arXiv 2501.056752026-03-30cs.AIcs.LG

融合大语言模型与任务特定模型用于时间序列异常检测

Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection

Zhejiang University（浙江大学）；Department of Computer Science and Technology, Singapore Management University（新加坡管理大学计算机科学与技术系）；Department of Computer Science and Technology, National University of Singapore（新加坡国立大学计算机科学与技术系）

Feiyi Chen, Leilei Zhang, Guansong Pang, Roger Zimmermann, Shuiguang Deng

备注：: This work has been submitted to the IEEE for possible publication

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AI总结：

本文提出CoLLaTe框架，通过融合大语言模型与任务特定模型，解决异常检测中的表达域不匹配和预测误差累积问题，提升检测性能。

摘要：

在异常检测中，基于大语言模型（LLMs）的方法可通过阅读专业文档融入专家知识，而任务特定的小模型擅长提取正常数据模式并检测训练数据中的值波动。受人类神经系统启发，我们提出CoLLaTe框架，旨在促进LLMs与任务特定模型之间的协作，利用两者的优势进行异常检测。具体而言，我们首先制定了协作过程并识别出两个关键挑战：（1）LLMs与任务特定小模型的表达域不匹配，（2）来自两个模型的预测误差累积。为解决这些问题，我们引入了两个关键组件：模型对齐模块和协作损失函数。通过理论分析和实验验证，我们证明这些组件有效缓解了识别出的挑战，并在性能上优于基于LLMs和任务特定模型的方案。

英文摘要：

In anomaly detection, methods based on large language models (LLMs) can incorporate expert knowledge by reading professional document, while task-specific small models excel at extracting normal data patterns and detecting value fluctuations from training data of target applications. Inspired by the human nervous system, where the brain stores expert knowledge and the peripheral nervous system and spinal cord handle specific tasks like withdrawal and knee-jerk reflexes, we propose CoLLaTe, a framework designed to facilitate collaboration between LLMs and task-specific models, leveraging the strengths of both models for anomaly detection. In particular, we first formulate the collaboration process and identify two key challenges in the collaboration: (1) the misalignment between the expression domains of the LLMs and task-specific small models, and (2) error accumulation arising from the predictions of both models. To address these challenges, we then introduce two key components in CoLLaTe: a model alignment module and a collaborative loss function. Through theoretical analysis and experimental validation, we demonstrate that these components effectively mitigate the identified challenges and achieve better performance than both LLM-based and task-specific models.

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