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Mitigating Memorization in Language Models: The Goldfish Loss Approach

Mitigating Memorization in Language Models: The Goldfish Loss Approach

Practical Solutions for Mitigating Memorization in Language Models

Addressing Privacy and Copyright Risks

Language models can pose privacy and copyright risks by memorizing and reproducing training data. This can lead to conflicts with licensing terms and exposure of sensitive information. To mitigate these risks, it’s crucial to address memorization during the initial model training.

Goldfish Loss Training Technique

Researchers have developed the “goldfish loss” training technique to reduce memorization in language models. This method excludes a random subset of tokens from the loss computation during training, preventing the model from memorizing exact sequences from its training data. Extensive experiments have shown that goldfish loss significantly reduces memorization with minimal impact on performance.

Quantifying and Mitigating Memorization

Researchers have explored various methods to quantify and mitigate memorization in language models, including extracting training data via prompts, differentially private training, and regularization methods. Innovative approaches like consistent token masking have also been developed to prevent the model from learning specific data passages verbatim.

Enhancing Privacy in Industrial Applications

The goldfish loss effectively prevents memorization in large language models across different training scenarios. Despite limitations against certain adversarial extraction methods, it remains a viable strategy for enhancing privacy in industrial applications.

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