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Related Questions
- How can we regularize the weights of the transformer encoder to prevent overfitting to in-domain data?
- What techniques can be used to enhance the robustness of the LLM to variations in language and domain?
- Can you discuss methods for incorporating external knowledge from a knowledge graph or domain-specific resources to improve LLM performance on out-of-domain tasks?
- How can we use few-shot or meta-learning to enable the LLM to quickly adapt to new domains or unseen tasks?
- What role does prompt engineering play in improving the LLM's ability to generalize across domains and tasks?
- Can you describe techniques for using self-supervised learning or unsupervised learning to improve the LLM's ability to learn from unlabeled data and generalize to new domains?
- How can we evaluate the generalizability of an LLM to unseen data or out-of-domain tasks, and what metrics or benchmarks can we use to measure its performance in these scenarios?
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