Work

Improving Emotional Support Delivery in Text-Based Community Safety Reporting Using Large Language Models

Year: 2025

Type: article

Abstract: 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, designed to suggest replies through simulating human dispatchers' languages with appropriate emotional support. 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.

Source: Proceedings of the ACM on Human-Computer Interaction

DOI: 10.1145/3711012

Authors: Yiren Liu, Yerong Li, Ryan Mayfield, Yun Huang

Institution: University of Illinois Urbana-Champaign

Language: English

Topic: AI in Service Interactions

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, Universitas Brawijaya, Office of Advanced Cyberinfrastructure