Transformer Architecture and LLM Basics

Describe the structure of a transformer block: self-attention, feed-forward network, residual connections, and layer normalization

Explain LayerNorm and how it stabilizes training in deep networks

Distinguish encoder-decoder transformers from decoder-only transformers and identify their typical use cases

Describe how decoder-only models like GPT perform text generation via iterative next-token sampling

Explain the KV cache mechanism and how it avoids redundant computation during autoregressive inference

Describe the pre-training and fine-tuning paradigm for large language models