Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications
arXiv:2605.26133v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become the predominant paradigm in NLP, advancing both research and industry. As model sizes and pretraining data grow, concerns about Pretraining Data Exposure (PDE) increase due to the scale and opacity of training datasets. PDE refers to determining whether specific data appeared in an LLM's pretraining corpus. It is critical for ensuring evaluation integrity and protecting privacy, intersecting two key areas: data contamination and membership inference. Though conceptually related, these areas have often be
The rapid growth in LLM size and reliance on vast, often opaque pretraining datasets has brought concerns about data privacy and integrity to the forefront.
Understanding and mitigating Pretraining Data Exposure (PDE) is crucial for the trustworthiness, security, and ethical deployment of large language models, impacting their adoption in sensitive applications.
Increased scrutiny on LLM training data practices, potentially leading to new regulatory frameworks, data governance standards, and architectural solutions for privacy-preserving AI.
- · AI ethics researchers
- · Data privacy solution providers
- · Auditors of AI systems
- · Consumers concerned about data privacy
- · LLM developers with opaque data practices
- · Organizations using LLMs without proper data governance
- · Companies reliant on scraped public data
Increased focus on transparent data sourcing and de-identification techniques for LLM pretraining.
Development of new architectural paradigms for LLMs that inherently limit data exposure or allow for verifiable data provenance.
Potential for specialized 'privacy-by-design' LLM foundational models, leading to a bifurcated market based on data exposure guarantees.
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