Techniques For Improving Model Reliability And Robustness

via “robustness evaluation via adversarial and distribution-shifted inputs”

Stanford's holistic LLM evaluation — 42 scenarios, 7 metrics including fairness, bias, toxicity.

Unique: Embeds robustness testing into the core evaluation loop by generating multiple perturbed versions of each scenario (typos, paraphrases, out-of-distribution examples) and measuring accuracy degradation. Treats robustness as a first-class metric alongside accuracy rather than a post-hoc analysis.

vs others: More systematic than ad-hoc robustness testing because it applies consistent perturbation strategies across all 42 scenarios, enabling fair comparison of robustness profiles across models

Examples and guides for using the OpenAI API.

via “model-stability-and-robustness-testing”

via “model-robustness-assessment”

via “model-hardening-guidance”

via “model-performance-and-robustness-testing”