arXiv AI By Nagham Omar, Mahmoud Jabarin, Maya Rozenshtein, Rom Himelstein, Avi Mendelson, Amit LeVi

Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs

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The Illusion of Robustness: Aggregate Accuracy Hides Prediction Flips under Task-Irrelevant Context

As large language models (LLMs) grow more capable, they are increasingly deployed in context-rich settings where task inputs are often accompanied by long, partially irrelevant context. In a controlled setting, we find that state-of-the-art models often appear robust to task-irrelevant context at the aggregate level: prepending it to benchmark questions causes little change in overall accuracy.

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Contrastive Weak-to-strong Generalization

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