AIGC大模型测评综述:使能技术、安全隐患和应对OA北大核心CSTPCD

Artificial intelligence generated content(AIGC)models have attracted widespread attention and applica-tion worldwide due to their excellent content generation capabilities.However,the rapid development of AIGC large models also brings a series of hidden dangers,such as concerns about interpretability,fairness,security,and privacy preservation of model-generated content.In order to reduce the unknowable risks and their harms,it be-comes more and more important to carry out a comprehensive measurement and evaluation of AIGC large models.Academics have initiated AIGC large model evaluation studies aiming to effectively address the related challenges and avoid potential risks.This paper summarizes and analyzes the AIGC large model evaluation studies.Firstly,an overview of the model evaluation process is provided,covering model evaluation pre-preparation and corresponding measurement indicators,and existing measurement benchmarks are systematically organized.Secondly,the repre-sentative applications of the AIGC large model in finance,politics and healthcare and their problems are discussed.Then,the measurement methods are studied in depth through different perspectives,such as interpretability,fair-ness,robustness,security and privacy,and the new issues that need to be paid attention to AIGC large model evaluation are deconstructed,and the ways to cope with the new challenges of large model evaluation are pro-posed.Finally,the future challenges of AIGC large model evaluation are discussed,and its future development direc-tion is envisioned.