Exact and Efficient Unlearning for Large Language Model-based Recommendation (2404.10327v1)

Abstract: The evolving paradigm of LLM-based Recommendation (LLMRec) customizes LLMs through parameter-efficient fine-tuning (PEFT) using recommendation data. The inclusion of user data in LLMs raises privacy concerns. To protect users, the unlearning process in LLMRec, specifically removing unusable data (e.g., historical behaviors) from established LLMRec models, becomes crucial. However, existing unlearning methods are insufficient for the unique characteristics of LLM-Rec, mainly due to high computational costs or incomplete data erasure. In this study, we introduce the Adapter Partition and Aggregation (APA) framework for exact and efficient unlearning while maintaining recommendation performance. APA achieves this by establishing distinct adapters for partitioned training data shards and retraining only the adapters impacted by unusable data for unlearning. To preserve recommendation performance and mitigate considerable inference costs, APA employs parameter-level adapter aggregation with sample-adaptive attention for individual testing samples. Extensive experiments substantiate the effectiveness and efficiency of our proposed framework

Exact and Efficient Unlearning for LLM-based Recommendation Using APA Framework

Introduction

The integration of LLMs into recommendation systems has elevated their capability to understand and cater to user preferences. However, the inclusion of user data raises significant privacy concerns, necessitating the development of efficient unlearning mechanisms. The paper by Zhiyu Hu and colleagues introduces the Adapter Partition and Aggregation (APA) framework for exact and efficient unlearning in LLM-based recommendation systems (LLMRec). This framework addresses the need for precise data removal while preserving the recommendation performance, a challenge inadequately met by existing unlearning methods due to either high computational costs or incomplete data erasure.

Methodology

Overview of APA Framework

The APA framework innovatively employs data partitioning and adapter training to facilitate effective and efficient unlearning. By partitioning the training data into disjoint shards and training distinct adapters for each shard, APA allows for targeted retraining of adapters impacted by "unusable" data, thereby ensuring exact unlearning. The framework employs parameter-level adapter aggregation, incorporating sample-adaptive attention during inference to maintain recommendation performance without incurring prohibitive inference costs.

Adapter Partition and Retraining

The APA method partitions training data based on semantic characteristics, ensuring heterogeneity across and homogeneity within shards. Each shard is associated with an individual LoRA adapter, specifically trained on that shard. Upon a user's request to erase their data, only adapters corresponding to the affected shard are retrained, significantly reducing the computational load compared to full model retraining.

Adapter Aggregation

For inference, APA aggregates the weights of individual adapters into a unified adapter using a sample-adaptive approach. This means assigning more weight to adapters that are likely to yield better performance for a given testing sample, based on the performance of similar validation samples. This strategy allows APA to leverage the strengths of individual adapters, thus improving the overall recommendation quality.

Experiments and Results

The research conducted extensive experiments on real-world datasets, demonstrating the APA method's effectiveness in achieving exact unlearning, while substantially maintaining or even enhancing recommendation performance. Compared to existing methods, APA presents a significant improvement in unlearning efficiency, with the retraining process being notably faster, thereby addressing one of the critical challenges in LLMRec unlearning. Furthermore, the framework's utility in preserving recommendation performance is evident, with APA outperforming or matching the state-of-the-art, despite the reduced computational cost and enhanced privacy protection.

Implications and Future Developments

The development of the APA framework represents a significant step forward in the realms of privacy protection and model efficiency within the LLMRec context. By addressing the precise unlearning challenge directly and efficiently, APA provides a practical solution to the privacy concerns that have become increasingly prevalent with the widespread use of LLMs in recommendation systems.

Looking ahead, the methodology establishes a foundation for future research in efficient unlearning across various domains beyond recommendation systems. The partition and aggregation strategy, coupled with the precise retraining method, offers a versatile framework that can be adapted to different LLM applications, including those with stringent privacy requirements or where model performance is critical.

Moreover, the research opens pathways to further explore parameter-efficient tuning techniques and their integration with unlearning processes. The optimization of these techniques could lead to more granular control over unlearning, potentially reducing computational costs even further while ensuring data privacy and model performance are upheld.

In conclusion, the APA framework's introduction marks a pivotal advancement in addressing the dual challenges of model efficiency and privacy in the context of LLM-based recommendation systems. As the research community continues to navigate the complexities of incorporating LLMs into practical applications, methodologies like APA serve as crucial enablers, balancing performance with ethical and legal considerations.