mlsanityData to 14 Oct 2026

Persona Prompting in Multimodal Urban Perception: Descriptive Convergence and Interpretive Variation

arXiv 2605.29064 ↗First posted 27 May 2026cs.CL cs.CV cs.HCcited by 0

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 9 of its 22 references that are in the map. First posted on arXiv 27 May 2026.

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9 of 22 references.

References · 9

  1. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 752
  2. 2022Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studiescited by 515
  3. 2024Quantifying the Persona Effect in LLM Simulationscited by 169
  4. 2024Large language models that replace human participants can harmfully misportray and flatten identity groupscited by 144
  5. 2023Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Promptingcited by 81
  6. 2024FLAME: Learning to Navigate with Multimodal LLM in Urban Environmentscited by 19
  7. 2025Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathycited by 10
  8. 2025UrbanFeel: A Comprehensive Benchmark for Temporal and Perceptual Understanding of City Scenes through Human Perspectivecited by 7
  9. 2026Stable Behavior, Limited Variation: Persona Validity in LLM Agents for Urban Sentiment Perceptioncited by 2

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  1. 2025Multimodal Large Language Models Predict Urban Safety Perception but Encode Non-Neutral Demographic Priors
  2. 2025A Thousand Words or An Image: Studying the Influence of Persona Modality in Multimodal LLMs
  3. 2026Context-Dependent Affordance Reports in Vision-Language Models
  4. 2025Do Vision-Language Models See Urban Scenes as People Do? An Urban Perception Benchmark
  5. 2026Where is the Mind? Persona Vectors and LLM Individuation
  6. 2026When Persona Attributes Improve Population Alignment in Large Language Models
  7. 2025Dissecting Persona-Driven Reasoning in Language Models via Activation Patching
  8. 2026Persona Prompting as a Lens on LLM Social Reasoning