Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities
Conference of the European Chapter of the Association for Computational Linguistics (to appear) 2026
I am a postdoctoral research associate and principal investigator at the University of Hamburg. My research focuses on robust reasoning, interpretability, and compositional generalization to build trustworthy AI systems. I currently lead the DFG LUMO project.
More information can be found on the Research, Teaching, and CV pages.
Research Interests
Latest News
- Mar 2026 The LUMO project has now an official website with detailed information about the project, the team, and the research. Check it out!
- Jan 2026 Our paper Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities has been accepted to EACL 2026.
- Oct 2025 Tutorial on Explaining Vision and Language Models: A Concept-Based Approach at ECAI 2025 (with Dr. Gesina Schwalbe).
Recent Publications
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Mental Modelling of Reinforcement Learning Agents by Language Models
Transactions on Machine Learning Research 2025
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Towards Ethical Multi-Agent Systems of Large Language Models: A Mechanistic Interpretability Perspective
Presented at LLM-based Multi-Agent Systems: Towards Responsible, Reliable, and Scalable Agentic Systems (LaMAS 2026) 2025
Recent Blog Posts
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Deep Learning Courses and Books for Beginners
A curated list of deep learning resources for CS students who want to understand recent research papers.
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Common LaTeX Mistakes by Newbies
A list of common LaTeX mistakes that beginners often make, with examples of correct usage.