1 citations · 1 across the 9 of their papers we have counts for
10 papers
Comment on "Modeling rapid language learning by distilling Bayesian priors into artificial neural networks"
Orr Well, Idan Tarshish, Nur Lan +1
McCoy & Griffiths (2025, henceforth M&G) suggest that a Bayesian prior can be distilled into Artificial Neural Networks (ANNs) through Model-Agnostic Meta-Learning (MAML, Finn et a…
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…
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…
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…
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…
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…
Minimum Description Length Hopfield Networks
Matan Abudy, Nur Lan, Emmanuel Chemla +1
Associative memory architectures are designed for memorization but also offer, through their retrieval method, a form of generalization to unseen inputs: stored memories can be see…
Benchmarking Neural Network Generalization for Grammar Induction
Nur Lan, Emmanuel Chemla, Roni Katzir
How well do neural networks generalize? Even for grammar induction tasks, where the target generalization is fully known, previous works have left the question open, testing very l…
Minimum Description Length Recurrent Neural Networks
Nur Lan, Michal Geyer, Emmanuel Chemla +1
We train neural networks to optimize a Minimum Description Length score, i.e., to balance between the complexity of the network and its accuracy at a task. We show that networks op…
On the Spontaneous Emergence of Discrete and Compositional Signals
Nur Geffen Lan, Emmanuel Chemla, Shane Steinert-Threlkeld
We propose a general framework to study language emergence through signaling games with neural agents. Using a continuous latent space, we are able to (i) train using backpropagati…