Unsupervised Text

Unsupervised text analysis focuses on extracting meaningful information from text data without relying on labeled training examples, addressing the high cost and scarcity of annotated datasets. Current research emphasizes developing robust text representations using large language models (LLMs) and transformer-based architectures, often employing techniques like contrastive pre-training, instruction tuning, and similarity-based approaches for tasks such as classification and retrieval. These advancements are significant because they enable effective text processing in scenarios with limited or no labeled data, impacting diverse applications ranging from information retrieval and automated auditing to various domains lacking sufficient annotated resources.