Benchmark Leakage and Contamination: Keeping Evaluation Honest

Leaked benchmarks inflate scores without improving real performance.

This creates false confidence and harmful product decisions.

Training data crawls often include public benchmarks.

Duplicate detection and hash-based filtering are essential safeguards.

Use overlap analysis, n-gram matching, and embedding similarity checks.

Flag suspiciously high scores on narrow benchmarks.

Maintain private, curated datasets that never enter training.

Version evaluation sets and track changes over time.

Audit training data sources and maintain data provenance.

Document evaluation protocols to preserve long-term credibility.

Run contamination checks whenever new data sources are added. Leakage often sneaks in during expansion.

Track benchmark overlap over time and report it alongside model results to preserve trust.

Maintain a quarantine list of suspect data sources until they are fully validated.

Schedule periodic re-checks of old datasets; leaks can appear later through merges.

Require sign-off before any benchmark data is introduced into training pipelines.

Keep an audit log of data source approvals so reviews stay traceable.

Publish a contamination report with each model release to keep evaluation credibility high.

Monitor contamination metrics in dashboards so regressions are visible early.

Run post-training scans to ensure no benchmark data slipped in during preprocessing.

Archive contamination reports to build institutional memory over time.

Review contamination alerts with the data team to decide mitigation quickly.

Maintain escalation rules for severe contamination events to trigger immediate pauses.

Tag datasets with risk levels so reviewers can prioritize the highest risk sources.

Restrict benchmark access to a small group to reduce accidental mixing.

Rotate evaluation prompts periodically so leaked items lose impact over time.

Record evaluator access in audit trails so reviews can verify isolation.

Is contamination always accidental? Often, but still damaging.

How do I start? Build a small private evaluation set and protect it.

What is the biggest red flag? Large jumps on a single benchmark only.