Unit 4: Alignment and current research
In this unit, you will learn more about the alignment stage of LLM training. You will also see several examples of current research in this and related areas. The unit features both lecture-style reviews of recent developments and videos from research presentations.
Lectures
The lectures start by exploring LLMs alignment. You will then learn about current research on how LLMs store facts, efficient fine-tuning techniques, retrieval-augmented generation, and how tokenisation relates to LLM privacy and security. The series concludes by reflecting on the “stochastic parrots” debate.
| Section | Title | Video | Slides | Quiz |
|---|---|---|---|---|
| 4.1 | LLM alignment | video | slides | quiz |
| 4.2 | LLMs for fact completion | video | slides | quiz |
| 4.3 | Efficient fine-tuning | video | paper | quiz |
| 4.4 | Retrieval-augmented generation (until 17:50) | video | none | quiz |
| 4.5 | Adversarial tokenization | video | slides | quiz |
| 4.6 | LLMs as stochastic parrots (until 15:00) | video | none | quiz |
To earn a wildcard for this unit, you must complete the quizzes before the teaching session on Unit 4.
Online meeting
In the online meeting, we will discuss the issue of evaluation of large language models. What methods do developers and researchers use to measure their performance? We will also discuss one specific benchmark that has been proposed in this area, “Humanity’s Last Exam”.
The meeting will take place on 2026-04-22 between 18:00–20:00. A Zoom link will be sent out via the course mailing list.
Additional materials
Lab
In this lab, you will implement LoRA, one of the most well-known methods for parameter-efficient fine-tuning of large language models. Along the way, you will earn experience with Hugging Face Transformers, a state-of-the-art library for training and deploying language models, as well as with several related libraries.