@inproceedings{zhao-etal-2025-personalens,
    title = "{P}ersona{L}ens: A Benchmark for Personalization Evaluation in Conversational {AI} Assistants",
    author = "Zhao, Zheng  and
      Vania, Clara  and
      Kayal, Subhradeep  and
      Khan, Naila  and
      Cohen, Shay B  and
      Yilmaz, Emine",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.findings-acl.927/",
    doi = "10.18653/v1/2025.findings-acl.927",
    pages = "18023--18055",
    ISBN = "979-8-89176-256-5",
    abstract = "Large language models (LLMs) have advanced conversational AI assistants. However, systematically evaluating how well these assistants apply personalization{---}adapting to individual user preferences while completing tasks{---}remains challenging. Existing personalization benchmarks focus on chit-chat, non-conversational tasks, or narrow domains, failing to capture the complexities of personalized task-oriented assistance. To address this, we introduce PersonaLens, a comprehensive benchmark for evaluating personalization in task-oriented AI assistants. Our benchmark features diverse user profiles equipped with rich preferences and interaction histories, along with two specialized LLM-based agents: a user agent that engages in realistic task-oriented dialogues with AI assistants, and a judge agent that employs the LLM-as-a-Judge paradigm to assess personalization, response quality, and task success. Through extensive experiments with current LLM assistants across diverse tasks, we reveal significant variability in their personalization capabilities, providing crucial insights for advancing conversational AI systems."
}