501 citations · 1k across the 35 of their papers we have counts for
8 papers · 1 filter
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…
CogBench: a large language model walks into a psychology lab
Julian Coda-Forno, Marcel Binz, Jane X. Wang +1
Large language models (LLMs) have significantly advanced the field of artificial intelligence. Yet, evaluating them comprehensively remains challenging. We argue that this is partl…
How should the advent of large language models affect the practice of science?
Marcel Binz, Stephan Alaniz, Adina Roskies +15
Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the…
Turning large language models into cognitive models
Marcel Binz, Eric Schulz
Large language models are powerful systems that excel at many tasks, ranging from translation to mathematical reasoning. Yet, at the same time, these models often show unhuman-like…
Meta-in-context learning in large language models
Julian Coda-Forno, Marcel Binz, Zeynep Akata +3
Large language models have shown tremendous performance in a variety of tasks. In-context learning -- the ability to improve at a task after being provided with a number of demonst…
Inducing anxiety in large language models can induce bias
Julian Coda-Forno, Kristin Witte, Akshay K. Jagadish +3
Large language models (LLMs) are transforming research on machine learning while galvanizing public debates. Understanding not only when these models work well and succeed but also…