@inproceedings{peng-etal-2025-ip,
    title = "{IP}-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data",
    author = "Peng, Bo  and
      Wang, Zhiheng  and
      Gong, Heyang  and
      Lu, Chaochao",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.923/",
    doi = "10.18653/v1/2025.findings-emnlp.923",
    pages = "17007--17040",
    ISBN = "979-8-89176-335-7",
    abstract = "In modern dialogue systems, the ability to implicitly infer user backgrounds from conversations and leverage this information for personalized assistance is crucial. However, the scarcity of high-quality data remains a fundamental challenge to evaluating and improving this capability. Traditional dataset construction methods are labor-intensive, resource-demanding, and raise privacy concerns. To address these issues, we propose a novel approach for automatic synthetic data generation and introduce the **I**mplicit **P**ersonalized **Dialog**ue (**IP-Dialog**) benchmark along with a training dataset, covering 10 tasks and 12 user attribute types. Additionally, we develop a systematic evaluation framework with four metrics to assess both attribute awareness and reasoning capabilities. We further propose five causal graphs to elucidate models' reasoning pathways during implicit personalization. Extensive experiments yield insightful observations and prove the reliability of our dataset."
}