73 citations · 126 across the 15 of their papers we have counts for
5 papers · 1 filter
Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models
Can Demircan, Tankred Saanum, Akshay K. Jagadish +2
In-context learning, the ability to adapt based on a few examples in the input prompt, is a ubiquitous feature of large language models (LLMs). However, as LLMs' in-context learnin…
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…
In-context learning agents are asymmetric belief updaters
Johannes A. Schubert, Akshay K. Jagadish, Marcel Binz +1
We study the in-context learning dynamics of large language models (LLMs) using three instrumental learning tasks adapted from cognitive psychology. We find that LLMs update their…
Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks
Akshay K. Jagadish, Julian Coda-Forno, Mirko Thalmann +2
Ecological rationality refers to the notion that humans are rational agents adapted to their environment. However, testing this theory remains challenging due to two reasons: the d…
Simplifying Latent Dynamics with Softly State-Invariant World Models
Tankred Saanum, Peter Dayan, Eric Schulz
To solve control problems via model-based reasoning or planning, an agent needs to know how its actions affect the state of the world. The actions an agent has at its disposal ofte…