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Large Language Models
Last Updated: 2026-06-01 11:33:07
Abstract
Large language models have become one of the most commonly deployed NLP inventions. In the past half-decade, their integration into core natural language processing tools has dramatically increased the performance of such tools, and they have entered the public discourse surrounding artificial intelligence.
Objective
To understand the mathematical foundations of large language models as well as how to implement them.
Content
We start with the probabilistic foundations of language models, i.e., covering what constitutes a language model from a formal, theoretical perspective. We then discuss how to construct and curate training corpora, and introduce many of the neural-network architectures often used to instantiate language models at scale. The course covers aspects of systems programming, discussion of privacy and harms, as well as applications of language models in NLP and beyond.
Resources
Literature
The lecture notes will be supplemented with various readings from the literature.
Learning Materials (Links)
- Main link
- Information
- Literature
- Formal Aspects of Language Modeling
General Information
- Language
- English
- Levels
- MSC , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 180 minutes
- Aids
- Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11. A simple non-programmable calculator.
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Large Language Models |
|
3 h weekly |
| exercise | Large Language Models |
|
2 h weekly |
| independent project | Large Language Models | No time listed | 2 h weekly |
Offered In
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Wahlfächer (Von den angebotenen Wahlfächern müssen mindestens zwei Lerneinheiten erfolgreich abgeschlossen werden.)
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Anwendungsgebiet (Nur für das Master-Diplom in Angewandter Mathematik erforderlich und anrechenbar. In der Kategorie Anwendungsgebiet für den Master in Angewandter Mathematik muss eines der zur Auswahl stehenden Anwendungsgebiete gewählt werden. Im gewählten Anwendungsgebiet müssen mindestens 8 KP erworben werden. Kreditpunkte aus anderen Anwendungsgebieten sind nicht für weitere Anwendungsgebiete anrechenbar.)
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Statistik Master (Die hier aufgelisteten Lehrveranstaltungen gehören zum Curriculum des Master-Studiengangs Statistik. Die entsprechenden KP gelten nicht als Mobilitäts-KP, auch wenn gewisse Lerneinheiten nicht an der ETH Zürich belegt werden können.)
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