Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception
For researchers studying bias in LLM outputs, this paper provides an incremental analysis of persona effects in a specific domain (urban perception).
The study investigates how persona prompting affects language generated by multimodal LLMs in urban perception, finding that captions converge across personas, justifications vary with socioeconomic/political attributes, and perception tags show no significant differences.
We study how persona prompting shapes language generated by multimodal large language models in an urban perception setting. Using 59,808 annotations from 1,200 persona-conditioned agents and two no-persona settings, we analyze captions, justifications, and perception tags across personas. Results indicate strong convergence in captions for different personas, whereas justifications display systematic variation associated with socioeconomic and political attributes, while perception tags show no statistically significant persona-related differences, though effect trends are observed. Topic analysis further reveals that personas emphasize different evaluative themes when interpreting the same scenes.