你正在被追踪吗？发掘大语言模型零样本轨迹追踪的强大能力！
Are You Being Tracked? Discover the Power of Zero-Shot Trajectory Tracing with LLMs!
AI总结：
本研究提出LLMTrack模型，通过结合角色扮演与逐步思考的单提示词技术，实现大语言模型对原始IMU数据的零样本轨迹识别，在室内外场景中性能超越传统及前沿深度学习方法，为AIoT轨迹解析提供新方案。
摘要：
当前，围绕大语言模型（LLMs）可作为核心组件无缝融入人工智能物联网（AIoT）以解析复杂轨迹的能力，相关讨论正日益增多。本研究提出LLMTrack模型，展示了如何借助一种新颖的单提示词技术实现LLMs的零样本轨迹识别：该技术结合角色扮演与逐步思考方法，直接处理原始惯性测量单元（IMU）数据。我们采用真实世界数据集对模型进行评估，这些数据集包含室内、室外场景下的差异化轨迹，旨在为模型带来挑战。在两类测试场景中，LLMTrack的性能不仅达到、甚至超越了传统机器学习方法及当前最先进深度学习模型设定的性能基准，且全程无需在专用数据集上进行训练。研究结果表明，通过策略性设计的提示词，LLMs能够调用其庞大的知识库，足以极为高效地分析原始传感器数据。
英文摘要：
There is a burgeoning discussion around the capabilities of Large Language Models (LLMs) in acting as fundamental components that can be seamlessly incorporated into Artificial Intelligence of Things (AIoT) to interpret complex trajectories. This study introduces LLMTrack, a model that illustrates how LLMs can be leveraged for Zero-Shot Trajectory Recognition by employing a novel single-prompt technique that combines role-play and think step-by-step methodologies with unprocessed Inertial Measurement Unit (IMU) data. We evaluate the model using real-world datasets designed to challenge it with distinct trajectories characterized by indoor and outdoor scenarios. In both test scenarios, LLMTrack not only meets but exceeds the performance benchmarks set by traditional machine learning approaches and even contemporary state-of-the-art deep learning models, all without the requirement of training on specialized datasets. The results of our research suggest that, with strategically designed prompts, LLMs can tap into their extensive knowledge base and are well-equipped to analyze raw sensor data with remarkable effectiveness.
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。
