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Related Questions
- What are the key differences between supervised and unsupervised domain adaptation in the context of large language models (LLMs)?
- Can you explain how supervised domain adaptation uses labeled data to adapt to a new domain, and how unsupervised domain adaptation uses unlabeled data?
- How do the objectives of supervised and unsupervised domain adaptation differ, and what are the implications for LLM performance?
- What are some common techniques used in supervised domain adaptation, such as multi-task learning and adversarial training?
- In unsupervised domain adaptation, how do methods like maximum mean discrepancy (MMD) and correlation alignment (CORAL) work to align the source and target domains?
- Can you discuss the challenges of unsupervised domain adaptation, such as the need for domain-invariant representations and the lack of labeled data?
- How does the choice of domain adaptation approach impact the interpretability and explainability of LLMs?
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