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S15 · Open
Session 15 · From next-token prediction to assistants
Live board · deepen in matheion MA-LLM · Session 15
Today’s story Same Transformer stack at every stage — change the data (and a bit of training) so a next-token machine behaves like a helpful assistant.
You should leave able to… • Name pre-train / fine-tune / align • Explain in-context examples vs weight updates • State two limits that follow from the mechanism
With matheion Use this board to teach. Open matheion → MA-LLM → Session 15 for full prose, diagrams, and the auto-quiz.
S15 · Stages
Same stack, new data
1 · Pre-train Next-token prediction on huge text corpora. Learns language, facts, patterns — not yet “be a helpful assistant”.
2 · Fine-tune Show (prompt → good answer) pairs. Teaches the format of assistance on top of the same architecture.
3 · Align Steer with human (or AI) preferences: prefer helpful / harmless / honest replies over worse ones. Still next-token underneath.
S15 · In context
Examples inside the prompt
What it is Put worked examples in the prompt. The model steers via attention — weights in the network stay frozen.
What it isn’t Not permanent learning. Clear the prompt, and those “lessons” vanish. Long prompts also cost more (Session 14’s quadratic bill).
S15 · Limits
Limits the mechanism imposes
Fluent ≠ true A sharp next-token lottery can sound confident while wrong. That’s hallucination — not a calibrated truth oracle.
Context window Only the tokens inside the window are visible to attention. Outside = invisible.
Seminar arc Tokens → lottery → soft lookup → stack → product. You can now place every piece on that arc.
S15 · Glossary
Glossary · key concepts this session
Keep this frame visible while teaching · say the term, then the plain line
Pre-training Next-token learning on huge raw text — language and patterns, not ‘assistant’ yet.
Fine-tuning Train on (prompt → good answer) pairs to teach helpful formats.
Alignment Steer toward preferred behaviour (helpful / harmless / honest) still via next-token training.
In-context learning Examples in the prompt steer attention; network weights stay frozen.
Hallucination Fluent, confident next-token output that isn’t true.
Context window limit Tokens outside the window are invisible to attention.
S15 · Check
Exit ticket
Say these aloud · then quiz in matheion MA-LLM · Session 15
Prompt 1 Pre-train vs fine-tune vs align in one line each.
Prompt 2 In-prompt examples: do weights change?
Prompt 3 Name two failure modes that follow from next-token lotteries.