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This study reveals that prompts can detect if language models memorize training data, a privacy concern. Prompt effectiveness varies significantly by model and prompt design, impacting memorization detection accuracy.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Natural Language Processing

Background:

  • Language models may memorize sensitive training data, posing privacy risks.
  • Identifying memorization is crucial for data privacy and model security.

Purpose of the Study:

  • To analyze the effectiveness of prompts in detecting training data memorization in masked language models.
  • To evaluate how prompt properties influence detection performance.

Main Methods:

  • Utilized 1,200 automatically generated prompts for named entity recognition (NER) tasks.
  • Employed a detection dataset with in-sample and out-of-sample entity names.
  • Measured memorization strength by prediction confidence differences between in-sample and out-of-sample names.

Main Results:

  • Prompt performance in detecting memorization varied by up to 24.5 percentage points across different prompts on the same model.
  • Prompt engineering further amplified performance gaps.
  • Detection performance was model-dependent but generalized across different name sets.

Conclusions:

  • Prompts are viable tools for detecting language model memorization.
  • Prompt design and model architecture significantly influence memorization detection capabilities.
  • Further research into prompt properties can enhance privacy-preserving AI.