Extreme Multi Label

Extreme multi-label classification (XMC) tackles the problem of assigning multiple labels from an extremely large set (often millions) to a single data instance, typically text. Current research focuses on improving model efficiency and accuracy, particularly for less frequent ("tail") labels, using architectures like transformers and dual encoders, and addressing challenges like missing labels and imbalanced data through techniques such as contrastive learning and optimal transport. XMC has significant implications for various applications, including e-commerce, information retrieval, and question answering, where efficient and accurate multi-label prediction is crucial. The field is actively developing improved evaluation metrics to better capture real-world performance, especially concerning long-tail label prediction.