1 citations · 1 across the 3 of their papers we have counts for
5 papers
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…
NeuralSet: A High-Performing Python Package for Neuro-AI
Jean-Rémi King, Corentin Bel, Linnea Evanson +25
Artificial intelligence (AI) is increasingly central to understanding how the brain processes information. However, the integration of neuroscience and modern AI is bottlenecked by…
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…
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 polar coordinate system represents syntax in large language models
Pablo Diego-Simón, Stéphane D'Ascoli, Emmanuel Chemla +2
Originally formalized with symbolic representations, syntactic trees may also be effectively represented in the activations of large language models (LLMs). Indeed, a 'Structural P…