4 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.SE2025
Risk Management for Mitigating Benchmark Failure Modes: BenchRisk
Sean McGregor, Victor Lu, Vassil Tashev +8
Large language model (LLM) benchmarks inform LLM use decisions (e.g., "is this LLM safe to deploy for my use case and context?"). However, benchmarks may be rendered unreliable by…
cs.CY2025★ 4 cited
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…
cs.LG2024★ 1 cited
Benchmark Inflation: Revealing LLM Performance Gaps Using Retro-Holdouts
Jacob Haimes, Cenny Wenner, Kunvar Thaman +4
The training data for many Large Language Models (LLMs) is contaminated with test data. This means that public benchmarks used to assess LLMs are compromised, suggesting a performa…