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Related Questions
- How does model complexity impact the ability of large language models to adapt to new tasks and domains?
- What are the typical trade-offs between model size and adaptability in large language models, and how do they affect performance?
- Can smaller language models be more adaptable than larger ones in certain situations, and if so, why?
- How does the choice of model architecture affect the balance between model size and adaptability?
- What are the implications of increasing model size on the ability to adapt to out-of-distribution data?
- Are there any specific techniques or strategies that can improve adaptability in large language models without significantly increasing model size?
- How does the concept of 'capacity' in large language models relate to model size and adaptability, and what are its implications for model design?
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