161 citations · 305 across the 11 of their papers we have counts for
6 papers · 1 filter
Fine-tuning language models to find agreement among humans with diverse preferences
Michiel A. Bakker, Martin J. Chadwick, Hannah R. Sheahan +8
Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static…
Beyond Bayes-optimality: meta-learning what you know you don't know
Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch +11
Meta-training agents with memory has been shown to culminate in Bayes-optimal agents, which casts Bayes-optimality as the implicit solution to a numerical optimization problem rath…
Continual task learning in natural and artificial agents
Timo Flesch, Andrew Saxe, Christopher Summerfield
How do humans and other animals learn new tasks? A wave of brain recording studies has investigated how neural representations change during task learning, with a focus on how task…
Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals
Timo Flesch, David G. Nagy, Andrew Saxe +1
Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep…
The Good Shepherd: An Oracle Agent for Mechanism Design
Jan Balaguer, Raphael Koster, Christopher Summerfield +1
From social networks to traffic routing, artificial learning agents are playing a central role in modern institutions. We must therefore understand how to leverage these systems to…
HCMD-zero: Learning Value Aligned Mechanisms from Data
Jan Balaguer, Raphael Koster, Ari Weinstein +4
Artificial learning agents are mediating a larger and larger number of interactions among humans, firms, and organizations, and the intersection between mechanism design and machin…