Transformer

GPT-2 (2019) Paper Notes

Paper notes on GPT-2 covering its core ideas: decoder-only Transformer scaling, WebText, next-token prediction, zero-shot task transfer, and the staged release controversy.

llm paper-reading transformer gpt-2 zero-shot openai

2026년 5월 17일

BERT (2018) Paper Notes

Paper notes covering the core ideas of BERT: the bidirectional Transformer encoder, masked language model, next sentence prediction, and the fine-tuning paradigm.

llm paper-reading transformer bert arxiv

2026년 4월 18일

LLM Architecture Basics: Encoder-only vs. Decoder-only

A comparison of encoder-only and decoder-only architectures that distinguish the BERT and GPT families.

llm transformer encoder-only decoder-only paper-reading

2026년 4월 18일

Transformer Basics: Encoder and Decoder

A clear explanation of what the Transformer encoder and decoder each do, grounded in the original architecture and illustrated with simple examples.

llm transformer encoder decoder paper-reading

2026년 4월 18일

Attention Is All You Need (2017) — Paper Notes

A reading note on the Transformer paper — the core ideas, why it mattered, and what to read next.

llm paper-reading transformer arxiv

2026년 4월 17일

Transformer Basics: Intuition Behind Q, K, and V

Explains the role of Q, K, and V through the attention formula, a plain-language walkthrough, and a concrete example.

llm transformer attention qkv paper-reading

2026년 4월 17일

Transformer Basics: Residual, LayerNorm, FFN

Why Residual, LayerNorm, and FFN are necessary in a Transformer block — explained with equations, commentary, and examples.

llm transformer residual layernorm ffn paper-reading

2026년 4월 17일