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Related Questions
- How does the quality of training data impact the ability of a large language model to generalize to novel contexts?
- What are the key factors that contribute to the diversity of a dataset used to train a language model, and how does this impact its ability to generalize?
- How does the concept of inductive bias relate to the generalization ability of a large language model, and what role does data quality play in mitigating this bias?
- Can a large language model effectively generalize to new contexts if the training data lacks diversity in terms of domains, styles, or sources?
- What is the impact of overfitting on a language model's ability to generalize to unseen contexts, and how can data quality mitigate this issue?
- How does the concept of data curation relate to the generalization ability of a large language model, and what are the best practices for ensuring high-quality data?
- What is the relationship between the size of the training dataset and the model's ability to generalize to new contexts, and are there any diminishing returns to increasing dataset size?
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