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

arXiv: https://arxiv.org/abs/2409.15706 · HTML: https://mlsanity.com/paper/2409.15706/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 86 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [Large Language Models Understand and Can be Enhanced by Emotional Stimuli](https://mlsanity.com/paper/2307.11760/) (2023)
- [AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation](https://mlsanity.com/paper/2202.13047/) (2022)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [Building Emotional Support Chatbots in the Era of LLMs](https://mlsanity.com/paper/2308.11584/) (2023)
- [Emotional Intelligence of Large Language Models](https://mlsanity.com/paper/2307.09042/) (2023)
- [Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench](https://mlsanity.com/paper/2308.03656/) (2023)
- [Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems](https://mlsanity.com/paper/1906.09308/) (2019)
- [From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap](https://mlsanity.com/paper/2004.05827/) (2020)
- [Multi-Grained Named Entity Recognition](https://mlsanity.com/paper/1906.08449/) (2019)
- [Towards an Online Empathetic Chatbot with Emotion Causes](https://mlsanity.com/paper/2105.11903/) (2021)
- [Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media](https://mlsanity.com/paper/1803.02952/) (2018)
- [CommunityClick: Capturing and Reporting Community Feedback from Town Halls to Improve Inclusivity](https://mlsanity.com/paper/2009.09053/) (2020)
- [Discovering the Hidden Facts of User-Dispatcher Interactions via Text-based Reporting Systems for Community Safety](https://mlsanity.com/paper/2211.04618/) (2022)
- [An Empathetic User-Centric Chatbot for Emotional Support](https://mlsanity.com/paper/2311.09271/) (2023)

## Abstract (arXiv)

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.