AI RESEARCH
CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors
arXiv CS.CL
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ArXi:2604.14773v1 Announce Type: new While LLMs have nstrated 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.