arXiv 2402.103502024-02-15cs.LGcs.AI

用于预测和异常检测的大型语言模型：系统性文献综述

Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review

AT&T Center for Virtualization（AT&T虚拟化中心）；Southern Methodist University（南卫理公会大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Ohio State University（俄亥俄州立大学）；Universidad Internacional Isabel I de Castilla（卡斯蒂利亚伊莎贝尔一世国际大学）；University of Florida（佛罗里达大学）；Peking University（北京大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Southern California（南加州大学）

Jing Su, Chufeng Jiang, Xin Jin, Yuxin Qiao, Tingsong Xiao, Hongda Ma, Rong Wei, Zhi Jing, Jiajun Xu, Junhong Lin

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AI总结：

本综述系统考察了大型语言模型在预测和异常检测中的应用，指出了数据依赖、泛化性、幻觉等关键挑战，并提出多模态数据整合与提升可解释性等策略，强调持续创新与跨学科合作的重要性。

摘要：

本系统性文献综述全面考察了大型语言模型（LLMs）在预测和异常检测中的应用，突出了当前的研究现状、固有的挑战以及未来的发展方向。LLMs在解析和分析大规模数据集以识别模式、预测未来事件以及检测跨多个领域的异常行为方面，展现出了巨大的潜力。然而，本综述指出了阻碍其更广泛采用和有效性的几个关键挑战，包括对大量历史数据集的依赖、跨不同上下文的泛化能力问题、模型幻觉现象、模型知识边界内的局限性，以及所需的大量计算资源。通过详细分析，本综述探讨了克服这些障碍的潜在解决方案和策略，例如整合多模态数据、学习方法的进步，以及强调模型可解释性和计算效率。此外，本综述概述了可能塑造LLMs在这些领域演变的关键趋势，包括向实时处理的推进、可持续建模实践的重要性，以及跨学科合作的价值。结论部分强调了LLMs对预测和异常检测可能产生的变革性影响，同时强调了持续创新、伦理考量和实际解决方案的必要性，以充分发挥其潜力。

英文摘要：

This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospective future directions. LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains. However, this review identifies several critical challenges that impede their broader adoption and effectiveness, including the reliance on vast historical datasets, issues with generalizability across different contexts, the phenomenon of model hallucinations, limitations within the models' knowledge boundaries, and the substantial computational resources required. Through detailed analysis, this review discusses potential solutions and strategies to overcome these obstacles, such as integrating multimodal data, advancements in learning methodologies, and emphasizing model explainability and computational efficiency. Moreover, this review outlines critical trends that are likely to shape the evolution of LLMs in these fields, including the push toward real-time processing, the importance of sustainable modeling practices, and the value of interdisciplinary collaboration. Conclusively, this review underscores the transformative impact LLMs could have on forecasting and anomaly detection while emphasizing the need for continuous innovation, ethical considerations, and practical solutions to realize their full potential.

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。