Explain tokenization and Transformer variants

Q: Explain tokenization and Transformer variants

This question evaluates understanding of tokenization techniques and Transformer architecture, covering competencies in subword and SentencePiece-style tokenizers, Transformer block internals, and comparisons of modern architectural variants and trade-offs.

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Question

Tokenization and Transformer Architecture Deep Dive

You are asked to explain common tokenization approaches and modern Transformer design choices used in large language models.

Answer the following:

SentencePiece

What is SentencePiece, and how does it work at a high level?

Tokenizers used in BERT and typical Transformer-based LMs

Which tokenizers do BERT and common decoder-only LMs (e.g., GPT-style, LLaMA, Qwen) typically use, and why?

Transformer block internals

Enumerate the core components inside a Transformer block and briefly describe the role of each.

Architectural comparisons and design trade-offs

Compare a vanilla Transformer (Vaswani et al., 2017) to modern LLaMA and Qwen architectures.
Discuss the benefits and trade-offs of choices such as Mixture-of-Experts (MoE), RMSNorm, and rotary positional embeddings (RoPE).

Explain tokenization and Transformer variants

Tokenization and Transformer Architecture Deep Dive

Solution

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Explain tokenization and Transformer variants

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Tokenization and Transformer Architecture Deep Dive

Solution

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