Chapter 16
Language Modeling as a Foundation for LLMs
Pretraining objective, scale, data quality, benchmarks, hallucination as model behavior, and why transformers became the default architecture.
What this chapter does
This chapter connects small language models to modern LLMs by keeping scale, data, benchmarks, and hallucination tied to the modeling setup.
Lessons
Read these in order.
Start with the chapter introduction, then move through the topic lessons. The order is chosen so each page can reuse ideas from the pages before it.
- 01Introduction
Connecting small language-modeling ideas to modern LLMs.
- 02Pretraining Objective
Next-token prediction as the broad pretraining signal.
- 03What Scale Changes
Scale changes capability, but mechanism still matters.
- 04Data Quality
Training data as part of model behavior.
- 05Benchmarks and Limits
Reading benchmark scores as useful but bounded evidence.
- 06Hallucination and Model Behavior
Why likely continuation is not the same as verified truth.
Review and practice
Close the chapter deliberately.
Use the conclusion and revision notes before the chapter exercises. Hints and solutions are collected here, while lesson-level exercises reveal their own help inline.
Closing the Language Modeling path before Transformers.
Summary and Revision NotesA compact review of pretraining, scale, data, benchmarks, hallucination, and the LLM bridge.
ExercisesChapter-level practice for connecting language modeling to LLM behavior.
HintsLow-spoiler nudges for the Chapter 16 exercises.
SolutionsExplained solutions for the Chapter 16 exercises.
Before moving on
- Describe the bridge from small language models to transformer language models.
- Explain why scale changes behavior without changing the basic objective.
- Write a short model card for a tiny language model.
Where this leads
- Transformers
- Generative Models
- RL Foundations