Shot Text Classification

Few-shot text classification focuses on training accurate text classifiers using minimal labeled data, a crucial challenge in natural language processing due to the high cost of annotation. Current research emphasizes improving model performance through techniques like prompt engineering, contrastive learning, and meta-learning, often leveraging pre-trained language models (PLMs) such as BERT and GPT variants, and exploring parameter-efficient fine-tuning methods. These advancements are significant because they enable the development of effective classifiers for domains with limited labeled data, impacting various applications from sentiment analysis to content moderation. Furthermore, research is actively investigating methods to improve the explainability and fairness of these models.