Editing Ai Evaluation (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a comprehensive AI evaluation system:

'''1. Evaluation taxonomy'''
<syntaxhighlight lang="text">
Capability Evaluation
├── Knowledge: MMLU, TriviaQA
├── Reasoning: GSM8K, MATH, ARC
├── Language: HellaSwag, WinoGrande
├── Code: HumanEval, SWE-bench
└── Instruction following: IFEval, MT-Bench

Safety Evaluation
├── Toxicity: Perspective API, HarmBench
├── Bias: BBQ, WinoBias
├── Truthfulness: TruthfulQA
├── Robustness: adversarial prompt test sets
└── Over-refusal: XSTest (does model refuse safe requests?)
</syntaxhighlight>

'''2. Evaluation infrastructure'''
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Model checkpoint
    ↓
[Automated eval pipeline: lm-eval-harness]
    ↓
[Results stored: scores + full outputs per example]
    ↓
[Dashboard: track scores over training time]
    ↓
[Regression alerts: score drops > threshold]
    ↓
[Human eval queue: route outputs for human review]
    ↓
[Aggregated report: capability + safety scorecard]
</syntaxhighlight>

'''3. Living benchmark maintenance'''
* Regularly audit benchmarks for contamination
* Retire saturated benchmarks; add harder successors
* Maintain private holdout sets never released publicly
* Collect failure cases from production for evaluation dataset augmentation

[[Category:Artificial Intelligence]]
[[Category:Machine Learning]]
[[Category:AI Evaluation]]
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