  In recent years, several models have improved the capacity to generate
synthetic tabular datasets. However, such models focus on synthesizing simple
columnar tables and are not useable on real-life data with complex structures.
This paper puts forward a generic framework to synthesize more complex data
structures with composite and nested types. It then proposes one practical
implementation, built with causal transformers, for struct (mappings of types)
and lists (repeated instances of a type). The results on standard benchmark
datasets show that such implementation consistently outperforms current
state-of-the-art models both in terms of machine learning utility and
statistical similarity. Moreover, it shows very strong results on two complex
hierarchical datasets with multiple nesting and sparse data, that were
previously out of reach.
