Why humans still judge
Automatic metrics miss nuance like helpfulness, tone, and subtle errors. Human evaluation asks people to rate or compare model outputs, capturing quality that scripts cannot.
Building a sound protocol
- Clear instructions that define each rating level with examples.
- Multiple raters per item so one opinion does not dominate.
- Randomized order so position and model identity stay hidden.
- Attention checks to catch raters who click without reading.
A protocol turns vague impressions into structured, repeatable data.
Measuring agreement
If raters disagree wildly, the scores mean little. Inter rater agreement, often reported with statistics like Cohen kappa, checks whether people apply the rubric consistently. Low agreement signals an unclear rubric or an ambiguous task, not just hard items.
Controlling bias and cost
Humans drift, tire, and favor longer or more confident answers. Calibration sessions, balanced batches, and blind setups reduce these effects. Because human labeling is slow and expensive, teams often label a careful sample and use it to validate cheaper automatic metrics.
Key idea
Human evaluation captures quality that metrics miss, but only a protocol with clear rubrics, multiple blind raters, and agreement checks produces judgments you can trust.