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Related Questions
- What are the key differences between in-domain and out-of-domain data for LLMs, and how do they impact model performance?
- How do LLMs handle concept drift in domain adaptation, and what are some techniques used to mitigate this issue?
- What is the role of task-specific knowledge in domain adaptation, and how can LLMs learn to adapt to new tasks and domains?
- What are some common challenges LLMs face when adapting to new domains, such as language differences, cultural nuances, and domain-specific terminology?
- How do LLMs handle the problem of catastrophic forgetting in domain adaptation, and what are some strategies to prevent this phenomenon?
- What is the relationship between domain adaptation and transfer learning in LLMs, and how can these techniques be used to improve model performance in new domains?
- What are some techniques used to evaluate the performance of LLMs in domain adaptation, such as metrics and benchmarks, and how can these be used to compare model performance across different domains?
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