The application discloses a
large model routing
data synthesis method with diversified expression styles, constructs a
user agent to simulate the language style and preference of a real user, generates query questions with diversified styles and clear
semantics, inputs the query questions into a candidate
language model for reasoning, and generates a benchmark answer by a large
language model with superior performance; based on the comparison between the output of the candidate model and the benchmark answer, the content quality is automatically evaluated, the
user satisfaction evaluation is simulated in combination with the
user agent features, and multi-dimensional routing labels are comprehensively generated; finally, the question, the candidate model output and the routing
label are packaged to form a routing
data set, which is used for training or evaluating a
large model router. The application can automatically synthesize a routing
data set covering multiple expression styles, multiple question types and performance differences of
multiple models, significantly improves the generalization ability and practicability of a
routing model in a real
open domain question and answer scene, and does not require high artificial labeling cost.