Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximization

TL;DR

This paper introduces EMSO, a novel entropy maximization framework with selective optimization, to effectively erase memorized textual sequences in large language models while maintaining their utility.

Contribution

It proposes a new erasure method based on entropy maximization and contrastive gradient metrics that outperforms existing techniques in forgetting memorized data without utility loss.

Findings

EMSO achieves better memorization erasure with minimal utility impact.

The contrastive gradient metric effectively localizes influential weights for erasure.

Experiments show superior performance across multiple model scales.

Abstract

Large Language Models (LLMs) have been found to memorize and recite some of the textual sequences from their training set verbatim, raising broad concerns about privacy and copyright issues when using LLMs. This Textual Sequence Memorization (TSM) phenomenon leads to a high demand to regulate LLM output to prevent it from generating certain memorized text to meet user requirements. However, our empirical study reveals that existing methods for TSM erasure fail to forget massive memorized samples without substantially jeopardizing the model utility. To achieve a better trade-off between the effectiveness of TSM erasure and model utility in LLMs, our paper proposes a new framework based on Entropy Maximization with Selective Optimization (EMSO), where the updated weights are chosen with a novel contrastive gradient metric without any participation of additional model or data. Our analysis shows that training with the entropy maximization loss has a more stable optimization process and better keeps model utility than existing methods. The contrastive gradient metric localizes the most influential weight for TSM erasure by taking both the gradient magnitude and direction into consideration. Extensive experiments across three model scales demonstrate that our method excels in handling large-scale forgetting requests while preserving model ability in language generation and reasoning.