Stanford HELM

Define custom support ticket dataset.

Run HELM on all three models using the custom scenario.

Compare accuracy vs. cost metrics in HELM dashboard.

Select HELM's 'Fairness' and 'Bias' scenarios.

Execute runs across demographic-sensitive prompts.

Generate a PDF report of fairness coefficients for regulators.

Run HELM benchmarks on the full-precision model.

Run the same suite on the 4-bit quantized version.

Analyze the 'reasoning' delta in the HELM summarizer.

Enable the 'Perturbation' metrics in HELM.

Apply character-level and synonym-level noise to inputs.

Observe the degradation in model performance.

Deploy the Medical QA scenario set within HELM.

Calculate the calibration score to see if the model is overconfident in wrong answers.

Adjust system prompts and re-test.

Use the 'Language Schema' metrics.

Test against diverse JSON structures.

Evaluate the success rate of syntax-valid outputs.

Utilize the contamination detection suite.

Search for exact n-gram overlaps between model training logs and test sets.

Adjust confidence scores based on overlap data.