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How do LLMs handle variable-length inputs?

What is a key difference in the training approach between LLMs and traditional ML models?

How do LLMs and traditional ML models differ in terms of scale and capacity?

What architecture do LLMs primarily use?

Why are positional encodings added to the input embeddings in a Transformer model?

What additional mechanism is included in the decoder layers that is not in the encoder layers?

What is the primary mechanism that forms the core of both the encoder and decoder in Transformer...

Which LLM uses both an encoder and a decoder for versatile text processing tasks?

What type of architecture does BERT use for understanding text?

Which LLM uses a decoder-only architecture for unidirectional processing?

Why is self-attention important for content production and machine translation in LLMs?

How does self-attention benefit language tasks in LLMs?

What is the role of self-attention in transformer architecture?