arXiv cs.AI· Wenshuo Zhang, Leixian Shen, Qiqi Zhou, Shuchang Xu, Feiyang Hao, Huamin Qu, Haobo Li, Zhao Chen, Lin-Ping Yuan·· 12 小时前AI 评分20
QuAD：内容感知多智能体系统实现定性数据质量的分布对齐定量评估
QuAD: Quantitative Assessment via Distribution-Alignment for Qualitative Data Quality Enabled by a Content-Aware Multi-Agent System
AI 导读
QuAD 是一个对定性回答数据集做定量预分析的框架，通过比较从回答重建的内在聚类分布与研究者基于元数据定义的外在分析视角，以四象限评估指标联合判断数据集是否适配特定分析目标。
正文 · 原文
Abstract:Assessing whether qualitative responses collected from semi-structured questions are ready for a particular analysis goal is a persistent challenge. Manual qualitative analysis is interpretive and labor-intensive, while existing automated methods often lack a dataset-level perspective for deciding whether a response set supports a researcher-selected analytical lens. We introduce QuAD, a framework for quantitative pre-analysis assessment of qualitative response datasets. QuAD compares a reconstructed intrinsic cluster distribution reconstructed from responses with an extrinsic, researcher-defined analytical lens derived from metadata. QuAD is structured into a four-quadrant assessment whose metrics are read jointly, from lens-level relations to within-cluster variation, to provide fitness-for-purpose signals for revealing general patterns and unique insights. Because this assessment relies on lens-conditioned inter-response dissimilarity, we propose a content-aware multi-agent system with two-stage instruction generation and adaptive anchors to improve scoring consistency. We validate QuAD through controlled experiments on synthetic data, including robustness and baseline comparisons, and a real-world social survey case study. The case study shows how QuAD quantitatively assesses whether a selected analysis axis is promising and also surfaces representative or unusual responses for human interpretation.
| Subjects: | Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2610.23153 [cs.AI] |
| (or arXiv:2610.23153v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.23153 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wenshuo Zhang [view email]
[v1]
Fri, 16 Oct 2026 08:46:00 UTC (8,280 KB)
来源：arXiv cs.AI · arxiv.org