AI生成的可视化建议比人类更全面、更专业，尤其在技术细节上表现突出。
Understanding Why ChatGPT Outperforms Humans in Visualization Design Advice
- 对比分析ChatGPT与人类在可视化建议中的语言结构和知识广度
- ChatGPT-4在覆盖范围和技术反馈上优于人类和前代模型
- 适合关注AI辅助设计、人机协作的研究者与开发者
本文系统比较了生成式AI模型与人类在数据可视化知识任务中的表现。通过分析回答可视化问题的输出，发现不同模型（ChatGPT-3.5与ChatGPT-4）与人类在修辞结构、知识广度和感知质量方面存在差异。研究显示，更先进的ChatGPT-4兼具人类与早期模型的特征，整体表现优于人类。其在内容覆盖广度、技术性反馈和任务导向性上的优势，共同提升了建议质量。研究结果为利用大模型潜力优化用户体验提供了启示，对AI在人机交互中的应用具有广泛意义。
原文摘要 · Abstract (English)
This paper investigates why recent generative AI models outperform humans in data visualization knowledge tasks. Through systematic comparative analysis of responses to visualization questions, we find that differences exist between two ChatGPT models and human outputs over rhetorical structure, knowledge breadth, and perceptual quality. Our findings reveal that ChatGPT-4, as a more advanced model, displays a hybrid of characteristics from both humans and ChatGPT-3.5. The two models were generally favored over human responses, while their strengths in coverage and breadth, and emphasis on technical and task-oriented visualization feedback collectively shaped higher overall quality. Based on our findings, we draw implications for advancing user experiences based on the potential of LLMs and human perception over their capabilities, with relevance to broader applications of AI.
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