The field of natural language processing is moving towards developing more effective methods for machine unlearning and text classification. Recent research has focused on improving the efficiency and accuracy of unlearning sensitive content from large language models, with a particular emphasis on understanding the factors that affect unlearning success. Additionally, innovations in text classification have led to the development of novel approaches such as batch aggregation, which can improve classification accuracy by modeling the dependence between augmented texts. Noteworthy papers in this area include one that proposes a sharpness-aware parameter selection method for machine unlearning, which can improve the efficacy of unlearning with low computational cost. Another notable paper introduces a Memory Removal Difficulty metric to quantify sample-level unlearning difficulty in large language models, and proposes an MRD-based weighted sampling method to optimize existing unlearning algorithms.