The application belongs to the technical field of large
language model optimization, and discloses a large data driving-based large
language model optimization method and
system. The method comprises the following steps: inputting public text data into a test model to obtain test reply data, and calculating the test deviation between the test reply data and the reply data; when the test deviation meets a preset correction trigger condition, corresponding
correction text data and correction reply data are identified; data supplement
processing is performed based on the
correction text data and the correction reply data to generate supplement data for model optimization; the data supplement
processing comprises similarity comparison of the
correction text data to divide the correction text data into data groups, generation of error registration data, and performance of a supplement operation based on a
statistical analysis result. Through
inductive analysis of the deviation and
quality control of the supplement data, the application improves the reply accuracy, logical consistency and robustness of the target
language model.