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AI / LLM Basics Interview Questions

What is Layer Normalization?

Layer Normalization is a technique used inside a Transformer to keep the scale of values flowing through the network stable as they pass through many stacked layers.

  • Rescales the values within each layer's output to have a consistent mean and variance
  • Helps training converge faster and more reliably, since wildly varying value scales can make gradient-based training unstable
  • Applied at specific points within each Transformer block, typically before or after the attention and feed-forward steps

Without this kind of stabilization, training a network as deep as a modern LLM, often dozens of stacked layers, would be considerably harder to get working reliably.

What does Layer Normalization keep stable?
What does Layer Normalization help with during training?

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What is a Large Language Model (LLM)? What are Tokens in an LLM? What is Tokenization? What is Byte-Pair Encoding (BPE)? What is the Vocabulary of an LLM? What is an Embedding in the context of LLMs? Define the Transformer architecture? What is the Attention Mechanism? What is Self-Attention? What is Multi-Head Attention? What is Positional Encoding? What is the purpose of the Feed-Forward Network inside a Transformer block? What is Layer Normalization? What is Causal Masking? Describe the role of Softmax in an LLM's output layer? What is Cross-Entropy Loss? What is the purpose of a Loss Function during LLM training? What is Pretraining in the context of LLMs? What is Fine-Tuning? What is Instruction Tuning? Describe Reinforcement Learning from Human Feedback (RLHF)? What is Alignment in the context of LLMs? Define In-Context Learning? Define Zero-Shot Learning? Define Few-Shot Learning? What is Chain-of-Thought Prompting? What is Prompt Engineering? What is the purpose of a System Prompt? What is a Context Window? What is the Max Tokens parameter? What are Stop Sequences? What is Temperature in LLM sampling? What are Top-k and Top-p Sampling? What is Hallucination in LLMs? What is Perplexity in language modeling? What is Overfitting in the context of training an LLM? What are Parameters in an LLM? What is Quantization? What is Model Distillation? What is a Foundation Model? What is a Decoder-Only model? What is an Encoder-Only model? What is an Encoder-Decoder model? What is a Mixture of Experts (MoE) architecture? What is Retrieval-Augmented Generation (RAG)? How are Embeddings used beyond text generation? What are Guardrails in LLM applications? What is Prompt Injection?
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