Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs

Original Paper: https://arxiv.org/abs/2406.10209

By: Abhimanyu Hans, Yuxin Wen, Neel Jain, John Kirchenbauer, Hamid Kazemi, Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping, Abhinav Bhatele, Tom Goldstein

Abstract:

Large language models can memorize and repeat their training data, causing privacy and copyright risks. To mitigate memorization, we introduce a subtle modification to the next-token training objective that we call the goldfish loss. During training, a randomly sampled subset of tokens are excluded from the loss computation. These dropped tokens are not memorized by the model, which prevents verbatim reproduction of a complete chain of tokens from the training set. We run extensive experiments training billion-scale Llama-2 models, both pre-trained and trained from scratch, and demonstrate significant reductions in extractable memorization with little to no impact on downstream benchmarks.

Summary Notes

Large Language Models (LLMs) have demonstrated remarkable proficiency in generating human-like text, but their impressive capabilities come with a downside: the tendency to memorize and regurgitate training data. This poses significant privacy and copyright risks.

In a recent research study, a team of engineers proposed a novel solution called the Goldfish Loss to address this issue.

Introduction: The Problem of Memorization

Memorization in LLMs refers to the phenomenon where models store and later reproduce verbatim copies of their training data. This behavior is problematic for several reasons:

• Privacy Risks: Regenerated training data may contain Personally Identifiable Information (PII) or sensitive content.
• Copyright Risks: The output of LLMs could include copyrighted material, leading to legal complications for both users and providers of these models.

To tackle these challenges, the researchers introduced the Goldfish Loss, a modification to the next-token prediction objective used during model training.

Methodology: The Goldfish Loss

The Goldfish Loss is a subtle yet effective tweak to the standard training procedure. Here's how it works:

1. Forward Pass: The model processes all tokens in a training batch as usual.
2. Loss Calculation: Unlike the traditional approach where the next-token prediction loss is calculated for all tokens, the Goldfish Loss excludes a pseudo-random subset of tokens (e.g., 25%) from the loss computation.
3. Backward Pass: The model updates its weights based on the loss calculated from the remaining tokens.

By excluding certain tokens from the loss calculation, the model does not learn to reproduce these specific tokens.

At inference time, when the model encounters these "dropped" tokens, it has to make an educated guess, thereby preventing it from generating the exact sequence from the training data.

Key Findings and Results

The researchers conducted extensive experiments to validate the effectiveness of the Goldfish Loss. They trained billion-scale LLaMA-2 models using both standard and Goldfish Loss methods. Here are some key findings:

• Reduction in Memorization: The models trained with Goldfish Loss showed a significant reduction in verbatim memorization. For instance, when prompted with the opening of the first chapter of "Harry Potter," the standard model regenerated the original text, while the Goldfish Loss model did not.
• Utility Preservation: Despite the reduction in memorization, the Goldfish Loss models maintained comparable performance on downstream tasks. This indicates that the models still learned effectively from the training data.

Implications and Potential Applications

The Goldfish Loss has several important implications:

• Privacy and Security: By mitigating memorization, the Goldfish Loss enhances the privacy and security of LLMs, making them safer to deploy in commercial applications.
• Legal Compliance: Reducing the risk of copyright infringement can help companies avoid legal issues related to the unauthorized use of copyrighted material.
• Broader Adoption: The simplicity and effectiveness of the Goldfish Loss make it an attractive technique for widespread adoption in the training of LLMs.

Real-World Applications

The Goldfish Loss can be particularly useful in the following scenarios:

• Customer Support: LLMs used in customer support applications can handle user data with greater privacy, reducing the risk of sensitive information leakage.
• Content Generation: In industries like marketing and media, where the generation of unique content is crucial, the Goldfish Loss ensures that the output is original and free from verbatim reproductions of training data.

Conclusion

The Goldfish Loss presents a promising solution to the problem of memorization in LLMs. By strategically excluding a subset of tokens from the loss computation, this technique effectively reduces the risk of verbatim memorization without compromising the model's performance on downstream tasks.

As we continue to advance the capabilities of LLMs, techniques like the Goldfish Loss will be essential in ensuring that these models are both powerful and safe to use.

Let's embrace the wisdom of the goldfish and train our models to forget just enough to make them secure and compliant!