Automated data extraction pipeline for large language model training

An apparatus for data processing, comprising:

• one or more memories storing processor-executable code;
• and
• one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to:
• extract a plurality of code segments from a plurality of natural language question-answer combinations, wherein each question-answer combination comprises a provided input, a provided output, and a provided code segment formatted to transform the provided input into the provided output;
• generate, using a first large language model, a predicted output from a question portion of a first natural language question-answer combination of the plurality of natural language question-answer combinations;
• execute a first extracted code segment from the extracted plurality of code segments to generate a first actual output of the first extracted code segment;
• generate one or more data samples for a second large language model based at least in part on a comparison of the first actual output of the first extracted code segment to the predicted output from the first large language model;
• and
• train the second large language model using the one or more data samples.

A non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by one or more processors to:

• extract a plurality of code segments from a plurality of natural language question-answer combinations, wherein each question-answer combination comprises a provided input, a provided output, and a provided code segment formatted to transform the provided input into the provided output;
• generate, using a first large language model, a predicted output from a question portion of a first natural language question-answer combination of the plurality of natural language question-answer combinations;
• execute a first extracted code segment from the extracted plurality of code segments to generate a first actual output of the first extracted code segment;
• generate one or more data samples for a second large language model based at least in part on a comparison of the first actual output of the first extracted code segment to the predicted output from the first large language model;
• and
• train the second large language model using the one or more data samples.