让大模型与专用模型协作，提升时间序列异常检测效果
Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection
- 构建双模型协同框架，利用大模型知识与小模型模式识别能力
- 提出对齐模块和联合损失函数，解决表达错位与误差累积问题
- 在多个数据集上优于单一模型，适合工业场景异常检测
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，而特定任务的小模型则擅长从目标应用的训练数据中提取正常模式并检测数值波动。受人类神经系统启发——大脑存储专家知识，外周神经与脊髓处理具体反应如缩手反射——我们提出CoLLaTe框架，促进大模型与特定任务模型的协作，充分发挥两者优势进行异常检测。具体地，我们首先建模协作过程，并识别两大挑战：(1) 大模型与小模型表达域不一致；(2) 两模型预测导致的误差累积。为应对这些挑战，我们引入两个核心组件：模型对齐模块与协同损失函数。通过理论分析与实验验证，证明该设计有效缓解上述问题，性能优于单独使用大模型或小模型。
原文摘要 · Abstract (English)
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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