Dirk Wulff

The course introduces the use of open-source large language models (LLMs) from the Hugging Face ecosystem for research in the behavioral and social sciences. In short lectures, participants will learn about key concepts (e.g., embeddings, causal attention, feature extraction, classification, prediction, fine-tuning, and token generation) and practical examples from social and behavioral science. In hands-on exercises, participants will apply language models to answer research questions from psychology, political science, decision-making research, and other fields. During and after the course, participants will engage in a personal research project applying LLMs to a personal research question. Two lecturers will hold this course, implying a high level of support during the exercises and research project design.

LLMs (think ChatGPT) are incredibly useful tools for research in the social and behavioral sciences. In this course, you will (1) learn about the fundamental principles of LLMs, (2) learn how to employ open-source LLMs using the Hugging Face ecosystem, (3) learn about the rich opportunities that LLMs offer for behavioral and social science research, and (4) gain experience in applying LLMs to answer personal research questions.

In today’s data-driven world, the ability to effectively tell stories with data is essential for success in science and business. Data storytelling goes beyond visualizing numbers—it’s about crafting compelling narratives that resonate with your audience, guiding them through insights, and helping them make informed decisions. This course focuses on equipping participants with the skills to create impactful data stories–using the programming language R–that combine powerful visualization techniques with research or business context. By integrating principles of psychology, design, and communication, participants will learn how to translate data into clear, engaging narratives that drive action.