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Related Questions
- What are the potential consequences of biased training data on the performance of large language models in real-world scenarios?
- How do large language models handle out-of-distribution data and what strategies can be employed to mitigate the impact of non-representative training data?
- In what ways can the diversity and quality of training data influence the ability of large language models to generalize to new, unseen situations?
- Can you explain the concept of 'overfitting' in large language models and how it relates to the representativeness of the training data?
- What techniques can be used to augment or expand the training data of large language models to improve their generalizability?
- How does the representation of minority groups or underrepresented populations in the training data affect the fairness and accuracy of large language models?
- Can you discuss the trade-offs between the size and quality of the training data, and how they impact the performance of large language models in different tasks?
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