The invention discloses a dynamic random language input generation method based on
large model architecture and training data features, and relates to the technical field of
large model testing and
natural language processing crossing. Comprising the steps of obtaining a target
large model type, training data and a
test scene demand, and performing large model-classical feature collaborative preparation; the
noise tolerance is corrected by training
data field concentration degree and grammar specification degree quantification in combination with a model type coefficient, and the
confusion degree is finely adjusted according to a
trigger rate; effective conflicts are screened by means of cross-
modal consistency loss, and input effectiveness is verified in a three-dimensional mode through the
trigger rate, the strength and the influence degree; calculating power and
resource efficiency quantitative feedback iteration is carried out, stage adaptive input is generated based on
time sequence features, and cross-task feature
multiplexing is realized through task similarity; performing low-sensitive word disturbance and confrontation-random balance enhancement as required, verifying a result, and returning to a parameter
adaptation link for readjustment if the result does not reach the standard; finally,
high input adaptation and high-efficiency generation are realized, and the test is ensured to be accurate and efficient.