CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors
For researchers and developers of personalized QA systems, this benchmark offers a more comprehensive evaluation method grounded in cognitive factors, addressing a critical bottleneck in personalization assessment.
The paper addresses the lack of data-driven personalization evaluation in LLM-based QA by mining Community-Individual Preference Divergence to define six cognitive factors, and introduces CoPA, a benchmark with 1,985 user profiles for fine-grained factor-level assessment, providing a more discriminative standard than generic metrics.
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.