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Related Questions
- How does the quality of training data impact the ability of a large language model to adapt to new, unseen scenarios?
- What are the consequences of training a model on biased or unrepresentative data for its ability to generalize to diverse tasks?
- Can a model be trained to generalize across multiple domains if the training data is sourced from a single domain?
- How does the size and diversity of the training dataset affect the model's ability to learn abstract concepts?
- What techniques can be employed to ensure the training data is representative of the real-world scenarios the model will encounter?
- Can a model be fine-tuned to adapt to new tasks and domains if the original training data is limited?
- What are the trade-offs between data diversity, representativeness, and model performance in terms of generalizability?
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