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Related Questions
- What is the primary difference in architecture between large language models and traditional NLP models?
- How do large language models handle sequence-to-sequence tasks compared to traditional NLP models?
- What design choices led to the development of large language models, and how do they impact performance?
- How do large language models incorporate context and long-term dependencies compared to traditional NLP models?
- What is the role of self-supervised learning in the development of large language models, and how does it differ from traditional NLP models?
- How do large language models handle out-of-vocabulary words and rare events compared to traditional NLP models?
- What are some key challenges in designing large language models that are not present in traditional NLP models, and how are they addressed?
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