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Related Questions
- How do input/output representations, such as tokenization and embedding schemes, contribute to the generalizability of LLMs across different domains?
- What are the implications of using masked language modeling vs. next sentence prediction as pre-training objectives on the ability of LLMs to adapt to new domains?
- Can you explain how the choice of linguistic pre-training objectives, such as predicting next sentence or masked language modeling, affects the generalizability of LLMs to out-of-domain tasks?
- In what ways can LLMs be fine-tuned to handle domain-specific linguistic variations, such as dialects or jargon, to improve their generalizability?
- How do the design choices of LLMs, including the size of the model, the number of layers, and the type of activation functions, impact their ability to generalize across domains?
- What role does the choice of pre-training data, such as the type of texts and the amount of data, play in ensuring the generalizability of LLMs across different domains?
- Can you discuss the trade-offs between the use of domain-specific pre-training data vs. general-purpose pre-training data in terms of generalizability and performance?
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