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AI Research
arXiv CS.AI·May 27·12 min read
Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications
TL;DR
Large language models trained on massive datasets risk exposing their pretraining data, creating privacy and evaluation integrity problems. This survey maps membership inference (detecting if specific data was in training) and data contamination (test data leaking into training) as complementary attack vectors.
✦ Why It Matters
Engineers can use this framework to audit training pipelines, detect contamination, and implement privacy-preserving pretraining practices.
Key Takeaways
- Assess your LLMs for membership inference vulnerabilities.
- Implement defenses against data contamination in training datasets.
- Stay updated on empirical findings related to PDE.
- Explore future research directions to enhance model security.
How It Works
PDE is assessed through various exposure levels, where researchers can employ specific attack strategies to determine if certain data was included in the training set. Techniques such as membership inference attacks leverage model outputs to infer the presence of data points, while defenses may involve data sanitization or differential privacy methods to mitigate risks.