Large Language Models and Agents (FSS2025)
Large language models (LLMs) such as ChatGPT, Claude, Llama, Gemini, and Mistral have the potential to enable a wide range of new applications and to significantly improve the performance of existing systems. The course introduces students to LLMs and teaches them how to employ the models within applications.
The course covers the following topics:
- Introduction to LLMs
- Instruction tuning and reinforcement learning from human feedback
- Prompt engineering and efficient adaptation
- LLM-based agents
- Evaluation of LLMs and agents
- Development of LLM-based applications
The course participants gain knowledge about the principles of training LLMs. They will be able to identify opportunities for employing LLMs in business applications and will learn to apply prompt engineering techniques as well as agent frameworks for solving complex tasks. In the second half of the course, the participants apply their knowledge in team projects and will report about the results of the projects in the form of a written report as well as an oral presentation.
Time and Location
- Thursdays, 15:30–17:00. Location: B6 A101 (Starting: 13.02.2025)
Assessment
In the second half of the course, students will work on applied projects in teams. The grade received for this course is based on the quality of the contents of their project report (70%) and the associated presentation (30%). There is no written exam for this course.
ECTS
- 3 ECTS: 70 % project report, 30 % presentation
Requirements
- Programming skills in Python
- Basic machine learning concepts and techniques
Registration and Participation
- The course is open to students of the Mannheim Master in Data Science, the Mannheim Master in Social Data Science, and Master Business Informatics.
- The course is restricted to 60 participants. The registration for the course is organized by the Studiengang Management and is done via Portal2.
Outline and Course Material
Day | Topic | Additional Material |
---|---|---|
13.02.2025 | Lecture: Introduction to Language Models | |
20.02.2025 | Lecture: Instruction Tuning and RLHF | |
27.02.2025 | Lecture: Prompt Engineering and Efficient Adaptation | |
06.03.2025 | Exercise: Introduction to LangGraph 1 | |
13.03.2025 | Lecture: LLM Agents and Tool Use | |
20.03.2025 | Exercise: Introduction to LangGraph 2 | |
27.03.2025 | Project: Introduction to Student Projects | |
03.04.2025 | Exercise: Introduction to AutoGen | |
10.04.2025 | Project Coaching | |
30.04.2025 | Project Coaching | |
08.05.2025 | Project Coaching | |
15.05.2025 | Project Coaching | |
22.05.2025 | Project Coaching | |
28.05.2025 | Presentation of Project Results |
Literature
- Daniel Jurafsky & James H. Martin: Speech and Language Processing.
- Zhao et al.: A Survey of Large Language Models, 2024, arXiv:2303.18223.
- Wang et al.: A Survey on Large Language Model Based Autonomous Agents, 2024, Frontiers of Computer Science.
- Zhou et al.: A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT, 2024, International Journal of Machine Learning and Cybernetics.