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Related Questions
- How do different training objectives such as next sentence prediction and sentence ordering impact a model's understanding of context and relationships between sentences?
- What are the key differences between maximum likelihood estimation and other training objectives like reinforcement learning and self-supervised learning in the context of language models?
- In what ways can the choice of training objective affect the model's ability to generalize to new, unseen data and adapt to changing language patterns?
- How do masked language modeling and other forms of self-supervised learning enable a model to learn from unlabelled data and improve its performance over time?
- Can you explain the concept of overfitting and underfitting in the context of language models, and how different training objectives can mitigate these issues?
- How do the hyperparameters of a model, such as the learning rate and batch size, interact with the choice of training objective to impact the model's performance and adaptability?
- What are some common pitfalls or challenges that can arise when using maximum likelihood estimation and masked language modeling in language model training, and how can they be addressed?
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