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Related Questions
- How do prompt engineers ensure that LLMs learn domain-specific knowledge without compromising their ability to generalize to new, unseen situations?
- What techniques can prompt engineers use to adapt existing LLMs to tackle complex, domain-specific tasks while maintaining their generalizability?
- What is the role of knowledge graph-based approaches in enabling LLMs to acquire domain-specific knowledge while preserving their ability to generalize?
- How do prompt engineers balance the trade-off between providing enough context for domain-specific knowledge and avoiding over-specification, which can limit generalizability?
- Can you explain the concept of 'domain adaptation' in the context of LLMs and how prompt engineers can achieve it?
- What is the relationship between domain-specific knowledge and the concept of 'common sense' in LLMs, and how do prompt engineers address this relationship?
- How do prompt engineers evaluate the effectiveness of LLMs in learning domain-specific knowledge and generalizable understanding, and what metrics do they use for this purpose?
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