1 citations · 4 across the 8 of their papers we have counts for
9 papers
Beyond Blind Spots: Analytic Hints for Mitigating LLM-Based Evaluation Pitfalls
Ora Nova Fandina, Eitan Farchi, Shmulik Froimovich +4
Large Language Models are increasingly deployed as judges (LaaJ) in code generation pipelines. While attractive for scalability, LaaJs tend to overlook domain specific issues raisi…
Vintage Code, Modern Judges: Meta-Validation in Low Data Regimes
Ora Nova Fandina, Gal Amram, Eitan Farchi +6
Application modernization in legacy languages such as COBOL, PL/I, and REXX faces an acute shortage of resources, both in expert availability and in high-quality human evaluation d…
Automated Validation of LLM-based Evaluators for Software Engineering Artifacts
Ora Nova Fandina, Eitan Farchi, Shmulik Froimovich +4
Automation in software engineering increasingly relies on large language models (LLMs) to generate, review, and assess code artifacts. However, establishing LLMs as reliable evalua…
Exploring Straightforward Conversational Red-Teaming
George Kour, Naama Zwerdling, Marcel Zalmanovici +3
Large language models (LLMs) are increasingly used in business dialogue systems but they pose security and ethical risks. Multi-turn conversations, where context influences the mod…
How Safe is Your Safety Metric? Automatic Concatenation Tests for Metric Reliability
Ora Nova Fandina, Leshem Choshen, Eitan Farchi +3
Consider a scenario where a harmfulness evaluation metric intended to filter unsafe responses from a Large Language Model. When applied to individual harmful prompt-response pairs,…
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…
Barriers for Faster Dimensionality Reduction
Ora Nova Fandina, Mikael Møller Høgsgaard, Kasper Green Larsen
The Johnson-Lindenstrauss transform allows one to embed a dataset of points in into while preserving the pairwise distance between any pair of po…
The Fast Johnson-Lindenstrauss Transform is Even Faster
Ora Nova Fandina, Mikael Møller Høgsgaard, Kasper Green Larsen
The seminal Fast Johnson-Lindenstrauss (Fast JL) transform by Ailon and Chazelle (SICOMP'09) embeds a set of points in -dimensional Euclidean space into optimal $k=O(\vareps…
Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures
Yair Bartal, Ora Nova Fandina, Kasper Green Larsen
It is well known that the Johnson-Lindenstrauss dimensionality reduction method is optimal for worst case distortion. While in practice many other methods and heuristics are used,…