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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

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Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses.

Micah Goldblum, Dimitris Tsipras, Chulin Xie

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 25, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Machine learning training data requires careful curation to prevent security vulnerabilities. This study categorizes dataset exploits and proposes defenses to ensure model integrity.

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    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    703

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning Security

    Background:

    • Machine learning (ML) systems demand vast datasets for optimal performance.
    • Automated and outsourced data curation are common but introduce security risks.
    • Lack of human oversight in data collection can lead to model manipulation.

    Purpose of the Study:

    • To systematically categorize dataset vulnerabilities and exploits in machine learning.
    • To discuss existing and novel approaches for defending against data-driven attacks.
    • To identify and highlight open research problems in dataset security.

    Main Methods:

    • Literature review and synthesis of existing research on data poisoning and adversarial attacks.
    • Categorization framework development for dataset vulnerabilities.
    • Analysis of defense mechanisms against data manipulation.

    Main Results:

    • Identified a taxonomy of dataset vulnerabilities, including data poisoning, backdoor attacks, and data integrity issues.
    • Evaluated the effectiveness of various defense strategies, such as data sanitization and robust training methods.
    • Highlighted the limitations of current defenses and the need for more resilient systems.

    Conclusions:

    • Dataset security is a critical, yet often overlooked, aspect of machine learning.
    • Proactive defense mechanisms and robust data governance are essential for trustworthy AI.
    • Further research is needed to develop comprehensive solutions for securing ML training data.