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You can check whether evaluation examples appear in a training corpus you can inspect by searching for exact duplicates and near-duplicate text, then reviewing candidate matches. That can reveal overlap in the corpus searched; it cannot by itself prove that a particular model trained on that corpus or that exposure improved its score. If the training data is private, a different kind of black-box test can offer behavioral evidence, but not access to the hidden corpus.
The “ten minutes” in the headline is a time-box for an initial screen, not a validated benchmark: what is feasible depends on corpus size, indexing, and access. No prevalence estimate establishes that a particular reader’s eval set is probably in any specific model’s training data.
What an overlap check can—and cannot—tell you
Evaluation contamination can occur when test items, answers, labels, or close variants are present in training data. It can undermine how confidently a benchmark score reflects generalization. But “overlap found” and “this model was contaminated” are not interchangeable conclusions.
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- A corpus search establishes whether the corpus being searched contains text resembling an eval item.
- It does not establish that a specific model trained on that corpus, unless that training relationship is independently known.
- It does not, by itself, show that the overlap caused a higher score.
- A search that finds no candidates only means these checks found no evidence in the corpus and material examined; it does not prove the eval is clean.
The distinction matters in light of a NAACL 2024 experiment: researchers reported exact-match rates of 52% for ChatGPT and 57% for GPT-4 on a task that guessed missing options in MMLU test data. Those figures describe performance on that task, not the share of MMLU in either model’s training set and not present-day models. NAACL 2024 study.
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Run a first-pass check on a searchable corpus
- Fix the eval material. Identify the exact benchmark version and split. Preserve each item’s prompt, context passages, answer, and label as separate fields so you can tell what matched.
- Prepare comparable text. Apply the same normalization to eval items and corpus text—for example, consistent handling of whitespace and casing—and record the choices. Avoid transformations that erase meaningful distinctions in code, symbols, or answer choices.
- Search for exact duplicates first. Record item-level matches and the corpus locations where they occur. Exact matches are strong candidates for review, but may also arise from boilerplate or widely used wording.
- Expand to long shared n-grams or near-duplicate passages. Keep the thresholds and matching method in your report. In a controlled simulation of continual pretraining on multiple-choice benchmarks, n-gram matching achieved the highest F1 in the authors’ setup. That result is not a universal ranking for every corpus, benchmark, or kind of contamination. Eval4NLP 2025 study.
- Review the strongest candidates manually. A distinctive prompt appearing alongside the same answer or label is more concerning than a shared common phrase. Separate possible prompt-text reuse from answer or label exposure.
- Report the scope and result. Name the searched corpus and snapshot, benchmark and split, normalization, matching methods, flagged items, manual-review findings, and any unavailable material. Use wording such as “candidate overlap found in corpus X” or “these checks found no evidence in corpus X,” not “the model definitely trained on the eval.”
Choose a method that matches the evidence you need
| Method | Access needed | What it can detect | Main limitation | What the result supports |
|---|---|---|---|---|
| Exact-string search | Searchable corpus and eval text | Literal duplicates | Common text can produce weak candidates; paraphrases and translations can be missed | Whether literal text was found in the searched corpus |
| N-gram or near-duplicate search | Searchable corpus and eval text; indexing can help at scale | Shared sequences and close textual variants | Thresholds and corpus scale affect results; it is not a general detector of semantic reuse | Candidate textual overlap in the searched corpus |
| Permutation-based comparison | Method-specific data and evaluation setup | Evidence targeted by the tested permutation procedure | Directly compared with n-gram methods in a controlled simulation, not established as best for all contamination types or corpora | Evidence within the procedure’s assumptions and setup |
| Semantic or risk-level analysis | Eval material and suitable comparison evidence; requirements vary by method | Broader forms of similarity, including informational or label-related risk | Similarity is not proof of training exposure; findings depend on the method and validation | Risk characterization, not automatic proof that a specific model saw an item |
| Black-box canonical-versus-shuffled ordering test | Access to the model interface; no training corpus or weights required | Behavioral evidence from comparing likelihoods for canonical benchmark order and shuffled order | Not corpus retrieval; its guarantees are specific to the paper’s statistical procedure | Evidence under that test’s assumptions, not direct access to training examples |
The black-box method in an ICLR 2024 paper reports false-positive guarantees under its procedure. It is a different form of evidence from finding text in a corpus and does not generally prove that a model saw a particular item. ICLR 2024 paper.
Interpret matches without overstating them
Separate prompt overlap from answer exposure
For each flagged item, note whether the match concerns the prompt, a context passage, an answer, or a label. Shared question wording is not the same evidence as finding the question together with its correct answer or benchmark label. A benchmark-specific position paper argues for measuring contamination per benchmark and describing it carefully rather than treating all overlap as equivalent. Findings of EMNLP 2023 position paper.
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Expect string searches to miss some variants
Exact and n-gram checks are useful screens for literal and close textual reuse. They can miss paraphrased or translated examples, while looser similarity methods can flag text that is related but not evidence of training exposure. A 2023 preprint discusses how rephrasing can evade string-based checks. Yang et al., 2023 preprint.
Treat reported accuracy adjustments as method-specific
The DCR paper proposes semantic, informational, data, and label risk levels. Its abstract reports that accuracy adjusted using its DCR factor was within 4% average error across three specified benchmarks. That is a result from its validation setup, not a general guarantee for contamination checks. DCR, EMNLP 2025.
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If the training corpus is private
If you cannot inspect the corpus, say that corpus retrieval was unavailable; do not present a public-corpus search as a check of a private model’s training data. A black-box test may provide separate behavioral evidence when you can query the model, but its conclusion depends on that test’s assumptions and does not identify hidden training examples. If neither an appropriate searchable corpus nor the required model access is available, the result is simply that this check could not establish overlap.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to include in a contamination report
- Benchmark name, version, split, and the specific item fields checked.
- Corpus identity, snapshot or date, and known coverage limits.
- Normalization and search methods, including thresholds where relevant.
- Flagged item identifiers, matching corpus locations, and manual-review decisions.
- Whether possible prompt reuse, answer exposure, and label exposure were assessed separately.
- Data that could not be inspected and uncertainty the method cannot resolve.
This makes the finding auditable without converting a candidate match into a claim about model training or score causation.
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