50 citations · 65 across the 11 of their papers we have counts for
16 papers
Dolph2Vec: Self-Supervised Representations of Dolphin Vocalizations
Chiara Semenzin, Faadil Mustun, Roberto Dessi +5
Self-supervised learning (SSL) has opened new opportunities in bioacoustics by enabling scalable modeling of animal vocalizations without the need for expensive manual annotation.…
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…
Emergence of Phonemic, Syntactic, and Semantic Representations in Artificial Neural Networks
Pierre Orhan, Pablo Diego-Simón, Emmnanuel Chemla +3
During language acquisition, children successively learn to categorize phonemes, identify words, and combine them with syntax to form new meaning. While the development of this beh…
MapFormer: Self-Supervised Learning of Cognitive Maps with Input-Dependent Positional Embeddings
Victor Rambaud, Salvador Mascarenhas, Yair Lakretz
A cognitive map is an internal model which encodes the abstract relationships among entities in the world, giving humans and animals the flexibility to adapt to new situations, wit…
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…