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Review ArticleNot peer reviewed

A Taxonomy of Evaluation Leakage in Language-Model Benchmarks

Nadia Haddad, Ingrid Novak, Kofi Mensah

Version 2 (updated) · 14 October 2026 · 10.xxxx/ctm.pp.2026.0004

Benchmark scores can be inflated when test material reaches the training data or when evaluation choices quietly favour a model. We review 140 papers and incident reports and organise the mechanisms of evaluation leakage into a taxonomy with four top-level classes and eleven sub-classes. For each class we summarise how it can be detected, how often it has been reported, and what practices reduce its likelihood.

Abstract PDF 3,350 1,4105 cited
Technical ReportNot peer reviewed

Reproducibility-Checklist Compliance in Machine-Learning Papers: A technical report on what authors state and what they release

Ingrid Novak, Kofi Mensah

Version 1 · 27 July 2026 · 10.xxxx/ctm.pp.2026.0002

Many machine-learning venues now require authors to complete a reproducibility checklist. We audited 240 papers that ticked the item “code is provided” and checked whether the linked repositories existed, ran and reproduced the headline table. 87% had a reachable repository, 58% contained instructions sufficient to run training or evaluation, and 31% reproduced the headline result within the reported variance. Compliance was higher for papers reporting variance across seeds. We recommend checks that can be automated at submission time.

Abstract PDF 2,455 9314 cited