Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs
<|reference_start|>Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs: 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.<|reference_end|>
@article{hans2024be,
    title={Be like a Goldfish, Don't Memorize! Mitigating Memorization in
  Generative LLMs},
    author={Abhimanyu Hans, Yuxin Wen, Neel Jain, John Kirchenbauer, Hamid Kazemi,
  Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping,
  Abhinav Bhatele, Tom Goldstein},
    journal={arXiv preprint arXiv:2406.10209},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10209},
    primaryClass={cs.CL}
}