5 papers
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
Reference-Free Rating of LLM Responses via Latent Information
Leander Girrbach, Chi-Ping Su, Tankred Saanum +3
How reliable are single-response LLM-as-a-judge ratings without references, and can we obtain fine-grained, deterministic scores in this setting? We study the common practice of as…
A circuit for predicting hierarchical structure in-context in Large Language Models
Tankred Saanum, Can Demircan, Samuel J. Gershman +1
Large Language Models (LLMs) excel at in-context learning, the ability to use information provided as context to improve prediction of future tokens. Induction heads have been argu…
Centaur: a foundation model of human cognition
Marcel Binz, Elif Akata, Matthias Bethge +37
Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational model…
Evaluating alignment between humans and neural network representations in image-based learning tasks
Can Demircan, Tankred Saanum, Leonardo Pettini +5
Humans represent scenes and objects in rich feature spaces, carrying information that allows us to generalise about category memberships and abstract functions with few examples. W…