1 citations · 3 across the 11 of their papers we have counts for
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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…
Probing Syntax in Large Language Models: Successes and Remaining Challenges
Pablo J. Diego-Simón, Emmanuel Chemla, Jean-Rémi King +1
The syntactic structures of sentences can be readily read-out from the activations of large language models (LLMs). However, the ``structural probes'' that have been developed to r…
A Neural Model for Word Repetition
Daniel Dager, Robin Sobczyk, Emmanuel Chemla +1
It takes several years for the developing brain of a baby to fully master word repetition-the task of hearing a word and repeating it aloud. Repeating a new word, such as from a ne…
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…