Facilitate Collaboration between Large Language Model and Task-specific Model for Time Series Anomaly Detection

However, they are often insensitive to the value fluctuations in time series data, and their In anomaly detection, methods based on large NLP-based representations do not align well with the characteristics language models (LLMs) can incorporate expert of time series data (Jin et al., 2024). In contrast, knowledge, while task-specific smaller models task-specific methods, such as anomaly detection models, excel at extracting normal patterns and detecting typically lack the broad generalization capabilities of LLMs value fluctuations. Inspired by the human across multiple tasks. However, these models are specifically nervous system--where the brain stores expert designed for particular tasks and often exhibit superior knowledge and the peripheral nervous system and performance when applied to well-matched anomaly detection spinal cord handle specific tasks like withdrawal datasets (Zhou et al., 2023). Despite their strengths, and knee-jerk reflexes--we propose CoLLaTe, task-specific models also have notable limitations. First, for a framework designed to facilitate collaboration different application scenarios, researchers need to adapt between LLMs and task-specific models, leveraging anomaly detection models to incorporate domain-specific the strengths of both. In this work, we expertise to achieve optimal performance. For instance, first formulate the collaboration process and identify anomaly detection methods tailored for cloud service monitoring two key challenges in the collaboration between (Ma et al., 2021; Chen et al., 2024b) or aircraft LLMs and task-specific models: (1) the monitoring (e Silva & Murcca, 2023) have been modified misalignment between the expression domains of to suit these specific contexts.