133 citations · 365 across the 73 of their papers we have counts for
4 papers · 1 filter
When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions
Maya Larbi, Amal Akli, Mike Papadakis +4
Large Language Models (LLMs) have demonstrated impressive performance in code generation tasks under idealized conditions, where task descriptions are clear and precise. However, i…
On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
Mohamed Djilani, Thibault Simonetto, Karim Tit +4
Recent tabular Foundational Models (FM) such as TabPFN and TabICL, leverage in-context learning to achieve strong performance without gradient updates or fine-tuning. However, thei…
HInter: Exposing Hidden Intersectional Bias in Large Language Models
Badr Souani, Ezekiel Soremekun, Mike Papadakis +3
Large Language Models (LLMs) may portray discrimination towards certain individuals, especially those characterized by multiple attributes (aka intersectional bias). Discovering in…
Where Linux Breaks Under Radiation: A Cross-Architecture Kernel-Level Characterization of Proton-Induced Failures in COTS SoCs
Saad Memon, Rafal Graczyk, Tomasz Rajkowski +5
Linux is increasingly deployed in Low Earth Orbit on commercial off the shelf systems on chip that were not designed for space radiation. Ionizing particles can trigger single even…