54 citations · 113 across the 10 of their papers we have counts for
6 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…
The Illusion of Latent Generalization: Bi-directionality and the Reversal Curse
Julian Coda-Forno, Jane X. Wang, Arslan Chaudhry
The reversal curse describes a failure of autoregressive language models to retrieve a fact in reverse order (e.g., training on ``'' but failing on ``''). Recent work…
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…
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…
Playing repeated games with Large Language Models
Elif Akata, Lion Schulz, Julian Coda-Forno +3
LLMs are increasingly used in applications where they interact with humans and other agents. We propose to use behavioural game theory to study LLM's cooperation and coordination b…
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…