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Related Questions
- How can LLMs be designed to adapt to new domains or tasks without sacrificing performance in their original area of expertise?
- What are some strategies for mitigating the trade-off between specialization and generalization in LLMs?
- Can transfer learning or multi-task learning approaches help LLMs to adapt to new domains or tasks?
- How do you think LLMs can be designed to handle the 'long tail' of low-frequency tasks that require specialized knowledge?
- What are the potential risks of over-reliance on domain-specific knowledge in LLMs, and how can they be mitigated?
- Can meta-learning or few-shot learning approaches help LLMs to quickly adapt to new domains or tasks without extensive retraining?
- What are some potential implications of LLMs becoming overly specialized for a particular task or domain, and how can this be addressed?
- How can LLMs be designed to balance the need for specialization with the need for generalization in real-world applications?
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