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No Memorization, No Detection: Output Distribution-Based Contamination Detection in Small Language Models

arXiv – CS AI|Omer Sela|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed CDD (Contamination Detection via output Distribution) to identify data contamination in small language models by measuring output peakedness. The study found that CDD only works when fine-tuning produces verbatim memorization, failing at chance level with parameter-efficient methods like low-rank adaptation that avoid memorization.

Key Takeaways

→CDD detection method depends critically on whether fine-tuning produces verbatim memorization in language models.
→Parameter-efficient fine-tuning like low-rank adaptation can produce undetectable contamination since models learn without memorizing.
→The study tested models ranging from 70M to 410M parameters on datasets including GSM8K, HumanEval, and MATH.
→A memorization threshold governs whether contamination becomes detectable through output distribution analysis.
→Current output-distribution detection methods have significant blind spots with modern efficient training techniques.

#language-models #contamination-detection #memorization #fine-tuning #model-evaluation #parameter-efficiency #research

Read Original →via arXiv – CS AI

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