Model Evaluation Frameworks: Beyond Accuracy Metrics
Choosing the right evaluation metrics determines what your model optimizes for. Explore frameworks for measuring reasoning, safety, style, and user satisfaction.
Task-specific benchmarks
Generic benchmarks (MMLU, HumanEval) measure broad capability but don't predict performance on your product's tasks. Build custom evaluation sets that mirror real usage.
Include adversarial examples, edge cases, and failure modes you've seen in production. Automate evaluation and run it on every model change.
Beyond accuracy: measuring what users care about
Users don't experience 'accuracy'—they experience helpfulness, safety, and appropriate tone. Define metrics that capture these: task success rate, user satisfaction (thumbs up/down), and escalation rate.
Track refusal rate (when the model appropriately declines) separately from error rate (when the model fails incorrectly). Both matter, but they're different failure modes.
Building a representative evaluation set
Sample from production logs to ensure your evaluation reflects real usage. Weight by frequency: common queries should have more examples than rare ones.
But also include rare edge cases deliberately. A production-weighted sample might miss critical safety scenarios that occur infrequently.
Automatic vs human evaluation
Automatic metrics (exact match, BLEU, ROUGE) are fast and reproducible but don't capture nuance. Human evaluation is expensive and has inter-rater disagreement.
Use both: automatic metrics for rapid iteration and regression detection, human evaluation for final validation and quality assessment.
LLM-as-judge for scalable evaluation
Use a strong model (e.g., GPT-4) to evaluate outputs from your production model. Provide clear rubrics: 'Is this answer helpful? factual? appropriate?'
Validate LLM judges against human ratings on a subset. If correlation is high (>0.8), you can scale LLM evaluation with confidence.
Adversarial testing and red-teaming
Actively try to break your model: prompt injection attempts, requests for harmful content, edge cases designed to trigger failures.
Maintain a red-team dataset that grows over time. Every discovered vulnerability becomes a permanent test case.
Longitudinal evaluation: tracking over time
Run the same evaluation set across model versions and prompt changes. This lets you track improvement or regression over time.
Create dashboards showing evaluation metrics by version. Make it easy to see if a change helped, hurt, or had no effect.
Evaluation in CI/CD
Integrate evaluation into your deployment pipeline. Before promoting a model or prompt change to production, run it against your test suite.
Set quality gates: if accuracy drops >5% or refusal rate increases >10%, block the deployment and investigate.