Automatic Usefulness Prediction

Automatic usefulness prediction aims to computationally assess the value or relevance of various data elements, ranging from text and code to images and speech. Current research focuses on developing and evaluating methods for this prediction across diverse data types, employing machine learning models like transformers and neural networks, often incorporating external information (e.g., emotion, source reliability) to enhance accuracy. This field is significant for improving information retrieval, enhancing human-computer interaction, and optimizing machine learning training processes by prioritizing high-value data.