most citedWhat Makes Two Language Models Think Alike?

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

quant-ph2026

Homomorphic Aggregation of Continuous-Variable GKP States

Nilesh Vyas

Aggregating logical information in continuous-variable quantum networks is essential for distributed quantum architecture. However, direct passive linear optics degrade non-Gaussia…

cs.CL20261 cited

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…

cs.CL2026

Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from the Impossible

Imry Ziv, Nur Lan, Emmanuel Chemla

Are large language models (LLMs) sensitive to the distinction between humanly possible and impossible languages? This question was recently used in a broader debate on whether LLMs…

cs.CL2026

Large Language Models as Proxies for Theories of Human Linguistic Cognition

Imry Ziv, Nur Lan, Emmanuel Chemla +1

We consider the possible role of current large language models (LLMs) in the study of human linguistic cognition. We focus on the use of such models as proxies for theories of cogn…

cs.LG2025

A Minimum Description Length Approach to Regularization in Neural Networks

Matan Abudy, Orr Well, Emmanuel Chemla +2

State-of-the-art neural networks can be trained to become remarkable solutions to many problems. But while these architectures can express symbolic, perfect solutions, trained mode…