AI ANALYSIS REPORT
Personal Information Parroting in Language Models
This research delves into the critical issue of Personal Information (PI) parroting by large language models (LLMs). We introduce an improved regex and rules (R&R) detection suite for email addresses, IP addresses, and phone numbers, outperforming existing methods. Our findings reveal significant PI memorization across various Pythia models, with up to 19.6% of email addresses and 14.1% of IP addresses being verbatim parroted by the largest models. Model size, pretraining steps, and prefix length are all positively correlated with memorization, highlighting urgent privacy risks and the need for aggressive data filtering and anonymization.
19.6%
Email Parroting
14.1%
IP Parroting
3.3%
Phone Parroting
Deep Analysis & Enterprise Applications
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Introduction & R&R Suite
Memorization & Risk
Results & Analysis
This section introduces the problem of PI parroting in LLMs and presents the new R&R detector suite, which significantly improves precision for detecting character-based PI like email addresses, IP addresses, and phone numbers compared to existing regex-based solutions. The suite's enhanced capabilities are crucial for effective pretraining data sanitization.
17
R&R outperforms WIMBD in X categories out of 20
| PI Type | R&R Precision | WIMBD Precision |
|---|---|---|
| Significantly Higher | Moderate | |
| US Phone | High (0.3 on average) | Near 0% |
| IP Address | Comparable | Comparable |
| Note: R&R shows significant improvement, especially for phone numbers due to new regexes and post-processing rules. | ||
We quantify PI memorization using p-memorization and PARROTSCORE (Levenshtein distance). A score of 1 indicates verbatim parroting. This metric allows us to assess the privacy risk posed by LLMs generating exact PI from their training data.
13.6%
% of PI parroted verbatim by Pythia-6.9b model (average)
PI Memorization Measurement Workflow
Identify PI in Data
→
Extract Prefix
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Generate with LM
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Calculate PARROTSCORE
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Quantify Verbatim Parroting
Our analysis across Pythia models (160M-6.9B) reveals a strong positive correlation between model size, pretraining timesteps, and prefix length with PI memorization. Email addresses are the most susceptible to parroting, followed by IP addresses. Even small models show memorization, underscoring the pervasive risk.
20%
% of detected email addresses parroted by largest models
The Peril of Parroted PI
Consider a scenario where an LLM parrots a customer's full email address and phone number from its training data in response to a benign query. This direct exposure of Personal Information (PI) can lead to severe privacy breaches, identity theft, and regulatory non-compliance. Our findings emphasize that such risks are not theoretical; they are rampant across model sizes, demanding immediate action through enhanced data filtering and anonymization strategies.
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