4 citations · 4 across the 3 of their papers we have counts for
4 papers
Semantic Uncertainty Quantification Needs Factual Equivalence
Joseph Hoche, Quentin Guimard, Gianni Franchi
Semantic uncertainty quantification for large language models rests on a common template: sample several answers, measure how much they agree, and treat disagreement as uncertainty…
SEM: Sparse Embedding Modulation for Post-Hoc Debiasing of Vision-Language Models
Quentin Guimard, Federico Bartsch, Simone Caldarella +3
Models that bridge vision and language, such as CLIP, are key components of multimodal AI, yet their large-scale, uncurated training data introduce severe social and spurious biase…
Classifier-to-Bias: Toward Unsupervised Automatic Bias Detection for Visual Classifiers
Quentin Guimard, Moreno D'Incà, Massimiliano Mancini +1
A person downloading a pre-trained model from the web should be aware of its biases. Existing approaches for bias identification rely on datasets containing labels for the task of…
Early Detection of Security-Relevant Bug Reports using Machine Learning: How Far Are We?
Arthur D. Sawadogo, Quentin Guimard, Tegawendé F. Bissyandé +3
Bug reports are common artefacts in software development. They serve as the main channel for users to communicate to developers information about the issues that they encounter whe…