The evolving paradigm of Large Language Model-based Recom- mendation (LLMRec) customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) using recommenda- tion 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 Ag- gregation (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 un- learning. 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

翻译：基于大规模语言模型的推荐（LLMRec）这一新兴范式，通过使用推荐数据进行参数高效微调（PEFT）来定制大规模语言模型（LLM）。用户数据引入LLM引发了隐私问题。为保护用户，LLMRec中的遗忘过程——即从已建立的LLMRec模型中移除不可用数据（如历史行为）——变得至关重要。然而，现有遗忘方法难以适应LLMRec的独特特性，主要原因是计算成本过高或数据擦除不彻底。在本研究中，我们提出适配器分区与聚合（APA）框架，在保持推荐性能的同时实现精确且高效的遗忘。APA通过为分区的训练数据切片建立独立适配器，并在遗忘时仅重新训练受不可用数据影响的适配器来实现这一目标。为保持推荐性能并降低显著推理成本，APA采用参数级适配器聚合方法，结合对个体测试样本的自适应注意力机制。大量实验验证了我们所提框架的有效性和高效性。