摘要
1 Introduction Large Language Models(LLMs)possess massive parameters and are trained on vast datasets,demonstrating exceptional proficiency in various tasks.The remarkable advancements in LLMs also inspire the exploration of leveraging LLMs as recommenders(LLMRec),whose effectiveness stems from extensive open-world knowledge and reasoning ability in LLMs[1].LLMRec obtains the recommendation ability through instruction tuning on the user interaction data.But in many cases,it is also crucial for LLMRec to forget specific user data,which is referred to as recommendation unlearning[2],as shown in Fig.1.
基金
supported by the National Natural Science Foundation of China(Grant No.62177033)
sponsored by the Huawei Innovation Research Program.
