使用大型语言模型在灾难期间监控关键基础设施设施
Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models
AI总结：
本研究探索使用大型语言模型在零样本设置下通过社交媒体数据监控受自然灾害影响的关键基础设施设施的状态，发现LLMs在分类任务中表现良好但在复杂推理任务中存在挑战，并为未来灾害响应应用提供了方向。
摘要：
关键基础设施设施（CIFs），如医疗保健和交通设施，对于社区的功能至关重要，尤其是在大规模紧急情况期间。在本文中，我们探索了大型语言模型（LLMs）的一个潜在应用，即通过社交媒体网络中传播的信息来监控受自然灾害影响的CIFs的状态。为此，我们分析了来自两个不同国家两次灾害事件的社交媒体数据，以识别CIFs受到的报告影响及其影响严重程度和运营状态。我们采用最先进的开源LLMs执行包括检索、分类和推理在内的计算任务，所有这些任务均在零样本设置下进行。通过广泛的实验，我们使用标准评估指标报告了这些任务的结果，并揭示了LLMs的优势和劣势。我们注意到，尽管LLMs在分类任务中表现良好，但它们在推理任务中遇到了挑战，尤其是当上下文/提示复杂且冗长时。此外，我们概述了未来探索的各种潜在方向，这些方向在LLMs用于灾害响应任务的初始采用阶段可能是有益的。
英文摘要：
Critical Infrastructure Facilities (CIFs), such as healthcare and transportation facilities, are vital for the functioning of a community, especially during large-scale emergencies. In this paper, we explore a potential application of Large Language Models (LLMs) to monitor the status of CIFs affected by natural disasters through information disseminated in social media networks. To this end, we analyze social media data from two disaster events in two different countries to identify reported impacts to CIFs as well as their impact severity and operational status. We employ state-of-the-art open-source LLMs to perform computational tasks including retrieval, classification, and inference, all in a zero-shot setting. Through extensive experimentation, we report the results of these tasks using standard evaluation metrics and reveal insights into the strengths and weaknesses of LLMs. We note that although LLMs perform well in classification tasks, they encounter challenges with inference tasks, especially when the context/prompt is complex and lengthy. Additionally, we outline various potential directions for future exploration that can be beneficial during the initial adoption phase of LLMs for disaster response tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
