Mathematical Text

Mathematical text processing research focuses on developing computational methods to understand and reason with mathematical information expressed in natural language and symbolic forms. Current efforts concentrate on improving large language models' ability to extract mathematical concepts, generate proofs, and solve word problems, often employing architectures like BERT and UNet, and leveraging techniques such as operator splitting and multigrid methods. These advancements are crucial for automating tasks in mathematical research, education, and applications requiring mathematical reasoning, such as scientific literature analysis and automated theorem proving. The ultimate goal is to bridge the gap between human mathematical understanding and machine processing capabilities.