Mitigating Memorization in Language Models: The Goldfish Loss Approach

Practical Solutions for Reducing Memorization in Language Models Language models can create privacy and copyright issues by memorizing and reproducing training data, potentially leading to conflicts with licensing terms and exposure of sensitive information. To address these risks, it's important to focus on mitigating 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 involves excluding 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 demonstrated 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, such as 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 technique effectively prevents memorization in large language models across different training scenarios. While it may have limitations against certain adversarial extraction methods, it remains a valuable strategy for enhancing privacy in industrial applications. Leverage AI for Business Transformation Discover how AI can transform your work processes and customer engagement. Identify automation opportunities, define KPIs, select an AI solution, and implement gradually to harness the power of AI for your business. For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com and stay tuned on our Telegram channel @itinai and Twitter @itinaicom.