Amalgam: Hybrid LLM-PGM Synthesis Algorithm for Accuracy and Realism

ArXi:2603.27254v1 Announce Type: cross To generate synthetic datasets, e.g., in domains such as healthcare, the literature proposes approaches of two main types: Probabilistic Graphical Models (PGMs) and Deep Learning models, such as LLMs. While PGMs produce synthetic data that can be used for advanced analytics, they do not complex schemas and datasets. LLMs on the other hand, complex schemas but produce skewed dataset distributions, which are less useful for advanced analytics.