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arXiv · 2604.14773

CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors

Hang Su·Zequn Liu·Chen Hu·Xuesong Lu·Yingce Xia·Zhen Liu
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Abstract

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.

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Hang Su, Zequn Liu, Chen Hu, Xuesong Lu, Yingce Xia, Zhen Liu. 2026-04-16. CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors. https://arxiv.org/abs/2604.14773

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