Deterministic Checks, Model Judges, and Humans
Deterministic Checks, Model Judges, and Humans
Fact-check status
Key jargon
| Term | Plain-language meaning |
|---|---|
| Deterministic check | A rule with reproducible output, such as schema validation or exact comparison. S1 |
| Model judge | A model asked to score or compare outputs against criteria. S1 |
| Human calibration | Comparing automated grades with qualified human judgments. S1 |
| Inter-rater agreement | The extent to which independent graders reach compatible judgments. S1 |
Key concepts
- Use deterministic checks for properties code can decide; use judges for nuanced criteria and humans for calibration or high-stakes ambiguity. S1
- Model judges inherit bias and variance, so test position effects, rubric sensitivity, and disagreement. S1
Concept map
flowchart LR
A["Run objective checks"] --> B["Apply rubric-based judge"]
B --> C["Sample human review"]
C --> D["Calibrate thresholds and graders"]Diagram semantics verified against S1 and S2 on 2026-09-02.
| Evaluator | Best for | Limits S1 |
|---|---|---|
| Deterministic code | Schema, exact values, tests, invariants | Cannot judge open-ended usefulness alone S1 |
| Model judge | Scaled rubric application and semantic comparison | Bias, instability, shared blind spots, prompt sensitivity S1 |
| Human reviewer | Context, consequence, novelty, disputed cases | Cost, inconsistency, fatigue S1 |
Use deterministic checks first, calibrated model review for scalable judgment, and humans for high-impact or ambiguous cases. Compare model judges against human-labeled samples. S1
Evidence-backed grading
Grade demonstrated behavior, not a claimed success string. Preserve source spans, tool traces, test output, state transitions, and effect receipts. S1
Exercise
Score ten summaries with a deterministic citation check, a model rubric, and two humans. Calculate disagreement categories and revise the rubric.