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Evaluation leakage occurs when benchmark questions, answers, explanations, or close variants appear in material used to train or fine-tune a language model. A model can then score well partly because it has encountered the evaluation content, not solely because it generalizes to unseen examples. Detecting leakage requires matching evaluation data to available training text or, when training data are private, using behavioral probes that provide evidence without revealing a complete training history.
No single test proves that a model has or has not seen a benchmark. A defensible investigation defines the exposure being tested, documents its matching or probing procedure, checks flagged cases, and reports the limits of the evidence.
Why evaluation leakage changes what a score means
A benchmark score is intended to estimate performance on examples the model did not memorize from its training process. If test items or their answers occur in pretraining, instruction tuning, retrieval indexes, or later fine-tuning, the score can combine general capability with prior exposure. The presence of one or more matches does not reveal how much the final score is inflated: a model may have memorized an answer, learned a general pattern, or encountered text without retaining a usable solution.
Leakage is therefore an interpretation problem before it is a misconduct finding. Public benchmark material can enter a corpus accidentally through web crawls, tutorials, code repositories, or data releases. A detection result should normally be described as evidence of possible contamination unless the method’s assumptions support a stronger conclusion. It does not by itself establish intent, copyright infringement, or deliberate gaming.
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What counts as exposure
Write down the exposure hypothesis before testing. You might be checking for verbatim test questions, answer choices, worked solutions, benchmark instructions, a benchmark’s original training set, translated or paraphrased versions, or fine-tuning performed after the benchmark was published. These are different contamination types, and a test that detects one may miss the others.
Why a clean result is limited
Failure to find a match means only that the searched corpus and procedure produced no match. It cannot establish that a model never saw the item when the training corpus is incomplete, private, filtered, transformed, or unavailable. Altered, translated, paraphrased, or indirectly derived examples can evade a simple surface-overlap scan.
Start with a precise detection plan
- Define the target exposure. State whether you are looking for exact test examples, answers, explanations, benchmark variants, or later fine-tuning on evaluation material. Specify the model version, benchmark version, evaluation date, and training stages in scope.
- Inventory the evidence you can access. Gather the candidate pretraining and fine-tuning files, their release dates and hashes, and any known exclusions. If the corpus is only a sample or a web snapshot, say so; conclusions apply only to the material actually scanned.
- Choose a method that matches the evidence. Use corpus comparison when text is available. Use black-box probes when you can query the model but cannot inspect its data. Treat rewriting, backdoor, and embedding-based methods as specialized interventions rather than universal detectors.
- Prespecify matching and reporting rules. Record normalization (case, whitespace, punctuation, Unicode), n-gram length, similarity thresholds, deduplication, excluded files, and how near-duplicate candidates will be reviewed. Changing these choices after seeing results can change the apparent contamination rate.
Corpus comparison when training text is available
Exact matching is the most transparent first pass. Normalize the benchmark and candidate corpus in the same documented way, then search for complete questions, answer options, explanations, and distinctive spans. Exact matches are easy to audit but miss edits and reordered text.
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N-gram matching breaks text into contiguous sequences of n tokens or words and indexes them. EleutherAI’s lm-evaluation-harness documentation describes building indexes of evaluation n-grams and marking evaluation/document pairs that share them; its simple implementation uses 13-grams. Earlier work associated with OpenAI used values between 8 and 13 depending on the dataset. These are implementation choices, not universal contamination thresholds.
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- Version and freeze the benchmark, including its prompt format and answer key.
- Apply the same tokenizer or word-splitting rule to benchmark and corpus text, and save the normalized representation.
- Build an index of benchmark n-grams, recording the item identifier and span for each n-gram.
- Stream candidate training documents through the index and record every document-item pair with an overlap, along with the number and locations of shared n-grams.
- Group adjacent overlaps so that one copied passage is not counted as hundreds of independent hits.
- Review flagged pairs manually or with a higher-precision similarity check. Confirm that the shared text is not a generic phrase, a citation, or material that entered both datasets from an unrelated common source.
- Report the number of candidate pairs, the number judged meaningful, the corpus fraction scanned, and the procedure’s known recall limits.
What to do with a match
Preserve the source document, benchmark item, offsets, and normalization settings so another analyst can reproduce the finding. Classify the match: full question, answer only, explanation, title or metadata, benchmark instruction, or a transformed variant. A shared answer without the question may indicate exposure, but it is weaker evidence than a distinctive complete item and still does not reveal when or how the text entered training.
Behavioral checks for closed models
Closed models do not provide a complete training manifest. Their outputs can still be tested, but these procedures infer a statistical signal from behavior; they do not retrieve the model’s source documents.
Testset Slot Guessing
Deng, Zhao, Tang, Gerstein, and Cohan describe Testset Slot Guessing in a NAACL 2024 study. For a multiple-choice question, the protocol masks a wrong option and asks the model to recover it; for another format, it hides an unlikely word and measures whether the model fills the slot. In their 2024 MMLU experiment, ChatGPT recovered the missing option exactly 52% of the time and GPT-4 did so 57% of the time. Those percentages belong to that study’s prompts, models, and scoring rule; they are not a current-model baseline or a universal contamination cutoff.
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Canonical-order versus shuffled-order likelihood
Oren and coauthors propose a black-box statistical test that compares the model’s likelihood for benchmark examples in their canonical order with the likelihood after the same examples are shuffled. The method is designed to have exact false-positive guarantees under its stated assumptions and does not require access to pretraining data or model weights. It requires likelihood information, such as token probabilities; many chat interfaces expose neither probabilities nor a stable scoring endpoint.
A significant order-related signal supports the tested hypothesis under the chosen benchmark, ordering, and statistical model. It does not reconstruct a training corpus, identify the publication that supplied an item, or prove that every unusual score reflects contamination.
Interventions that test or reduce suspected leakage
Inference-time rewriting
Zhu and colleagues describe an inference-time procedure that detects suspected leaked samples and rewrites them before scoring. In their Findings of EMNLP 2024 experiments, the approach reduced inflated accuracy by 22.9% on GSM8K and 19.0% on MMLU. In the same study, MMLU results decreased 6.7% for Phi-3 and 3.6% for Mistral after decontamination. These are study-specific measurements, not expected reductions for another model or benchmark.
Rewriting changes the model’s input and can change difficulty, ambiguity, or the skills being measured. Report the original and rewritten conditions separately, describe the rewrite rules, and avoid calling the transformed score a clean measurement of the unchanged benchmark.
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Filtering candidate training data
Removing documents or passages that overlap with a benchmark can reduce direct exposure, but it also changes the training distribution and may remove legitimate educational or reference material. Keep a record of every exclusion and evaluate whether filtering disproportionately removes a topic, language, or source type. Filtering cannot address material that was missed by the scan or exposure that occurred in an unobserved corpus.
Fresh, private, or time-sensitive benchmarks
A benchmark assembled after the model’s training cutoff, held privately until evaluation, or refreshed with new items reduces the chance of public overlap. It still needs leakage controls: prompts, answer keys, pilot examples, and evaluator conversations can enter later training or retrieval systems. Establish a release policy and monitor all channels through which test material could be copied.
Specialized detection proposals
DyePack backdoor samples
DyePack mixes specially constructed backdoor examples into an evaluation set. A trigger response can flag a model that used the set during training. Cheng and coauthors report tests on five models and three datasets. Their EMNLP 2025 abstract reports false-positive rates as low as 0.000073% on MMLU-Pro and 0.000017% on Big-Bench-Hard when eight backdoors were used, and 0.127% on Alpaca when six were used. These figures apply to the authors’ backdoor designs, datasets, and experimental setup; they do not establish an off-the-shelf guarantee for arbitrary evaluations.
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Kernel Divergence Score
Kernel Divergence Score estimates contamination by comparing embedding-kernel similarity structure before and after fine-tuning on a benchmark. Choi and coauthors present it as a research method tied to its assumptions and experimental setup. It is not equivalent to direct corpus matching and should not be presented as a universal verdict for every model, embedding space, or dataset.
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How the methods differ
| Method | Access needed | Signal | Important limitation |
|---|---|---|---|
| Exact or n-gram overlap | Candidate training text and evaluation set | Shared surface sequences | Misses unscanned data and many changed or indirect variants; thresholds depend on the setup. |
| Testset Slot Guessing | Query access and benchmark examples | Recovery of a masked answer or unlikely word | Does not identify the source or prove exposure; published percentages are experiment-specific. |
| Canonical-order likelihood test | Black-box likelihood access and benchmark ordering | Likelihood difference between canonical and shuffled sequences | Tests a statistical signal, not full training-data provenance. |
| Inference-time rewriting | Query access and a rewrite procedure | Performance change on rewritten suspected examples | Rewriting changes inputs and may change difficulty or utility. |
| DyePack | Ability to create and include backdoor examples | Triggered behavior associated with benchmark use | Specialized; published scope is limited to tested setups. |
| Kernel Divergence Score | Embeddings before and after benchmark fine-tuning | Change in kernel-similarity structure | Depends on the method’s assumptions and experimental conditions. |
Interpreting positive and negative findings
Use calibrated language
For a corpus match, write that a specified evaluation item overlaps a specified document under a specified rule. For a behavioral result, write that the model exhibits evidence consistent with prior exposure under the tested probe. Reserve “confirmed contamination” for situations where the data lineage and method genuinely support that level of certainty.
Separate prevalence from impact
Count and characterize matches, but do not convert the count directly into score inflation. Estimate impact by evaluating a documented clean subset, a newly authored set, or a controlled before-and-after intervention. The fraction of overlapping items and the performance difference can vary independently.
Account for controls and multiple tests
Use control questions with similar wording and difficulty but no suspected benchmark history. If you run many probes, report all planned tests and the correction or threshold used for multiple comparisons. A single surprising output is weak evidence without a baseline.
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Public overlap may be inadvertent. Keep technical evidence separate from claims about a developer’s behavior, policy compliance, or misconduct. Those claims require independent evidence beyond a detector’s output.
A reporting checklist for evaluations
- Model name, version, access mode, decoding settings, and evaluation date.
- Benchmark name, version, item count, prompt template, answer format, and release date.
- Training or fine-tuning corpus versions, coverage, and any unavailable sources.
- Exposure definition: verbatim text, answers, explanations, variants, or post-release fine-tuning.
- Normalization, tokenizer, n-gram length, thresholds, exclusions, and deduplication rules.
- Flagged item and document identifiers, shared spans, manual-review outcomes, and unresolved cases.
- For probes, exact prompts, masks, controls, randomization or shuffling procedure, likelihood interface, and statistical test.
- For rewriting or filtering, the transformation or exclusion policy and results on both original and altered conditions.
- Uncertainty statement explaining what the procedure could not observe.
A practical decision path
- Have you got candidate training text? Start with exact and n-gram scans, then investigate near-duplicates and transformed variants.
- Is the corpus incomplete? Label the result as partial-corpus evidence and add behavioral probes; do not describe a clean scan as proof of no exposure.
- Can the model provide likelihoods? If yes, consider the canonical-versus-shuffled test alongside controls. If no, use slot-guessing or another query-only probe and report its narrower inference.
- Do you need a score for deployment decisions? Prefer a fresh or private evaluation set, document any filtering, and report original and transformed scores separately.
What a defensible conclusion looks like
A strong report might say: “Thirteen benchmark items shared at least one 13-gram with documents in the scanned training snapshot. Manual review judged five overlaps distinctive; the remaining matches were generic or common-source text. Because the snapshot excludes private data and transformed copies, the scan cannot establish that the model had no other exposure. On a slot-guessing probe, recovery exceeded the matched control set, providing additional evidence consistent with familiarity but not identifying its source.” That wording states the observation, method, scope, and uncertainty without turning a detector into a provenance oracle.
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