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

Source: [CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors](<https://www.sota2.com/research/paper/69e1bebc7d0172140ec7d8ff>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-18T05:00:04.772Z

Authors: Hang Su, Zequn Liu, Chen Hu, Xuesong Lu, Yingce Xia, Zhen Liu

Published: 2026-04-16T08:35:28.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.14773>)

[PDF](<https://arxiv.org/pdf/2604.14773.pdf>)