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Related Questions
- How does data diversity impact the performance of large language models in terms of their ability to generalize to new tasks and domains?
- What are the potential consequences of using a small, biased dataset to train a large language model, and how can this be mitigated?
- What are some strategies for ensuring that large language models are trained on a representative and diverse dataset?
- Can you explain the relationship between data sparsity and the quality of large language models, particularly in the context of low-resource languages?
- How does the quality of training data affect the ability of large language models to learn nuanced language patterns and idioms?
- What are some best practices for collecting and preprocessing data for large language models, particularly in terms of data curation and annotation?
- Can you discuss the trade-offs between data quantity and data quality in the context of large language model training, and how to balance these competing priorities?
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