Private evaluation datasets
Explore the tasks and capabilities covered, and the reasons and limits behind private test data.
Backend engineering
Service logic, error handling, and test verification.
CoverageContracts and boundaries · Retries and failures · CI and adversarial review · Transactions and consistency · Caching and regression
Scoring criteria →Frontend interaction
Interface behavior, user interactions, and state feedback.
CoverageLayout and adaptation · Interaction behavior · State continuity · Accessibility and feedback · State validation and regression
Scoring criteria →Knowledge & Reasoning
Cross-disciplinary knowledge and complex problem solving.
CoverageMathematical reasoning · Computer science & AI · Science and engineering · Life sciences and medicine · Humanities and general knowledge
Scoring criteria →The role and limits of private test data
Leaderboards use different tasks, scoring methods and model settings, so their rankings are not interchangeable. Private test data can complement public benchmarks, but task design and scoring still need scrutiny.
Some benchmarks are nearing their scoring ceiling
Some benchmarks have saturated: when leading models cluster near the scoring ceiling, scores become less useful for distinguishing them. Evaluation needs discriminating tasks and ongoing design and maintenance.
Published questions may enter training data
Published test material can enter training data or be used for benchmark-specific optimization. Private test data can reduce that exposure. A public leaderboard does not necessarily use public test questions.
Publish task coverage, scoring methods and results
ModelDial records results by evaluation dimension and explains the scoring criteria and measured results. Compare generations and reasoning efforts, then relate the evidence to your work.
Keeping data private does not guarantee a good evaluation. Sound tasks, reliable scoring and continued maintenance are also necessary.
Research and industry practice
- When AI Benchmarks Plateau
ICML 2026: nearly half of the 60 benchmarks studied showed saturation. Expert curation mattered for resilience; whether test data was public did not explain it.
- LiveBench
Uses questions from recent material and regularly adds tasks to reduce contamination risks.
- Scale · MASK
Ranks models on a private test split alongside a public split, to monitor overfitting and limit gaming.