Textual Knowledge

Textual knowledge research focuses on effectively utilizing and manipulating information encoded in text, aiming to improve tasks like fact-checking, question answering, and knowledge base maintenance. Current research emphasizes developing robust models, often leveraging transformer-based architectures and techniques like noise contrastive estimation and semantic similarity matching, to handle challenges such as low-resource languages, large knowledge bases, and the need for explainability and faithfulness in information processing. These advancements have significant implications for various applications, including improving the accuracy and reliability of online information, enhancing cybersecurity threat detection, and facilitating more efficient and reliable knowledge base updates.