1 citations · 1 across the 8 of their papers we have counts for
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Debias-SparseGPT: Bias-Aware Pruning for Large Language Models
Irina Proskurina, Guillaume Metzler, Antoine Gourru +1
Model compression techniques such as pruning and quantization facilitate the efficient deployment and acceleration of Large Language Models (LLMs). However, recent studies show tha…
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…