Point of Order: Action-Aware LLM Persona Modeling for Data-Grounded Civic Deliberation
Summary
A reproducible pipeline converts public Zoom recordings into speaker-attributed transcripts enriched with personas, topics, and action tags, then fine-tunes LLM personas on this data. Action-aware fine-tuning significantly improves persona fidelity, consistency, and deliberative responsiveness, enabling realistic civic deliberation simulations.
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# Point of Order: Action-Aware LLM Persona Modeling for Data-Grounded Civic Deliberation Source: [https://arxiv.org/abs/2511.17813](https://arxiv.org/abs/2511.17813) [View PDF](https://arxiv.org/pdf/2511.17813) > Abstract:LLM\-based simulations can enable controlled studies of civic deliberation, but current systems lack speaker\-attributed data and methods for evaluating long\-form institutional behavior\. ASR transcripts typically use anonymous labels such as $Speaker\\\_1$, preventing models from learning stable participant behavior across meetings\. We present a reproducible pipeline that converts public Zoom recordings into speaker\-attributed transcripts enriched with persona profiles, topics, and pragmatic "action tags" such as $\[propose\\\_motion\]$\. Using this pipeline, we release three public datasets of government deliberation \(Appellate Court hearings, School Board meetings, and Municipal Council sessions\) and fine\-tune LLM personas on this action\-aware data\. We evaluate simulations along four dimensions: persona fidelity, persona consistency, institutional fidelity, and behavioral coherence\. Action\-aware fine\-tuning cuts perplexity by 67%, doubles classifier\-based persona fidelity, increases vote attempts by up to $3\.6\\times$, and improves deliberative responsiveness by up to 70%\. Human evaluations show that simulated excerpts are often hard to distinguish from real deliberations, indicating a practical foundation for data\-grounded civic simulation studies\. ## Submission history From: Scott Merrill \[[view email](https://arxiv.org/show-email/daa16231/2511.17813)\] **[\[v1\]](https://arxiv.org/abs/2511.17813v1)**Fri, 21 Nov 2025 22:07:33 UTC \(1,726 KB\) **[\[v2\]](https://arxiv.org/abs/2511.17813v2)**Sat, 14 Mar 2026 18:19:24 UTC \(1,615 KB\) **\[v3\]**Fri, 10 Jul 2026 02:38:19 UTC \(5,117 KB\)
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