Summary and Revision Notes

Core ideas

• LLM pretraining is rooted in language modeling.
• Next-token prediction gives broad learning pressure.
• Scale changes capability, but mechanism still matters.
• Data quality shapes model behavior.
• Benchmarks are evidence with limits.
• Likely continuation is not the same as verified truth.

Check yourself

• Can you connect a modern LLM to next-token prediction?
• Can you explain why data quality matters?
• Can you explain hallucination using the language-modeling view?