The field of synthetic tabular data generation is moving towards dependency-aware models that can preserve inter-attribute relationships, such as functional dependencies and logical dependencies. This is crucial for applications in privacy-sensitive domains like healthcare. Recent innovations have also focused on ultra-fast generation methods and disjoint generative models that can increase privacy while maintaining utility. In the area of microbiome analysis, large language models are being explored for predicting microbial ontology and pathogen risk from environmental metadata, showing promising results. Additionally, diffusion-based dependency-aware multimodal imputation methods are being developed to address the challenges of sparse and noisy microbiome data. Noteworthy papers include:

• A framework that proposes the Hierarchical Feature Generation Framework for synthetic tabular data generation, which improves the preservation of functional dependencies and logical dependencies.
• A lightweight generative framework that explicitly captures sparse dependencies via an LLM-induced graph, reducing constraint violations and accelerating generation.
• A novel framework that combines diffusion-based generative modeling with a Dependency-Aware Transformer for microbiome data imputation, achieving higher accuracy and generalizability.