Unified Parameter-Efficient Unlearning for LLMs
This addresses privacy and security concerns for users of fine-tuned LLMs, though it is incremental as it builds on existing unlearning and PEFT techniques.
The paper tackles the problem of sensitive information retention in large language models after fine-tuning by introducing LLMEraser, a parameter-efficient unlearning framework that uses influence functions for precise adjustments, achieving efficient management of various unlearning scenarios without compromising model performance.
The advent of Large Language Models (LLMs) has revolutionized natural language processing, enabling advanced understanding and reasoning capabilities across a variety of tasks. Fine-tuning these models for specific domains, particularly through Parameter-Efficient Fine-Tuning (PEFT) strategies like LoRA, has become a prevalent practice due to its efficiency. However, this raises significant privacy and security concerns, as models may inadvertently retain and disseminate sensitive or undesirable information. To address these issues, we introduce a novel instance-wise unlearning framework, LLMEraser, which systematically categorizes unlearning tasks and applies precise parameter adjustments using influence functions. Unlike traditional unlearning techniques that are often limited in scope and require extensive retraining, LLMEraser is designed to handle a broad spectrum of unlearning tasks without compromising model performance. Extensive experiments on benchmark datasets demonstrate that LLMEraser excels in efficiently managing various unlearning scenarios while maintaining the overall integrity and efficacy of the models.