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263-5354-00L 8 Credits MSC , WBZ D-ITET , D-INFK , D-MATH

Large Language Models

Lecturers & Examiners: Prof. Dr. Mrinmaya Sachan, Prof. Dr. Ryan Cotterell, Prof. Dr. Florian Tramèr
VVZ CR 3.63

Last Updated: 2026-06-03 00:14:10

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.

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.
The exam will constitute 50% of the final grade. The remaining 50% will be based on several assignments released during the semester.

Course Components

Type Title Time & Place Hours
lecture Large Language Models
Tuesday: course is held in HG E7 and will be transferred via videoconference to HG E3.
  • Tue 14:15-16:00 (HG E 3)
  • Tue 14:15-16:00 (HG E 7)
  • Fri 10:15-11:00 (ETF E 1)
3 h weekly
exercise Large Language Models
  • Thu 16:15-18:00 (NO C 60)
2 h weekly
independent project Large Language Models No time listed 2 h weekly

Offered In