PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data
This addresses the problem of assessing personalization in AI for users with private data, but it is incremental as it focuses on benchmarking rather than solving the underlying challenge.
The authors tackled the lack of public datasets for evaluating AI models on understanding personal information from private user data by introducing PersonaBench, a benchmark using synthetic data, and found that current retrieval-augmented models struggle to answer private questions, highlighting performance gaps.
Personalization is critical in AI assistants, particularly in the context of private AI models that work with individual users. A key scenario in this domain involves enabling AI models to access and interpret a user's private data (e.g., conversation history, user-AI interactions, app usage) to understand personal details such as biographical information, preferences, and social connections. However, due to the sensitive nature of such data, there are no publicly available datasets that allow us to assess an AI model's ability to understand users through direct access to personal information. To address this gap, we introduce a synthetic data generation pipeline that creates diverse, realistic user profiles and private documents simulating human activities. Leveraging this synthetic data, we present PersonaBench, a benchmark designed to evaluate AI models' performance in understanding personal information derived from simulated private user data. We evaluate Retrieval-Augmented Generation (RAG) pipelines using questions directly related to a user's personal information, supported by the relevant private documents provided to the models. Our results reveal that current retrieval-augmented AI models struggle to answer private questions by extracting personal information from user documents, highlighting the need for improved methodologies to enhance personalization capabilities in AI.