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Related Questions
- What are some common pitfalls to avoid when pre-training a large language model on a diverse set of languages?
- How do linguistic differences across languages impact the pre-training process, and what adjustments can be made to mitigate these effects?
- What are some strategies for addressing the issue of linguistic diversity in pre-training data, and how do they impact model performance?
- Can you explain the concept of 'language universals' and how they can be leveraged to improve pre-training on diverse languages?
- What are some techniques for handling low-resource languages during pre-training, and how do they compare to those used for high-resource languages?
- How does pre-training on a diverse set of languages affect the model's ability to generalize to new, unseen languages?
- What role do you think data augmentation techniques play in addressing challenges associated with pre-training on diverse languages?
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