Large Language Models (LLMs) have demonstrated extraordinary performance across a broad array of applications, from traditional language processing tasks to interpreting structured sequences like time-series data. Yet, their effectiveness in fast-paced, online decision-making environments requiring swift, accurate, and concurrent responses poses a significant challenge. This paper introduces TStreamLLM, a revolutionary framework integrating Transactional Stream Processing (TSP) with LLM management to achieve remarkable scalability and low latency. By harnessing the scalability, consistency, and fault tolerance inherent in TSP, TStreamLLM aims to manage continuous & concurrent LLM updates and usages efficiently. We showcase its potential through practical use cases like real-time patient monitoring and intelligent traffic management. The exploration of synergies between TSP and LLM management can stimulate groundbreaking developments in AI and database research. This paper provides a comprehensive overview of challenges and opportunities in this emerging field, setting forth a roadmap for future exploration and development.

翻译：大型语言模型在从传统语言处理到解释时间序列数据等结构化序列的广泛应用中展现出卓越性能。然而，在需要快速、准确且并发响应的快节奏在线决策环境中，其有效性面临重大挑战。本文提出TStreamLLM这一革命性框架，将事务流处理与大型语言模型管理相结合，以实现显著的扩展性和低延迟。通过利用事务流处理固有的可扩展性、一致性和容错性，TStreamLLM旨在高效管理持续并发的大型语言模型更新与使用。我们通过实时患者监测和智能交通管理等实际用例展示其潜力。探索事务流处理与大型语言模型管理之间的协同效应可推动人工智能和数据库研究的突破性发展。本文全面概述了这一新兴领域的挑战与机遇，并制定了未来探索与发展的路线图。