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Related Questions
- How does pre-training a language model on a large corpus of text enable it to learn contextual relationships between words and phrases?
- What is the difference between pre-training and fine-tuning in the context of text generation models, and how do they contribute to contextual understanding?
- Can you explain the concept of contextual embedding and how it relates to the pre-training and fine-tuning process in text generation models?
- How do text generation models use contextual information to generate more coherent and relevant text during the fine-tuning process?
- What are some common techniques used during fine-tuning to adapt a pre-trained language model to a specific task or domain?
- How does the choice of pre-training data and fine-tuning objectives impact the ability of a text generation model to incorporate context?
- What are some challenges associated with fine-tuning pre-trained language models for contextual understanding, and how can they be addressed?
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