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Related Questions
- What are the benefits and challenges of using transfer learning for adapting LLMs to new domains?
- How do domain adaptation techniques such as few-shot learning and meta-learning improve LLM performance in new domains?
- What is the role of fine-tuning in adapting LLMs to new domains, and how does it affect model performance?
- What are the differences between domain adaptation and multi-task learning, and how do they impact LLM performance?
- How do LLMs handle out-of-domain data, and what techniques can be used to mitigate the effect of such data on model performance?
- What is the impact of domain shift on LLM performance, and how can it be addressed through techniques such as data augmentation and adversarial training?
- What are some common evaluation metrics used to measure the performance of LLMs in new domains, and how do they impact model adaptation?
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