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Related Questions
- Can you explain the difference between fine-tuning and pre-training in the context of language models?
- How does fine-tuning on a specific task, such as sentiment analysis, improve the model's performance compared to general language understanding?
- What are some examples of tasks that benefit from fine-tuning on a specific task, such as text classification or question answering?
- Can you provide examples of tasks that require general language understanding, such as conversational dialogue or language translation?
- How does the choice of fine-tuning or general language understanding affect the model's ability to generalize to new, unseen data?
- Can you explain the role of task-specific fine-tuning in adapting a general language model to a new domain or task?
- What are the advantages and disadvantages of using fine-tuning versus pre-training for a specific task, such as named entity recognition or part-of-speech tagging?
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