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Related Questions
- What are some examples of task similarity in NLP and how can it be leveraged to reduce training data requirements?
- Can you discuss the concept of 'meta-learning' and how it relates to task similarity in language models?
- What are some strategies for adapting pre-trained language models to new tasks with minimal fine-tuning?
- How can prompt engineers use 'few-shot learning' to reduce the need for extensive training data?
- What are some techniques for 'transfer learning' and how can they be applied to reduce training data requirements?
- Can you explain the concept of 'domain adaptation' and how it can be used to reduce the need for task-specific training data?
- What are some methods for 'zero-shot learning' and how can they be used to eliminate the need for training data?
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