@inproceedings{jain-etal-2025-autoeval,
    title = "{A}uto{E}val-{T}o{D}: Automated Evaluation of Task-oriented Dialog Systems",
    author = "Jain, Arihant  and
      Aggarwal, Purav  and
      Sahay, Rishav  and
      Dong, Chaosheng  and
      Saladi, Anoop",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-long.508/",
    pages = "10133--10148",
    ISBN = "979-8-89176-189-6",
    abstract = "Task-oriented Dialog systems (ToD) are essential in automating user interactions, but their complex design and dynamic nature make evaluation particularly challenging. Current evaluation methodologies heavily depend on human annotators, which can be inefficient, subjective, and expensive to scale. To advance the field, there is a pressing need for a reliable, scalable, and systematic evaluation framework that can provide comprehensive insights into ToD system performance. In this paper, we propose, AutoEval-TOD, an automated end-to-end evaluation framework using large language models (LLMs). Our framework first interacts with the ToD system and then assesses its performance across key dimensions by analyzing both the ToD`s responses and internal states. We validate our approach by applying it to multiple ToD systems, highlighting its adaptability and potential for widespread use in both research and industrial settings."
}