Eric SCHULZ – 2 novembre 2023
Titre : Machine Psychology
Large language models are on the cusp of transforming society while they permeate into many applications. Understanding how they work is, therefore, of great value. We propose to use insights and tools from psychology to study and better understand these models. Psychology can add to our understanding of LLMs and provide a new toolkit for explaining LLMs by providing theoretical concepts, experimental designs, and computational analysis approaches. This can lead to a machine psychology for foundation models that focuses on computational insights and precise experimental comparisons instead of performance measures alone. I will showcase the utility of this approach by showing how current LLMs behave across a variety of cognitive tasks, as well as how one can make them more human-like by fine-tuning on psychological data directly.
Eric SCHULZ, Max-Planck Research Group Leader, Tuebingen University works on the building blocks of intelligence using a mixture of computational, cognitive, and neuroscientific methods. He has worked with Maarten Speekenbrink on generalization as function learning and Sam Gershman and Josh Tenenbaum.
Binz, M., & Schulz, E. (2023). Using cognitive psychology to understand GPT-3. Proceedings of the National Academy of Sciences, 120(6), e2218523120
Akata, E., Schulz, L., Coda-Forno, J., Oh, S. J., Bethge, M., & Schulz, E. (2023). Playing repeated games with Large Language Models. arXiv preprint arXiv:2305.16867.
Allen, K. R., Brändle, F., Botvinick, M., Fan, J., Gershman, S. J., Griffiths, T. L., ... & Schulz, E. (2023). Using Games to Understand the Mind
Binz, M., & Schulz, E. (2023). Turning large language models into cognitive models. arXiv preprint.