Proseminar/Seminar: LLM Research Assistants

This combined Proseminar/Seminar explores the role of Large Language Models (LLMs) as research assistants. It is open to both Bachelor students (as Proseminar) and Master students (as Seminar). Students work in groups of two to investigate a specific use case of LLMs in the research process, including applications such as literature search, transforming papers into presentations, idea generation, or peer review. Each group will produce a written report, peer review another group’s report, and give an oral presentation.

By the end of the seminar, students will gain a solid understanding of LLMs, including their core mechanisms, capabilities, and limitations. They will become familiar with how LLMs are used to support various stages of the scientific research process and how these models can assist in AI for Science applications such as automating research tasks, accelerating scientific research, and enhancing collaboration. The seminar encourages students to develop a critical perspective on the reliability, interpretability, and ethical implications of using LLMs as research assistants, while also strengthening students’ understanding of how to responsibly integrate such tools into academic workflows.

• Course number: ML-4507
• Credits: 3 ECTS (2h)
• Total Workload: 90h
• Format: In-person
• Attendance: Mandatory for all sessions
   

• Report (5-6 pages, double column, excluding references)
• Presentation (25-30 minutes, max. 20 slides)
• Review of another report (1 page, double column)
• Discussion (during all presentations)

• Literature Search with LLMs: Efficiency, Accuracy, and Risks
• Summarizing Papers and Translating into Slide Decks or Talks
• LLMs in Generating Research Hypotheses or Project Ideas
• Peer Review Assistance: Quality Evaluation and Bias Analysis
• Using LLMs for Data Annotation and Dataset CurationPrompt Engineering for Academic Workflows
• Ethical Considerations and Hallucination Risks in LLM-Assisted Research

• Basic Computer Science skills: Variables, functions, loops, classes, algorithms
• Basic Math skills: Linear algebra, analysis, probability theory
• Basic knowledge of Large Language Models is beneficial, but not required
• Basic knowledge of NLP and AI is beneficial, but not required

• To participate in this seminar, you must register in ILIAS

Links to Latex/Overleaf templates for reports, reviews and slides. Reports and reviews must use the corresponding template. Presentation slides can be done with other tools, e.g., PowerPoint, Keynote.

• Report template: https://www.overleaf.com/read/cwjmyfnhjmgh 
• Review template: https://www.overleaf.com/read/xqfbmcpqqddq
• Slide template: https://www.overleaf.com/read/wxxwtgbrgcbm