PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data
Juntao Tan, Liangwei Yang, Zuxin Liu, Zhiwei Liu, Rithesh R N, Tulika Manoj Awalgaonkar, Jianguo Zhang, Weiran Yao
Abstract
Personalization is essential for AI assistants, especially in private AI settings where models are expected to interpret users’ personal data (e.g., conversations, app usage) to understand their background, preferences, and social context. However, due to privacy concerns, existing academic research lacks direct access to such data, making benchmarking difficult. To fill this gap, we propose a synthetic data pipeline that generates realistic user profiles and private documents, enabling the creation of PersonaBench—a benchmark for evaluating models’ ability to understand personal information. Using this benchmark, we assess Retrieval-Augmented Generation (RAG) pipelines on personalized questions and find that current models struggle to accurately extract and answer questions even when provided with the full set of user documents, highlighting the need for improved personalization methods.
BibTeX
@inproceedings{tan-etal-2025-personabench,
title = "{P}ersona{B}ench: Evaluating {AI} Models on Understanding Personal Information through Accessing (Synthetic) Private User Data",
author = "Tan, Juntao and
Yang, Liangwei and
Liu, Zuxin and
Liu, Zhiwei and
R N, Rithesh and
Awalgaonkar, Tulika Manoj and
Zhang, Jianguo and
Yao, Weiran and
Zhu, Ming and
Kokane, Shirley and
Savarese, Silvio and
Wang, Huan and
Xiong, Caiming and
Heinecke, Shelby",
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://aclanthology.org/2025.findings-acl.49/",
doi = "10.18653/v1/2025.findings-acl.49",
pages = "878--893",
ISBN = "979-8-89176-256-5"
}