用AI模型提升文字报警中情绪支持的一致性与质量。
Improving Emotional Support Delivery in Text-Based Community Safety Reporting Using Large Language Models
- 用2年8000多起事件数据训练专用LLM，模拟人工调度员语气与共情表达。
- 实测显示该模型生成回复在情感支持上优于普通模型，接近人工水平。
- 适合关注公共服务智能化、人机共情交互的研究者与政策制定者。
情绪支持是社区成员与警察调度员在事件报告过程中沟通的重要组成部分。然而，对于文本系统中情绪支持的传递方式，尤其是在非紧急情境下的理解仍十分有限。本研究分析了来自130所高等教育机构的两年聊天记录，涵盖8,239起事件中的57,114条消息。实证发现，调度员提供的情绪支持存在显著差异，受事件类型、服务时长影响，并在多个机构中随时间呈现明显下降趋势。为提升情绪支持的一致性与质量，我们开发并部署了一个名为dispatcherLLM的微调大型语言模型。通过将该模型生成的回复与人工调度员及其他现成模型进行对比，并使用真实聊天记录进行评估，同时开展人工评价以衡量其感知有效性。本研究不仅提供了文本调度系统中情绪支持的新实证认知，也展示了生成式AI在改善服务交付方面的巨大潜力。
原文摘要 · Abstract (English)
Emotional support is a crucial aspect of communication between community members and police dispatchers during incident reporting. However, there is a lack of understanding about how emotional support is delivered through text-based systems, especially in various non-emergency contexts. In this study, we analyzed two years of chat logs comprising 57,114 messages across 8,239 incidents from 130 higher education institutions. Our empirical findings revealed significant variations in emotional support provided by dispatchers, influenced by the type of incident, service time, and a noticeable decline in support over time across multiple organizations. To improve the consistency and quality of emotional support, we developed and implemented a fine-tuned Large Language Model (LLM), named dispatcherLLM. We evaluated dispatcherLLM by comparing its generated responses to those of human dispatchers and other off-the-shelf models using real chat messages. Additionally, we conducted a human evaluation to assess the perceived effectiveness of the support provided by dispatcherLLM. This study not only contributes new empirical understandings of emotional support in text-based dispatch systems but also demonstrates the significant potential of generative AI in improving service delivery.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。