@inproceedings{wang-etal-2025-trusteval,
    title = "{TRUSTEVAL}: A Dynamic Evaluation Toolkit on Trustworthiness of Generative Foundation Models",
    author = "Wang, Yanbo  and
      Ye, Jiayi  and
      Wu, Siyuan  and
      Gao, Chujie  and
      Huang, Yue  and
      Chen, Xiuying  and
      Zhao, Yue  and
      Zhang, Xiangliang",
    editor = "Dziri, Nouha  and
      Ren, Sean (Xiang)  and
      Diao, Shizhe",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-demo.8/",
    pages = "70--84",
    ISBN = "979-8-89176-191-9",
    abstract = "Ensuring the trustworthiness of Generative Foundation Models (GenFMs) is a pressing challenge as they gain widespread use. Existing evaluation toolkits are often limited in scope, dynamism, and flexibility. This paper introduces TRUSTEVAL, a dynamic and comprehensive toolkit designed for evaluating GenFMs across various dimensions. TRUSTEVAL supports both dynamic dataset generation and evaluation, offering advanced features including comprehensiveness, usability, and flexibility. TRUSTEVAL integrates diverse generative models, datasets, evaluation methods, metrics, inference efficiency enhancement, and evaluation report generation. Through case studies, we demonstrate TRUSTEVAL{'}s potential to advance the trustworthiness evaluation of GenFMs."
}