Factual Serialization

Factual serialization focuses on representing factual information from various data sources, such as tabular data and medical images, in a structured format suitable for machine learning and natural language processing. Current research emphasizes efficient serialization techniques, particularly for large language models, exploring methods like contrastive learning and novel serialization formats (e.g., LaTeX) to improve model performance and efficiency in tasks like classification and report generation. This work is significant for advancing applications in diverse fields, including healthcare (e.g., automated report generation from medical images) and scientific computing (e.g., efficient simulation and storage of large-scale neural networks), by enabling better integration of diverse data types and improving the scalability and reliability of machine learning models.