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ARXIV:2603.03203 · LANGUAGE MODEL RELIABILITY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.03203LANGUAGE MODEL RELIABILITYSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Detect data contamination in small language models by analyzing output distribution shape to mitigate memorization risks.
Opportunity summary
Pain Detect data contamination in small language models by analyzing output distribution shape to mitigate memorization risks.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Detect data contamination in small language models by analyzing output distribution shape to mitigate memorization risks. We study the conditions under which this approach succeeds and fails on small language models ranging from 70M…
CDD, or Contamination Detection via output Distribution, identifies data contamination by measuring the peakedness of a model's sampled outputs. We study the conditions under which this approach succeeds and fails on small language models…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our results characterize a memorization threshold that governs detectability and highlight a practical consideration: parameter-efficient fine-tuning can produce contamination that output-distribution methods do not…
Language Model Reliability moved forward this cycle; last verified April 2026. Public score 5.0/10.
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Detect data contamination in small language models by analyzing output distribution shape to mitigate memorization risks.
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Paper Pack
10.48550/arXiv.2603.03203Detect data contamination in small language models by analyzing output distribution shape to mitigate memorization risks.
Abstract
CDD, or Contamination Detection via output Distribution, identifies data contamination by measuring the peakedness of a model's sampled outputs. We study the conditions under which this approach succeeds and fails on small language models ranging from 70M to 410M parameters. Using controlled contamination experiments on GSM8K, HumanEval, and MATH, we find that CDD's effectiveness depends critically on whether fine-tuning produces verbatim memorization. With low-rank adaptation, models can learn from contaminated data without memorizing it, and CDD performs at chance level even when the data is verifiably contaminated. Only when fine-tuning capacity is sufficient to induce memorization does CDD recover strong detection accuracy. Our results characterize a memorization threshold that governs detectability and highlight a practical consideration: parameter-efficient fine-tuning can produce contamination that output-distribution methods do not detect. Our code is available at https://github.com/Sela-Omer/Contamination-Detection-Small-LM
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Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Preparing verified analysis
Dimensions overall score 5.0
PROBLEM
Detect data contamination in small language models by analyzing output distribution shape to mitigate memorization risks. We study the conditions under which this approach succeeds and fails on small language models ranging from 70M to 410M parameters.
METHOD
CDD, or Contamination Detection via output Distribution, identifies data contamination by measuring the peakedness of a model's sampled outputs. We study the conditions under which this approach succeeds and fails on small language models ranging from 70M to 410M parameters.
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our results characterize a memorization threshold that governs detectability and highlight a practical consideration: parameter-efficient fine-tuning can produce contamination that output-distribution met...
WHY NOW
Language Model Reliability moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Detect data contamination in small language models by analyzing output distribution shape to mitigate memorization risks. We study the conditions under which this approach succeeds and fails on small language models ranging from 70M to 410M parameters.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
CDD, or Contamination Detection via output Distribution, identifies data contamination by measuring the peakedness of a model's sampled outputs. We study the conditions under which this approach succeeds and fails on small language models ranging from 70M to 410M parameters.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our results characterize a memorization threshold that governs detectability and highlight a practical consideration: parameter-efficient fine-tuning can produce contamination that output-distribution methods do not detect.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Language Model Reliability moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Detect data contamination in small language models by analyzing output distribution shape to mitigate memorization risks.
Segment
Language Model Reliability
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Commercial read
5.0/10 public viability
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reason
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proof status
unverified
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confidence low
next verification path
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stale
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Integration burden
missing
Current read
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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