While LLMs have demonstrated remarkable potential in Question Answering (QA), evaluating personalization remains a critical bottleneck. Existing paradigms predominantly rely on lexical-level similarity or manual heuristics, often lacking sufficient data-driven validation. We address this by mining Community-Individual Preference Divergence (CIPD), where individual choices override consensus, to distill six key personalization factors as evaluative dimensions. Accordingly, we introduce CoPA, a benchmark with 1,985 user profiles for fine-grained, factor-level assessment. By quantifying the alignment between model outputs and user-specific cognitive preferences inferred from interaction patterns, CoPA provides a more comprehensive and discriminative standard for evaluating personalized QA than generic metrics. The code is available at https://github.com/bjzgcai/CoPA.
@article{arxiv.2604.14773,
title = {CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors},
author = {Hang Su and Zequn Liu and Chen Hu and Xuesong Lu and Yingce Xia and Zhen Liu},
journal= {arXiv preprint arXiv:2604.14773},
year = {2026}
}