?
- What are the key advantages of using transfer learning in large language models for text classification tasks, such as improved accuracy and reduced training time?
- Can you discuss the importance of choosing the right pre-trained model and fine-tuning strategy for a specific text classification task?
- How does transfer learning help large language models to capture domain-specific knowledge and adapt to new text classification tasks?
- What are some common challenges and limitations of using transfer learning in large language models for text classification tasks, such as overfitting and data quality?
- Can you provide examples of successful applications of transfer learning in large language models for text classification tasks, such as sentiment analysis and topic modeling?
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