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Related Questions
- Can domain adaptation techniques such as pseudo-labeling, active learning, and self-supervision help LLMs adapt to new domains with minimal degradation in performance?
- How can domain-invariant representation learning strategies, such as contrastive learning, transfer the commonalities across different domains and minimize the differences?
- Can transfer learning from similar tasks and domain-agnostic prompt templates alleviate the effect of domain shift on LLMs and enable faster adaptation?
- In what ways can the choice of input/output representations and linguistic pre-training objectives help alleviate domain mismatch and ensure that LLMs are generalizable?
- What techniques in prompt engineering, such as controlled-language interfaces or augmented input text, can modify the domain-adaptive behaviors of LLMs?
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