Binary Text Classification

Binary text classification focuses on automatically assigning one of two labels to textual data, a fundamental task with broad applications. Current research emphasizes improving model accuracy and calibration, particularly using transformer-based models like BERT and its variants, as well as exploring alternative architectures such as Kolmogorov-Arnold Networks for enhanced interpretability. Challenges remain in ensuring model generalizability across diverse datasets and domains, and in addressing the reliability and trustworthiness of predictions, especially when using large language models for annotation. These advancements are crucial for various fields, including information retrieval, sentiment analysis, and the detection of harmful online content.