eval-integrity

Auditing an LLM benchmark repository for contamination and reproducibility failures requires statically inspecting scoring code, CI workflows, and governance docs to identify leakage, judge drift, and broken publish mechanics before results go live.

Benchmark integrity in LLM evaluation ensures published rankings survive adversarial review by verifying pre-registration, holdout hygiene, and statistical honesty, preventing cherry-picking, noisy rankings, and data contamination from invalidating results.

Checking evaluation repositories for holdout leaks and judge validity issues involves static inspection of scoring code and publish mechanics, producing evidence-backed ratings that flag data leakage and inconsistent judging before publication.

Preventing leaderboard exclusion bugs in LLM evaluation workflows requires auditing publication mechanics and governance docs, applying evidence-backed checks across seven integrity dimensions to catch methodology gaps before the leaderboard goes live.

Using a benchmark audit identifies pre-registration gaps in LLM results by inspecting repository governance docs and methodology workflows, generating PRESENT, PARTIAL, or ABSENT ratings with file-and-line evidence and concrete fixes.

Noisy rankings in LLM benchmarks often stem from statistical honesty issues and judge drift, which you fix by auditing scoring code and CI workflows to produce evidence-backed severity ratings and concrete fixes before publication.