2 papers
cs.LG2026
Brittlebench: Quantifying LLM robustness via prompt sensitivity
Angelika Romanou, Mark Ibrahim, Candace Ross +8
Existing evaluation methods largely rely on clean, static benchmarks, which can overestimate true model performance by failing to capture the noise and variability inherent in real…
cs.LG2025
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
Rylan Schaeffer, Joshua Kazdan, Yegor Denisov-Blanch +11
Science progresses by iteratively advancing and correcting humanity's understanding of the world. In machine learning (ML) research, rapid advancements have led to an explosion of…