  Recent observations have underscored a disparity between the inflated
benchmark scores and the actual performance of LLMs, raising concerns about
potential contamination of evaluation benchmarks. This issue is especially
critical for closed-source models and certain open-source models where training
data transparency is lacking. In this paper we study data contamination by
proposing two methods tailored for both open-source and proprietary LLMs. We
first introduce a retrieval-based system to explore potential overlaps between
evaluation benchmarks and pretraining corpora. We further present a novel
investigation protocol named \textbf{T}estset \textbf{S}lot Guessing
(\textit{TS-Guessing}), applicable to both open and proprietary models. This
approach entails masking a wrong answer in a multiple-choice question and
prompting the model to fill in the gap. Additionally, it involves obscuring an
unlikely word in an evaluation example and asking the model to produce it. We
find that certain commercial LLMs could surprisingly guess the missing option
in various test sets. Specifically, in the TruthfulQA benchmark, we find that
LLMs exhibit notable performance improvement when provided with additional
metadata in the benchmark. Further, in the MMLU benchmark, ChatGPT and GPT-4
demonstrated an exact match rate of 52\% and 57\%, respectively, in guessing
the missing options in benchmark test data. We hope these results underscore
the need for more robust evaluation methodologies and benchmarks in the field.
