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Related Questions
- What are the key architectural differences between transformer-based and recurrent neural network (RNN) models, and how do these differences impact their conversational capabilities?
- How do the use of attention mechanisms and self-attention in transformer models enable them to better understand context and engage in more coherent dialogue?
- In what ways do the design choices in pre-trained language models, such as BERT and RoBERTa, facilitate their ability to participate in extended conversations and understand nuances of language?
- What role do memory and working memory play in the architecture of conversational AI models, and how do these components influence their ability to maintain context over time?
- How do the use of pre-training and fine-tuning in large language models impact their ability to engage in extended dialogue and adapt to new topics and contexts?
- What are the trade-offs between model size, complexity, and computational resources, and how do these factors influence the ability of a model to engage in extended dialogue?
- How do the design choices in models that use graph-based or graph-attention mechanisms enable them to better understand relationships between entities and engage in more coherent dialogue?
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