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cs.CL2025
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.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…
cs.CL2025
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…