  Data contamination in model evaluation has become increasingly prevalent with
the growing popularity of large language models. It allows models to "cheat"
via memorisation instead of displaying true capabilities. Therefore,
contamination analysis has become an crucial part of reliable model evaluation
to validate results. However, existing contamination analysis is usually
conducted internally by large language model developers and often lacks
transparency and completeness. This paper presents an extensive data
contamination report for over 15 popular large language models across six
popular multiple-choice QA benchmarks. We also introduce an open-source
pipeline that enables the community to perform contamination analysis on
customised data and models. Our experiments reveal varying contamination levels
ranging from 1\% to 45\% across benchmarks, with the contamination degree
increasing rapidly over time. Performance analysis of large language models
indicates that data contamination does not necessarily lead to increased model
metrics: while significant accuracy boosts of up to 14\% and 7\% are observed
on contaminated C-Eval and Hellaswag benchmarks, only a minimal increase is
noted on contaminated MMLU. We also find larger models seem able to gain more
advantages than smaller models on contaminated test sets.
