1 citations · 2 across the 2 of their papers we have counts for
11 papers
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…
Polar probe linearly decodes semantic structures from LLMs
Pablo J. Diego-Simón, Pierre Orhan, Emmanuel Chemla +2
How do artificial neural networks bind concepts to form complex semantic structures? Here, we propose a simple neural code, whereby the existence and the type of relations between…
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…
DiscoPhon: Benchmarking the Unsupervised Discovery of Phoneme Inventories With Discrete Speech Units
Maxime Poli, Manel Khentout, Angelo Ortiz Tandazo +3
We introduce DiscoPhon, a multilingual benchmark for evaluating unsupervised phoneme discovery from discrete speech units. DiscoPhon covers 6 dev and 6 test languages, chosen to sp…
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…
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…