Readings

• GeeksforGeeks: What are LLM Parameters?

• OpenAI: What are tokens and how to count them?

• IBM: What is a context window?

• Datasets for large language models: a comprehensive survey

• You may explore this visualization of an LLM workflow. It includes many technical details, so reviewing it is optional. You may simply explore the visualization to get a general sense of how LLM components connect.

• Optional: Jay Alammar: The Illustrated Transformer explains the transformer architecture in detail. Some parts may be technically challenging.

• AI Demystified: Introduction to large language models

• Andreas Stöffelbauer: How Large Language Models work? From zero to ChatGPT

• MIT Sloan Management Review: How LLMs Work: Top 10 Executive-Level Questions

• Drexel University: Dragons’ Guide to GAI

• Blog posts about Measuring LLM Performance

  • Data leakage is a major issue when measuring LLM performance
  • What LLM benchmarks get wrong about measuring model performance
  • Why the LMArena is a flawed approach to LLM evaluation

• Simon Willison: Understanding the recent criticism of the Chatbot Arena

• The Leaderboard Illusion

• Wikipedia: Language model benchmark

• Meta’s benchmarks for its new AI models are a bit misleading

• Evidently AI: 30 LLM evaluation benchmarks and how they work

• A Multimodal World

• GeeksforGeeks: Multimodal Large Language Models

• Google: What is agentic AI?

• Explaining Agentic AI: The Good, the Bad & the Ugly

• I asked ChatGPT if I should drive or walk to the car wash to get my car washed — and it struggled with basic logic

• A Definition of AGI

• Why language models hallucinate

• The AI revolution is running out of data. What can researchers do?

• Anthropic: A small number of samples can poison LLMs of any size

• Yann LeCun: We Won’t Reach AGI By Scaling Up LLMS

• Richard Sutton – Father of RL thinks LLMs are a dead end

• How close is AGI? What the experts say.

• Is AI Hiding Its Full Power? With Geoffrey Hinton