Logical Form

Logical form research centers on representing the meaning of natural language or structured data (like tables) in a formal, computationally tractable way, primarily to improve the accuracy and explainability of AI systems. Current research focuses on automatically generating and utilizing these logical forms in tasks such as question answering, natural language generation, and graph neural network design, often employing neural network architectures like sequence-to-sequence models and leveraging techniques like self-training and data augmentation to overcome data scarcity and improve model performance. This work has significant implications for building more robust and trustworthy AI systems, particularly in applications requiring high fidelity and explainability, such as those involving knowledge bases and business decision support.