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This paper explores vulnerabilities in large language models, particularly those aligned to follow user instructions. It presents novel attacks that can bypass alignment defenses to extract sensitive training data from models like OpenAI’s ChatGPT, raising concerns about the privacy and security of training datasets in production systems. The findings emphasize the inadequacies of existing safeguards and propose further improvements in model training processes to prevent data leakage.
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What is scalable extraction of training?
Scalable extraction of training refers to the methodical process of obtaining and using training data from various sources, ensuring that the extraction can be scaled or adapted according to the size and complexity of the datasets involved.
Who is required to file scalable extraction of training?
Entities or organizations that engage in data extraction for training purposes or that utilize machine learning models requiring large datasets must file for scalable extraction of training.
How to fill out scalable extraction of training?
To fill out scalable extraction of training, one must provide detailed information regarding the data sources, training objectives, methods of extraction, and any regulations or compliance measures in place.
What is the purpose of scalable extraction of training?
The purpose of scalable extraction of training is to systematically collect and prepare data for machine learning models, ensuring efficiency, effectiveness, and adherence to legal and ethical standards.
What information must be reported on scalable extraction of training?
Reports must include data source descriptions, methodologies used in extraction, intended use of training data, compliance with data protection regulations, and any potential impacts on privacy.
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