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Related Questions
- What are the primary objectives of fine-tuning and re-training large language models, and how do they differ in terms of model adaptation and task-specific knowledge?
- How do the differences in fine-tuning and re-training impact the generalizability of large language models to new tasks and domains?
- Can you provide examples of scenarios where fine-tuning versus re-training would be more appropriate for a large language model, and why?
- How do the hyperparameters and training objectives used in fine-tuning and re-training affect the model's ability to generalize to unseen data?
- What are some common challenges and limitations associated with fine-tuning and re-training large language models, and how can they be addressed?
- How do the differences in fine-tuning and re-training impact the model's ability to capture task-specific nuances and context-dependent relationships?
- Can you discuss the role of transfer learning in fine-tuning and re-training large language models, and how it contributes to improved generalizability?
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