The Tool Desk
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What an overlap check can—and cannot—tell you
Evaluation contamination occurs when test examples, answers, labels, or close variants appear in data used to train a model. It can make benchmark scores harder to interpret, but “overlap” covers different levels of exposure: a shared phrase is not the same as a copied question with its answer, and neither alone demonstrates that a particular model saw the material.
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A direct corpus search answers a limited question: does the corpus being searched contain text like this evaluation item? It does not establish that a model was trained on that corpus, that the matching material was included in its training, or that exposure caused a higher score. Those claims require evidence beyond a text match.
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The ten-minute framing is a time box, not a duration validated by the cited studies. A small, indexed corpus may permit a fast first pass; large corpora, unavailable data, or manual review can take longer.
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Run a first-pass check when you can inspect the corpus
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Fix the evaluation target
Record the benchmark version and exact split. Preserve each item’s prompt, context passages, answer choices, correct answer, and label as separate fields. This lets you identify what matched instead of treating an entire example as one undifferentiated string.
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Normalize both sides consistently
Apply the same text-normalization rules to the evaluation items and the searchable training corpus. Keep the original text, too, so reviewers can verify a match and understand what normalization changed.
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Search exact text, then longer shared n-grams
Begin with exact duplicates and proceed to searches for long shared sequences of words. Retain the evaluation item and the matching corpus location for each candidate. In a controlled simulation of leakage into multiple-choice benchmarks, n-gram matching achieved the highest F1 score among the compared methods, including permutation approaches. That finding describes the study’s setup, not a universal winner for every corpus or type of contamination. Read the Eval4NLP 2025 study.
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Manually inspect the strongest candidates
Give more weight to a distinctive prompt that appears with the same answer or label than to a common phrase, boilerplate, or topical similarity. Record text reuse separately from evidence that an answer or label was present. String matching can miss paraphrases and translations, so a clean literal-match search is not proof that no related example exists. The DCR paper and a study of rephrased samples discuss broader contamination and matching risks.
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Describe what the search covered
Report the corpus snapshot and its coverage, the benchmark version and split, normalization and matching methods, flagged items, and manual-review criteria. State which data you could not inspect. If no candidates were found, say that the checks found no evidence of overlap; do not call the evaluation clean.
Choose a method that matches your access and question
| Approach | Access needed | What it can indicate | Main limitation | What a result supports |
|---|---|---|---|---|
| Exact matching | A searchable corpus and the evaluation text | Literal reuse of item text | Misses altered wording, paraphrases, and translations; common text can match by chance | That the searched corpus contains matching text, subject to review |
| N-gram matching | A searchable corpus and the evaluation text | Long shared sequences that may reveal near-duplicate text | Results depend on thresholds and corpus composition; it can miss non-literal reuse | Candidate text overlap in the searched corpus, not model exposure |
| Permutation methods | A searchable corpus and evaluation items; implementation details affect the test | Potential leakage patterns in multiple-choice settings | Evidence in the cited comparison is specific to its controlled simulation | A method-dependent signal, not proof of training or score impact |
| Semantic or risk-oriented analysis | Evaluation examples and an analysis method; corpus access may vary | Potentially broader relationships, including informational or label exposure | Similarity or risk categories do not by themselves establish model training exposure | A structured assessment of possible contamination, with method-specific limits |
| Black-box ordering test | Access to model outputs, but not necessarily training data or weights | Statistical evidence based on canonical versus shuffled benchmark ordering | Its conclusions depend on the test procedure and assumptions; it does not reveal the hidden corpus | Evidence under that procedure, not direct confirmation of corpus contents |
The table’s distinctions matter: retrieval tests inspect a corpus, while a black-box test examines model behavior. They answer different questions and should not be presented as interchangeable ways to prove exposure.
The 2024 ICLR paper describes a black-box statistical test comparing likelihoods for canonical and shuffled benchmark order, with false-positive guarantees under its procedure. Its result is a different kind of evidence from finding text in a corpus; it neither exposes hidden training data nor provides a general proof of exposure.
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You cannot run a direct corpus search against data you cannot inspect. Unless the data owner provides an appropriate searchable corpus, say that retrieval-based verification was unavailable. A black-box statistical test may offer separate behavioral evidence, but its assumptions and scope need to be reported, and it cannot establish what is inside the hidden corpus.
Research on benchmark contamination recommends measuring it benchmark by benchmark rather than treating a single general-purpose check as decisive. A position paper in Findings of EMNLP 2023 discusses why levels of contamination and disclosure matter for evaluation interpretation. See the paper on benchmark-specific measurement.
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Interpret published figures in their proper scope
Two results illustrate why contamination figures need their experimental context:
- 52% and 57%: A NAACL 2024 study reported these exact-match rates for ChatGPT and GPT-4, respectively, on a task that guessed missing options in MMLU test data. They are results for that task and study—not estimates of what fraction of MMLU appeared in either training set, and not claims about current models. Read the NAACL study.
- Within 4% average error: The DCR paper’s abstract reports this result for accuracy adjusted with its DCR factor across three specified benchmarks. It is a validation result from that study’s setup, not a general guarantee for contamination checks. Read the DCR paper.
Likewise, the Eval4NLP authors’ conclusion that n-gram matching performed best by F1 applies to their simulated continual-pretraining experiments. It is useful evidence for selecting a first-pass method, not a reason to assume the same ranking across different data or contamination types.
Report findings without overstating them
Use wording that identifies the evidence and its boundary. For example: “We found candidate overlap between items in benchmark version X, split Y, and corpus snapshot Z using normalized exact and n-gram matching; manual review confirmed these matches as text reuse. The search does not establish that a particular model trained on this corpus.” If the search found nothing, say: “These checks found no evidence of overlap in the corpus and text forms examined.”
Quick Recap
- Name the benchmark, version, and split.
- Identify the corpus snapshot and explain its coverage.
- Describe normalization, matching methods, and any thresholds used.
- List flagged items and summarize manual review, separating prompt, answer, and label matches.
- Disclose unavailable data and the limits of detecting paraphrased or translated variants.
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