Context Information

Context information, encompassing the surrounding data influencing a system's response, is a crucial area of research across numerous fields, aiming to improve model accuracy, robustness, and explainability. Current research focuses on how to effectively integrate contextual information into various models, including large language models (LLMs), vision-language models (VLMs), and other machine learning architectures, often employing techniques like retrieval-augmented generation (RAG), attention mechanisms, and contrastive learning. This work is significant because effective contextualization is vital for building reliable and trustworthy AI systems across applications ranging from natural language processing and computer vision to medical diagnosis and autonomous navigation.