This September 2025 paper investigates the reliability and robustness of Large Language Models (LLMs) when evaluated using traditional benchmarks. The authors systematically paraphrased questions across six common benchmarks and observed how 34 different LLMs performed. Their findings indicate that while LLM rankings remain relatively consistent, their absolute effectiveness scores significantly decline when faced with reworded questions, suggesting a lack of robustness to linguistic variability. The study highlights that current benchmark evaluations may overstate LLM generalization abilities and advocates for more robustness-aware evaluation methodologies that better reflect real-world language use.Source:https://arxiv.org/pdf/2509.04013

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