Research on Algorithm Multi-Dimensional Testing and Evaluation for NLP Intelligent Algorithms

This paper aims to enhance the efficiency and precision of intelligent algorithm evaluations and to advance the standardization of testing and evaluation processes. By dissecting the testing mechanisms of intelligent systems, the essence of intelligent algorithms, and the factors that influence algorithmic models, this thesis introduces a multi-dimensional testing and evaluation approach for intelligent algorithms. This approach addresses several key issues: the high costs associated with physical or semi-physical simulation verification, the insufficient validation of intelligent algorithms, the absence of testing techniques for non-functional requirements, and the lack of comprehensive indicators within current algorithm evaluation methodologies. Additionally, it tackles the problem of incomplete technical indices in algorithm evaluation.