@inproceedings{k-etal-2025-aura,
    title = "{AURA}-{QG}: Automated Unsupervised Replicable Assessment for Question Generation",
    author = "K, Rajshekar  and
      Khadilkar, Harshad  and
      Bhattacharyya, Pushpak",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.159/",
    pages = "2979--2992",
    ISBN = "979-8-89176-298-5",
    abstract = "Question Generation (QG) is central to information retrieval, education, and knowledge assessment, yet its progress is bottlenecked by unreliable and non-scalable evaluation practices. Traditional metrics fall short in structured settings like document-grounded QG, and human evaluation, while insightful, remains expensive, inconsistent, and difficult to replicate at scale. We introduce AURA-QG: an Automated, Unsupervised, Replicable Assessment pipeline that scores question sets using only the source document. It captures four orthogonal dimensions i.e., answerability, non-redundancy, coverage, and structural entropy, without needing reference questions or relative baselines. Our method is modular, efficient, and agnostic to the question generation strategy. Through extensive experiments across four domains i.e., car manuals, economic surveys, health brochures, and fiction, we demonstrate its robustness across input granularities and prompting paradigms. Chain-of-Thought prompting, which first extracts answer spans and then generates targeted questions, consistently yields higher answerability and coverage, validating the pipeline{'}s fidelity. The metrics also exhibit strong agreement with human judgments, reinforcing their reliability for practical adoption. The complete implementation of our evaluation pipeline is publicly available."
}