PERFORMANCE OF NATURAL LANGUAGE-TO-LOGICAL FORM CONVERSION USING PREFERENCE ALIGNMENT TRAINING

Techniques are disclosed herein for automatically curating preference alignment training (PAT) data and subsequently using the PAT data to train a machine-learning model used to perform natural language-to-query language tasks. A first PAT dataset is generated using a first trained LLM in an execution-based data generation technique. A second PAT dataset is generated using a second trained LLM in a LLM-based data generation technique. The second PAT dataset is populated with synthetic data points generated by the second trained LLM. Each data point of first PAT dataset and the second PAT dataset comprises a natural language query and an executable and non-executable query language statement. The first PAT dataset and the second PAT dataset can be combined and filtered to generate a PAT dataset that is optimized for training a machine-learning model to overcome one or more weaknesses exhibited when generating query language statements responsive to natural language questions.