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Related Questions
- How does data noise and bias in the training data affect the model's performance on out-of-distribution examples?
- What are some common issues that can arise from low-quality training data, such as overfitting or underfitting?
- Can you explain the concept of data curation and its importance in improving the quality of training data for large language models?
- What are some strategies for evaluating the quality of training data, such as data validation or data augmentation?
- How can domain adaptation and transfer learning be used to improve the generalization of a large language model on a new task or domain?
- What is the impact of data size and diversity on the generalization of a large language model, and how can these factors be optimized?
- Can you discuss the role of human evaluation and feedback in improving the quality of training data for large language models?
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