用大模型从混乱对话中自动生成可信警报报告
Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach
- 基于大模型解析嘈杂多角色对话，提取关键执法信息
- 生成结构化报告，提升内容质量与程序透明度
- 适合警务自动化与执法透明化研究者使用
在追求公平与透明的背景下，本研究提出一种创新的AI系统，旨在从复杂、噪声大且涉及多方角色的对话数据中自动生成警报报告草稿。该方法智能提取执法互动的关键要素，并将其融入报告中，生成结构清晰、质量高的叙述文本，不仅增强问责性，还提升执法程序的透明度。该框架有望革新报告流程，推动未来警务实践中的监督力度、一致性与公正性。系统演示视频可访问：https://drive.google.com/file/d/1kBrsGGR8e3B5xPSblrchRGj-Y-kpCHNO/view?usp=sharing
原文摘要 · Abstract (English)
Achieving a delicate balance between fostering trust in law enforcement and protecting the rights of both officers and civilians continues to emerge as a pressing research and product challenge in the world today. In the pursuit of fairness and transparency, this study presents an innovative AI-driven system designed to generate police report drafts from complex, noisy, and multi-role dialogue data. Our approach intelligently extracts key elements of law enforcement interactions and includes them in the draft, producing structured narratives that are not only high in quality but also reinforce accountability and procedural clarity. This framework holds the potential to transform the reporting process, ensuring greater oversight, consistency, and fairness in future policing practices. A demonstration video of our system can be accessed at https://drive.google.com/file/d/1kBrsGGR8e3B5xPSblrchRGj-Y-kpCHNO/view?usp=sharing
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