Mitigating Memorization In Language Models (2410.02159v1)

Abstract: LLMs (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that data. This ability to extract training data can be problematic, for example, when data are private or sensitive. In this work, we investigate methods to mitigate memorization: three regularizer-based, three finetuning-based, and eleven machine unlearning-based methods, with five of the latter being new methods that we introduce. We also introduce TinyMem, a suite of small, computationally-efficient LMs for the rapid development and evaluation of memorization-mitigation methods. We demonstrate that the mitigation methods that we develop using TinyMem can successfully be applied to production-grade LMs, and we determine via experiment that: regularizer-based mitigation methods are slow and ineffective at curbing memorization; fine-tuning-based methods are effective at curbing memorization, but overly expensive, especially for retaining higher accuracies; and unlearning-based methods are faster and more effective, allowing for the precise localization and removal of memorized information from LM weights prior to inference. We show, in particular, that our proposed unlearning method BalancedSubnet outperforms other mitigation methods at removing memorized information while preserving performance on target tasks.

• The paper introduces unlearning methods that efficiently excise memorized content while preserving task accuracy.
• It demonstrates that regularizer-based methods are slow and fine-tuning compromises accuracy, emphasizing the superiority of unlearning approaches.
• By using TinyMem for rapid prototyping, the research validates that these mitigation strategies scale effectively to production-grade language models.

Mitigating Memorization in LLMs

The research paper investigates the phenomenon of memorization in LLMs (LMs) and explores various strategies to mitigate it. Memorization in this context refers to an LM's tendency to retain verbatim information from its training data, which can lead to privacy concerns when sensitive data is involved.

Key Findings

The authors examine different methods to mitigate memorization, categorizing them into regularizer-based, fine-tuning-based, and unlearning-based approaches. Notably, they propose five new unlearning methods.

1. Regularizer-Based Methods: These methods attempt to reduce memorization by modifying the training process itself. However, the paper reports that these methods are generally slow and not particularly effective.
2. Fine-Tuning-Based Methods: While effective at reducing memorization, these methods often come at the cost of increased training time and reduced accuracy on target tasks.
3. Unlearning-Based Methods: These methods are highlighted as both faster and more effective. Specifically, the proposed BalancedSubnet method stands out for being able to efficiently excise memorized information while preserving task performance.

Experimental Framework

For the development and evaluation of these methods, the authors introduce TinyMem, a suite of small, computationally efficient LLMs. These models allow for rapid prototyping and testing of memorization mitigation strategies. The authors demonstrate that strategies developed on TinyMem can be successfully applied to larger, production-grade LMs.

Results

• Unlearning methods outperformed other strategies: The paper concludes that unlearning methods are optimal, and the BalancedSubnet method specifically showed superior performance in efficiently mitigating memorization while maintaining model accuracy.
• Memorization is context dependent: The results also emphasize that models are more likely to memorize data based on certain factors such as data duplication and model size.

Implications and Future Directions

The findings of this paper hold significant implications for the development and deployment of LMs, particularly in contexts where data privacy is paramount. By focusing on unlearning methods, developers can create models that are less likely to inadvertently expose sensitive information. The introduction of TinyMem also suggests a pathway toward more efficient evaluation of mitigation strategies.

Future research might explore:

• Further optimization of unlearning methods for scalability and efficiency in real-world settings.
• Investigation into the balance between reducing memorization and maintaining performance across diverse tasks.
• Development of adaptive unlearning strategies that can dynamically respond to different types of data.

In summary, this paper provides a comprehensive examination of memorization in LMs and suggests practical solutions for mitigating its potential risks, thus contributing valuable insights to the field of AI model safety and privacy.