1 citations · 3 across the 11 of their papers we have counts for
5 papers · 1 filter
Disentanglement and Compositionality of Letter Identity and Letter Position in Variational Auto-Encoder Vision Models
Bruno Bianchi, Aakash Agrawal, Stanislas Dehaene +2
Human readers can accurately count how many letters are in a word (e.g., 7 in ``buffalo''), remove a letter from a given position (e.g., ``bufflo'') or add a new one. The human bra…
A polar coordinate system represents syntax in large language models
Pablo Diego-Simón, Stéphane D'Ascoli, Emmanuel Chemla +2
Originally formalized with symbolic representations, syntactic trees may also be effectively represented in the activations of large language models (LLMs). Indeed, a 'Structural P…
What Makes Two Language Models Think Alike?
Louis Jalouzot, Christophe Pallier, Emmanuel Chemla +1
Do architectural and training differences influence the way models represent and process language? Traditional similarity metrics tell us whether two models share a similar represe…
Metric Learning Encoding Models: A Multivariate Framework for Interpreting Neural Representations
Louis Jalouzot, Christophe Pallier, Emmanuel Chemla +1
Understanding how explicit theoretical features are encoded in opaque neural systems is a central challenge now common to neuroscience and AI. We introduce Metric Learning Encoding…
Bridging the Empirical-Theoretical Gap in Neural Network Formal Language Learning Using Minimum Description Length
Nur Lan, Emmanuel Chemla, Roni Katzir
Neural networks offer good approximation to many tasks but consistently fail to reach perfect generalization, even when theoretical work shows that such perfect solutions can be ex…