Key Points

1. Memorization in large language models (LLMs) can pose risks such as copyright infringement, privacy concerns, and potential legal issues. This can occur when models internally store and regenerate verbatim copies of training data, leading to potential misuse of intellectual property and sensitive information.

2. The paper introduces the "goldfish loss," a training modification for LLMs, designed to mitigate verbatim memorization of training data. This approach involves excluding a randomly sampled subset of tokens from the loss computation during training, preventing the model from memorizing these tokens and reproducing verbatim sequences from the training set.

3. The study conducted extensive experiments, including training billion-scale LLaMA-2 models with and without the goldfish loss, which demonstrated significant reductions in extractable memorization while minimally impacting downstream benchmarks.

4. The goldfish loss was shown to effectively prevent verbatim memorization in both extreme training scenarios and standard training regimens, providing evidence that the technique resists reproducing long training sequences.

5. The impact of the goldfish loss on model performance was assessed through various downstream evaluations, including knowledge-intensive reasoning benchmarks and raw language modeling ability. The results indicated that models trained with the goldfish loss showed comparable performance to standard training in most cases, with minimal impact on language modeling quality.

6. The paper addressed potential adversarial extraction methods, such as membership inference attacks and beam search, demonstrating that while goldfish models were resistant to long-form verbatim memorization, they were not impervious to certain adversarial attempts to extract information.

7. The study acknowledged limitations of the goldfish loss, emphasizing that it does not guarantee complete prevention of data extraction and may still be vulnerable to leakage under specific conditions. However, it highlighted the potential utility of the goldfish loss in industrial settings due to its simplicity, scalability, and relatively minor impacts on model performance.

8. The paper emphasized the need for future research on how the benefits of the goldfish loss scale to larger models and documented the potential advantages of the goldfish loss in empowering data owners and model training outfits to coexist harmoniously while respecting intellectual property expectations.

9. The research used significant resources and support from various organizations and highlighted the importance of research at the intersection of compliance and capability in advancing generative models and their applications.

Summary

This research paper introduces a technique called the "goldfish loss" to mitigate the memorization of training data by large language models. The goldfish loss involves excluding a random subset of tokens from the loss computation during training, which prevents the model from memorizing and reproducing complete chains of tokens from the training set.

Experiments with Billion-Scale Llama-2 Models
The paper reports on experiments with billion-scale Llama-2 models, both pre-trained and trained from scratch, and demonstrates significant reductions in extractable memorization without substantially impacting downstream performance. The authors find that models trained with the goldfish loss are able to resist memorization and verbatim reproduction of training data, even in scenarios designed to aggressively promote memorization.

Training and Testing of the Goldfish Loss
Specifically, the authors train a 7B parameter model for 100 epochs on a small number of Wikipedia articles and find that the goldfish loss model is able to resist memorization, while the standard training model memorizes most of the training data. The authors then test the goldfish loss on more standard training regimens and observe that the memorization metrics of goldfish models closely resemble models that never saw the training data.

Theoretical and Practical Considerations of Goldfish Loss
While the goldfish loss does not provide a theoretical guarantee against memorization, the authors demonstrate that it is able to significantly reduce extractable memorization under benign sampling conditions. However, the paper also notes that membership inference attacks can still successfully extract some information from goldfish models, albeit with reduced accuracy compared to standard training.

Practical Applications of Goldfish Loss
The authors argue that the goldfish loss represents a practical tool for industrial settings due to its simplicity, scalability, and relatively small impacts on model performance. They suggest that the goldfish loss can be selectively applied to high-risk documents or late stages of training to limit negative impacts on utility while focusing mitigation where it matters most.

Overall, this paper presents a novel and effective technique for mitigating the memorization of training data by large language models, while maintaining strong performance on downstream tasks.

Reference: https://arxiv.org/abs/2406.10209