26 citations · 50 across the 24 of their papers we have counts for
3 papers · 1 filter
Unveiling Safety Vulnerabilities of Large Language Models
George Kour, Marcel Zalmanovici, Naama Zwerdling +5
As large language models become more prevalent, their possible harmful or inappropriate responses are a cause for concern. This paper introduces a unique dataset containing adversa…
Predicting Question-Answering Performance of Large Language Models through Semantic Consistency
Ella Rabinovich, Samuel Ackerman, Orna Raz +2
Semantic consistency of a language model is broadly defined as the model's ability to produce semantically-equivalent outputs, given semantically-equivalent inputs. We address the…
Automatic Generation of Attention Rules For Containment of Machine Learning Model Errors
Samuel Ackerman, Axel Bendavid, Eitan Farchi +1
Machine learning (ML) solutions are prevalent in many applications. However, many challenges exist in making these solutions business-grade. For instance, maintaining the error rat…