Question Representation

Question representation research focuses on developing effective methods to encode questions for various downstream tasks, such as question answering and knowledge tracing, aiming to improve accuracy and efficiency. Current research emphasizes leveraging large language models (LLMs) and graph neural networks (GNNs) to capture semantic meaning and contextual information within questions, often incorporating contrastive learning and other techniques to enhance representation quality. These advancements have significant implications for personalized learning, improved information retrieval, and the development of more robust and interpretable AI systems across diverse applications.