Large Language Models for Spatial Trajectory Patterns Mining
This addresses the problem of analyzing human mobility patterns for applications like disease monitoring and elderly care, but is incremental as it evaluates existing LLMs on a new domain.
The paper assessed large language models (LLMs) like GPT-4 and Claude-2 for detecting anomalous human spatial trajectory patterns from mobility data, finding they achieve reasonable anomaly detection performance without specific cues and improve with contextual clues while providing explanatory transparency.
Identifying anomalous human spatial trajectory patterns can indicate dynamic changes in mobility behavior with applications in domains like infectious disease monitoring and elderly care. Recent advancements in large language models (LLMs) have demonstrated their ability to reason in a manner akin to humans. This presents significant potential for analyzing temporal patterns in human mobility. In this paper, we conduct empirical studies to assess the capabilities of leading LLMs like GPT-4 and Claude-2 in detecting anomalous behaviors from mobility data, by comparing to specialized methods. Our key findings demonstrate that LLMs can attain reasonable anomaly detection performance even without any specific cues. In addition, providing contextual clues about potential irregularities could further enhances their prediction efficacy. Moreover, LLMs can provide reasonable explanations for their judgments, thereby improving transparency. Our work provides insights on the strengths and limitations of LLMs for human spatial trajectory analysis.