Kanwal Mehreen Highlights 5 Papers Explaining LLMs, From 175-Billion-Parameter GPT-3 to RAG
Updated
Updated · KDnuggets · Jun 3
Kanwal Mehreen Highlights 5 Papers Explaining LLMs, From 175-Billion-Parameter GPT-3 to RAG
1 articles · Updated · KDnuggets · Jun 3
Summary
Five papers anchor Mehreen’s guide to how large language models work, laying out a path from Transformer basics to retrieval-augmented generation.
The list starts with “Attention Is All You Need,” which introduced self-attention, multi-head attention and the Transformer architecture that underpins models such as GPT, Llama and Gemini.
GPT-3’s 175-billion-parameter paper and the scaling-laws study explain why prompting works and why bigger models, more data and more compute drove rapid gains in performance.
InstructGPT and the RAG paper then show how base models become practical assistants—through human-feedback tuning and by pulling external documents for more grounded, up-to-date answers.
Together, the sequence frames modern LLM development as Transformer architecture, pretraining, scaling, instruction tuning and retrieval rather than a single breakthrough.