2 citations · 3 across the 6 of their papers we have counts for
6 papers
StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs
Pierre Le Jeune, Étienne Duchesne, Weixuan Xiao +4
Multilingual studies of social bias in open-ended LLM generation remain limited: most existing benchmarks are English-centric, template-based, or restricted to recognizing pre-spec…
Phare: A Safety Probe for Large Language Models
Pierre Le Jeune, Benoît Malézieux, Weixuan Xiao +1
Ensuring the safety of large language models (LLMs) is critical for responsible deployment, yet existing evaluations often prioritize performance over identifying failure modes. We…
RealHarm: A Collection of Real-World Language Model Application Failures
Pierre Le Jeune, Jiaen Liu, Luca Rossi +1
Language model deployments in consumer-facing applications introduce numerous risks. While existing research on harms and hazards of such applications follows top-down approaches d…
Object Detection in Aerial Images in Scarce Data Regimes
Pierre Le Jeune
Most contributions on Few-Shot Object Detection (FSOD) evaluate their methods on natural images only, yet the transferability of the announced performance is not guaranteed for app…
A Comparative Attention Framework for Better Few-Shot Object Detection on Aerial Images
Pierre Le Jeune, Anissa Mokraoui
Few-Shot Object Detection (FSOD) methods are mainly designed and evaluated on natural image datasets such as Pascal VOC and MS COCO. However, it is not clear whether the best metho…
Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images
Pierre Le Jeune, Mustapha Lebbah, Anissa Mokraoui +1
This paper proposes a few-shot method based on Faster R-CNN and representation learning for object detection in aerial images. The two classification branches of Faster R-CNN are r…