分析多模态大语言模型代理在城市感知中生成解释的角色效应
Persona Prompting in Multimodal Urban Perception: Descriptive Convergence and Interpretive Variation
AI总结：
通过对比不同角色提示和无角色设置下多模态大语言模型生成的文本，发现标题描述趋同，但理由描述随社会经济和政治属性系统变化，感知标签无显著差异。
摘要：
我们研究了角色提示如何塑造多模态大语言模型在城市感知环境中生成的语言。使用来自1,200个角色条件代理和两个无角色设置的59,808个注释，我们分析了不同角色下的标题、理由和感知标签。结果表明，不同角色的标题高度趋同，而理由描述显示出与社会经济和政治属性相关的系统变化，感知标签则没有统计上显著的角色相关差异，尽管观察到了效应趋势。主题分析进一步揭示，角色在解释相同场景时强调不同的评价主题。
英文摘要：
This study examines how persona prompting shapes language generated by two multimodal large language models in urban perception, a setting for examining subjective interpretations of shared visual evidence. We organize outputs into three functional layers: descriptive grounding (captions), intermediate semantic layer (perception tags), and interpretive framing (justifications). Using approximately 60,000 persona-conditioned annotations from each of two MLLMs, Qwen3-VL and Gemma4, we find that captions converge strongly across persona profiles and show only small attribute-associated differences. Justifications vary substantially more: economic status produces the largest difference in both models, with political orientation and personality also prominent. Paired image-level comparisons confirm larger justification than caption differences for these three attributes. For perception tags, personas sharing the same attribute level produce more similar tag sets than personas with different attribute levels, with the largest separation observed for economic status. Exploratory topic analysis further suggests persona-specific evaluative emphasis. Across models, profile-pair similarity patterns are strongly correlated for all three output types, although agreement is lowest for justifications. Overall, persona prompting affects interpretive framing more strongly than descriptive grounding.
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。
