Scholar
Michael Franke
Google Scholar ID: xTP7D3YAAAAJ
University of Tübingen
Citations & Impact
Citations
1,711
H-index
22
i10-index
46
Publications
20
Co-authors
9
list available
Publications
Resume (English only)
Academic Achievements
- 2025: Relevant answers to polar questions. In: Philosophical Transactions of the Royal Society B.
- 2025: Non-literal Understanding of Number Words by Language Models. Proceedings of CogSci 47.
- 2025: Integrating Neural and Symbolic Components in a Model of Pragmatic Question-Answering. Society for Computation in Linguistics 8(1).
- 2025: The Alignment Model of Indirect Communication. PLoS One 20(5): e0323839.
- 2024: Deep and shallow thinking in a single forward pass. NeurIPS 2024 Workshop on Behavioral Machine Learning.
- 2024: Strategic use of English quantifiers in the reporting of quantitative information. Discourse Processes.
- 2024: Cognitive Modeling with Scaffolded LLMs: A Case Study of Referential Expression Generation. arXiv.
- 2024: Bayesian Statistical Modeling with Predictors from LLMs. arXiv.
- 2024: Latent meaning representations in great-ape gestural communication. In: Proceedings of CogSci.
- 2024: The rationality of inferring causation from correlational language. In: Proceedings of CogSci.
- 2024: Experimental Pragmatics with Machines: Testing LLM Predictions for the Inferences of Plain and Embedded Disjunctions. In: Proceedings of CogSci.
- 2024: Predictions from language models for multiple-choice tasks are not robust under variation of scoring methods. arXiv.
Research Experience
- Currently, he is the coordinator of the DFG-funded Priority Area LaSTing and a co-speaker of the DFG-funded CRC Common Ground.
Background
- A computational cognitive scientist, appointed as Professor of Pragmatics and Linguistics and coopted as a Professor of Computer Science at the University of Tübingen. Broadly interested in reasoning and language use in humans and machines. His work uses statistical, cognitive, and evolutionary modeling, human-experimental methods, and simulation experiments. He is also interested in foundational questions concerning the relation between empirical data, abstract theory, and explicit mathematical modeling.
Miscellany
- Provides a range of research and teaching resources, including scripts for the course Introduction to Logic, a web-book for the course Understanding Large Language Models, a framework for web-based experiments (magpie), an R package for interpreting categorical predictors in Bayesian regression (faintr), a method for writing reproducible research papers in pure LaTeX, web-books for courses Introduction to Data Analysis and Bayesian Regression: Theory & Practice, tutorial papers on Bayesian data analysis (with Timo B. Roettger) and softmax in probabilistic modeling (with Judith Degen), and a web-book on Probabilistic Pragmatics (with Gregory Scontras and Michael Henry Tessler).
