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

1. 1Identify the need for synthetic dataset generation in a specific domain
2. 2Choose between LLMs and PGMs based on the complexity of the schema and desired dataset distribution
3. 3Implement the Amalgam algorithm to combine the strengths of LLMs and PGMs
4. 4Evaluate the generated synthetic dataset for accuracy and realism

Data scientists and AI engineers on a team can benefit from Amalgam as it addresses the limitations of both LLMs and PGMs in generating synthetic datasets, enabling more accurate and realistic data for advanced analytics

💡 Combining LLMs and PGMs can produce synthetic datasets with both complex schemas and realistic distributions

💡 Amalgam: A hybrid LLM-PGM synthesis algorithm for generating accurate & realistic synthetic datasets