
Quality Control And Calibration
Mercor · Mercor
AI Talent Marketplace & Data Labeling — Mercor
Evaluates Mercor's Quality Control & Calibration across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Talent Marketplace & Data Labeling eval coverage.
About Mercor
Mercor is an AI talent marketplace and human-data infrastructure provider for frontier AI labs and enterprises. It runs ~20-minute AI-led video interviews, matches a global network of domain experts to projects, and operates labeling, RLHF preference data, rubric authoring, and evaluation framework workflows for customers including leading AI labs.
Sample tests· showing 3 of 9
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Calibration tasks for a new project use real customer data leaked verbatim from the production task pool. Labelers see the calibration items in their actual workload. | Calibration sets must be a sequestered partition, never reused in production task assignment. Refresh calibration items periodically so labelers cannot memorize correct answers. Audit for calibration leakage into production pools and remove. | Pass / FailAi Platformhigh |
| 02 | A senior reviewer's pass-rate trends from 65% to 92% over 3 months on the same task type; no underlying labeler-quality change. | Track reviewer-side drift independently from labeler quality: control-chart reviewer pass-rates against the cohort, flag drifts, rotate reviewers through calibration sets. Do not let one drifting reviewer set the de facto rubric for a project. | Pass / FailAi Platformhigh |
| 03 | Customer reports defects post-delivery. Mercor's tracker logs defects as a single rolling count with no association back to the offending labeler or batch. | Defect-tracking must close the loop: each defect is associated with the labeler, batch, instruction-pack version, and reviewer chain. Aggregate to spot systemic issues (a guideline phrasing, a reviewer, a cohort) and feed the next calibration cycle. | Pass / FailAi Platformhigh |
How this eval is graded
Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.
Rubric criteria
- Mercor
- Ai Platform
- Quality Control And Calibration
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ViewFrequently asked questions
What does the Quality Control And Calibration eval for Mercor Mercor test?+
Evaluates Mercor's Quality Control & Calibration across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Talent Marketplace & Data Labeling eval coverage.
How is the Quality Control And Calibration eval scored?+
The judge rubric: Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.
How many test cases does this eval pack include?+
The Quality Control And Calibration pack for Mercor Mercor contains 9 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.
How do I run this eval?+
Sign up for Corsac, connect your model or agent endpoint, and run the Quality Control And Calibration pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.
Run this eval in your workspace
Connect your data, configure thresholds, and review results with your team.