Point of Order: Action-Aware LLM Persona Modeling for Realistic Civic Simulation

• URL: http://arxiv.org/abs/2511.17813v1
• Date: Fri, 21 Nov 2025 22:07:33 GMT
• Title: Point of Order: Action-Aware LLM Persona Modeling for Realistic Civic Simulation
• Authors: Scott Merrill, Shashank Srivastava,
• Abstract summary: This work introduces a pipeline to transform public Zoom recordings into speaker-attributed transcripts with metadata like persona profiles and pragmatic action tags.<n>We release three local government deliberation datasets: Appellate Court hearings, School Board meetings, and Municipal Council sessions.<n>Fine-tuning LLMs to model specific participants using this "action-aware" data produces a 67% reduction in perplexity.
• Score: 9.827138852806305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models offer opportunities to simulate multi-party deliberation, but realistic modeling remains limited by a lack of speaker-attributed data. Transcripts produced via automatic speech recognition (ASR) assign anonymous speaker labels (e.g., Speaker_1), preventing models from capturing consistent human behavior. This work introduces a reproducible pipeline to transform public Zoom recordings into speaker-attributed transcripts with metadata like persona profiles and pragmatic action tags (e.g., [propose_motion]). We release three local government deliberation datasets: Appellate Court hearings, School Board meetings, and Municipal Council sessions. Fine-tuning LLMs to model specific participants using this "action-aware" data produces a 67% reduction in perplexity and nearly doubles classifier-based performance metrics for speaker fidelity and realism. Turing-style human evaluations show our simulations are often indistinguishable from real deliberations, providing a practical and scalable method for complex realistic civic simulations.

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