理解为何ChatGPT在可视化设计建议上优于人类
Understanding Why ChatGPT Outperforms Humans in Visualization Design Advice
AI总结：
本文通过系统比较ChatGPT与人类在数据可视化知识任务中的回答，发现ChatGPT-4兼具人类与ChatGPT-3.5特征，其覆盖广度与技术导向反馈使其回答更受青睐，并据此提出利用LLM与人类感知推进用户体验的启示。
摘要：
本文探讨了为何近期生成的AI模型在数据可视化知识任务中表现优于人类。通过对可视化问题的回答进行系统性比较分析，我们发现两个ChatGPT模型与人类输出在修辞结构、知识广度和感知质量方面存在差异。研究结果显示，作为更先进的模型，ChatGPT-4展现出人类与ChatGPT-3.5特征的混合体。这两个模型普遍比人类回答更受青睐，而它们在覆盖范围和广度上的优势，以及对技术和任务导向的可视化反馈的强调，共同塑造了更高的整体质量。基于我们的发现，我们提出了利用LLMs和人类感知能力的潜力来推进用户体验的启示，这对AI的更广泛应用具有相关性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
