Model Robustness

Model robustness is the property that a model's behavior remains reliable and predictable when inputs deviate from the distribution seen during training — through natural variation, distribution shift, or deliberate adversarial manipulation. A non-robust model may perform well on standard benchmarks while failing catastrophically on slightly rephrased inputs or real-world edge cases. Robustness evaluation includes stress-testing with paraphrases, perturbations, out-of-distribution inputs, and adversarial examples.