Par Michael Pichat, William Pogrund, Paloma Pichat, Armanouche Gasparian, Samuel Demarchi, Martin Corbet, Alois Georgeon, Théo Dasilva et Michael Veillet-Guillem.
Article publié au sein de la revue en ligne Arxiv (Cornell University) le 25 février 2025.
Abstract
How do language models segment their internal experience of the world of words to progressively learn to interact with it more efficiently ? This study in the neuropsychology of artificial intelligence investigates the phenomenon of synthetic categorical restructuring — a process through which each successive perceptron neural layer abstracts and combines relevant categorical sub-dimensions from the thought categories of its previous layer. This process shapes new, even more efficient categories for analyzing and processing the synthetic system’s own experience of the linguistic external world to which it is exposed. Our genetic neuron viewer, associated with this study, allows visualization of the synthetic categorical restructuring phenomenon occurring during the transition from perceptron layer 0 to 1 in GPT2-XL.
DOI : https://doi.org/10.48550/arXiv.2503.10643
Michael Pichat est MCF en psychologie, fondateur du Cabinet Chrysippe-R&D et de Neocognition, co-directeur Diplômes « Management & Coaching » Université Paris Dauphine & Cabinet Chrysippe-R&D et membre titulaire de l’ER IPC.
