The field of natural language processing is moving towards more sophisticated and nuanced approaches to text classification, with a particular emphasis on hierarchical classification and the application of large language models. Researchers are exploring new methods for leveraging hierarchical information in text data, such as the use of contrastive semantic highlighting and fuzzy logic, to improve the accuracy and efficiency of classification systems. Additionally, there is a growing interest in the development of more robust and scalable language models, with a focus on semi-supervised and iterative frameworks for building hierarchical text classifiers. Noteworthy papers in this area include: A BERT-based Hierarchical Classification Model, which introduces a novel hierarchical text classification approach based on BERT. An Auditable Pipeline for Fuzzy Full-Text Screening, which presents a scalable and auditable pipeline for full-text screening in systematic reviews. LLM-as-classifier, which proposes a comprehensive semi-supervised framework for building hierarchical text classifiers using large language models. Speculating LLMs' Chinese Training Data Pollution, which investigates the pollution of Chinese training data in large language models. From BERT to LLMs, which compares and understands Chinese classifier prediction in language models. AI-Powered Detection of Inappropriate Language, which evaluates the use of small language models and pre-trained large language models for detecting inappropriate language in medical school curricula. Cross-Platform E-Commerce Product Categorization, which develops and deploys a multimodal hierarchical classification framework for e-commerce product categorization.