7 papers
The Fairness Collapse Phenomenon: Bias Amplification in Language Models Trained on Synthetic Data
Irina Proskurina, Antoine Gourru, Julien Velcin
Generative models trained on artificially generated data have been shown to exhibit model collapse, resulting in significant performance degradation. As synthetic content increasin…
Beyond Arrow's Impossibility: Fairness as an Emergent Property of Multi-Agent Collaboration
Sayan Kumar Chaki, Antoine Gourru, Julien Velcin
Fairness in language models is typically studied as a property of a single, centrally optimized model. As large language models become increasingly agentic, we propose that fairnes…
Fair Text Classification via Transferable Representations
Thibaud Leteno, Michael Perrot, Charlotte Laclau +2
Group fairness is a central research topic in text classification, where reaching fair treatment between sensitive groups (e.g., women and men) remains an open challenge. We propos…
Are Stereotypes Leading LLMs' Zero-Shot Stance Detection ?
Anthony Dubreuil, Antoine Gourru, Christine Largeron +1
Large Language Models inherit stereotypes from their pretraining data, leading to biased behavior toward certain social groups in many Natural Language Processing tasks, such as ha…
Doing More with Less: A Survey on Routing Strategies for Resource Optimisation in Large Language Model-Based Systems
Clovis Varangot-Reille, Christophe Bouvard, Antoine Gourru +3
Large Language Model (LLM)-based systems, i.e. interconnected elements that include an LLM as a central component, such as conversational agents, are usually designed with monolith…
Capturing Style in Author and Document Representation
Enzo Terreau, Antoine Gourru, Julien Velcin
A wide range of Deep Natural Language Processing (NLP) models integrates continuous and low dimensional representations of words and documents. Surprisingly, very few models study…