26 citations · 46 across the 12 of their papers we have counts for
6 papers · 1 filter
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor +35
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (trai…
Classifier Data Quality: A Geometric Complexity Based Method for Automated Baseline And Insights Generation
George Kour, Marcel Zalmanovici, Orna Raz +2
Testing Machine Learning (ML) models and AI-Infused Applications (AIIAs), or systems that contain ML models, is highly challenging. In addition to the challenges of testing classic…
Automatically detecting data drift in machine learning classifiers
Samuel Ackerman, Orna Raz, Marcel Zalmanovici +1
Classifiers and other statistics-based machine learning (ML) techniques generalize, or learn, based on various statistical properties of the training data. The assumption underlyin…
Density-based interpretable hypercube region partitioning for mixed numeric and categorical data
Samuel Ackerman, Eitan Farchi, Orna Raz +2
Consider a structured dataset of features, such as . A user may want to know where in the feature space obser…
FreaAI: Automated extraction of data slices to test machine learning models
Samuel Ackerman, Orna Raz, Marcel Zalmanovici
Machine learning (ML) solutions are prevalent. However, many challenges exist in making these solutions business-grade. One major challenge is to ensure that the ML solution provid…
Machine Learning Model Drift Detection Via Weak Data Slices
Samuel Ackerman, Parijat Dube, Eitan Farchi +2
Detecting drift in performance of Machine Learning (ML) models is an acknowledged challenge. For ML models to become an integral part of business applications it is essential to de…