NLP Classification Task

NLP classification tasks aim to automatically categorize text into predefined classes, a fundamental problem with broad applications. Current research focuses on improving efficiency and interpretability, exploring techniques like cross-lingual knowledge transfer to address data scarcity in low-resource languages, information-theoretic approaches to understand input feature importance, and dynamic data subset selection to reduce computational costs of training large language models (LLMs) such as BERT and GPT-2. These advancements are crucial for enhancing the accuracy, scalability, and trustworthiness of NLP systems across diverse applications, from sentiment analysis to question answering.