LLMs Unplugged is a set of hands-on teaching resources that demonstrate
how large language models work—no computers or coding required. You count
word patterns in a text, record them as tally marks on grid paper or physical
piles of paper cutouts, then roll dice to generate new text from those patterns.
After building a Language modelA system that predicts what text comes next based on patterns learned from training data. Your hand-built grid or cutouts spread is a language model.View in glossary by hand, something clicks: you see that tools like ChatGPTOpenAI's chatbot, and probably the most well-known LLM product. On this site we often use "ChatGPT" as shorthand for any modern LLM chatbot---the concepts apply equally to Claude, Gemini, DeepSeek and others. The underlying principles are the same regardless of which product you use.View in glossary work using the same basic principles, just at a vastly larger scale.
Who is this for?
Although these activities are called "lessons" grouped into "topics",
they're not just for school classrooms. These activities are suitable
for audiences from primary school age through to adults—we've
delivered them to hundreds of participants including school students,
undergraduate students, and senior executives from both industry and
government. Zero coding background required, and no maths beyond basic
counting and percentages.
They work equally well for professionals who need to understand AI for
decision-making in their organisations, parents wanting to learn
alongside their kids, and educators teaching AI concepts in schools or
universities. Even if you know a bit (or a lot!) about how LLMs work,
you'll still have fun making one and sharing it with others.
LLMs Unplugged is a Cybernetic Studio project created by Dr. Ben Swift (PhD
Computer Science, 2012). But if you're worried that you'll need
a PhD in Computer Science to understand it then rest assured—the whole
point of these resources is that demonstrate that you don't. Which leads
to the question…
What prior knowledge do I need?
For instructors, the reading and numeracy skills
required to teach this stuff are really only primary-school level.
This isn't just for specialist maths teachers or tertiary educators,
although it works great in those contexts too. For participants, really all you need is a curiosity about how AI systems actually
work and a willingness to engage with hands-on activities.
Each lesson includes instructor notes and discussion
questions, plus interactive widgets (which you can use to get your head
around the concepts) and—coming soon—explainer videos you can
use in class. We also run teacher training sessions and offer professional
delivery services if you'd like support bringing these activities to your
organisation or classroom.
Why does this matter?
ChatGPT arrived in November 2022 and
suddenly (most) everyone's using Language modelA system that predicts what text comes next based on patterns learned from training data. Your hand-built grid or cutouts spread is a language model.View in glossary . Yet most people have no real mental model of what's actually
happening under the hood. They've heard the hand-wave-y and
mystical-sounding explanations, maybe picked up some vague notions
about "neural networks" and "training data", but the core mechanism
remains opaque.
Understanding how these systems actually work—not through
metaphor or handwaving but through direct experience—is what
we're all about. In a world where everyone uses LLMs, this kind of
understanding isn't optional anymore.
These resources don't tell you or your students what to think about
role of AI (good/mid/cooked) in society. They provide a grounding for
you to have that discussion; one that's informed not by folktales
about what might be happening under the hood but by a lived experience of
doing it for yourself.
Why do you keep saying LLMs—this is AI, right?
Honestly, the short answer (these days) is yes. The long answer is
more complicated—AI is a term that has been applied to many
different algorithmic techniques and systems over the years. But today
(in the mid-2020s) Large Language Models (LLMs) have sucked all the
air out of the room when it comes to AI, so that's mostly what we're
talking about here. We do like to use the term LLM rather than AI (or
even "GenAI") to describe these systems because it's a bit clearer
what we're talking about.
Where should I start?
Start with the "Fundamentals" lessons. These cover the essential concepts for building and using language
models: training a bigram model and generating text. After completing
these lessons, you'll understand the fundamental mechanism behind
LLM-based AI systems.
Contact
If you have questions, success stories about using these materials, or
would like to discuss adaptations or improvements you've made, get in
touch at ben.swift@anu.edu.au. If you'd like to hear about what cool stuff is going on at the
School of Cybernetics, then sign up for the mailing list.
