Model and variable selection using large language model

A method to select a statistical model from a plurality of statistical models, the method comprising:

• obtaining, by a processor, output from a data structure, wherein the data structure is configured to:
• store a Large Language Model (LLM) that is pre-trained using a training data;
• obtain inputs from an interface;
• and
• execute the LLM based on the training data and the inputs obtained from the interface to determine a probability of each LLM token input through the interface, wherein each said probability is based on relationships between variables and models identified by the LLM during pre-training;
• processing, by the processor, the output obtained from the data structure by executing a probabilistic interference algorithm based on the probabilities of LLM tokens;
• selecting, by the processor, the statistical model from the plurality of statistical models based on processing of the output obtained from the data structure;
• and
• outputting, by the processor, the selected statistical model via an output generator.

A non-transitory computer-readable storage medium having instructions stored thereupon which, when executed by a processor, cause the processor to:

• obtain output from a data structure, wherein the data structure is configured to:
• store a Large Language Model (LLM) that is pre-trained using a training data;
• obtain inputs from an interface;
• and
• execute the LLM based on the training data and the inputs obtained from the interface to output one or more of:
• LLM tokens, probabilities of LLM tokens and LLM parameters;
• process the output obtained from the data structure by executing a probabilistic interference algorithm;
• select a statistical model from a plurality of statistical models based on processing of the output obtained from the data structure;
• and
• output the selected statistical model via an output generator.