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

Abstract: In anomaly detection, methods based on 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.

• The paper formalizes a collaborative process that pairs LLMs with task-specific models to address data sparsity and score misalignment in time series anomaly detection.
• It introduces an alignment module and a custom collaborative loss function to harmonize anomaly scores and mitigate error accumulation during predictions.
• Experimental results demonstrate that CoLLaTe consistently outperforms standalone models across varied domains, ensuring robust real-time monitoring.

Integration Framework of LLMs and Task-Specific Models for Anomaly Detection

The paper "Facilitate Collaboration between LLM and Task-specific Model for Time Series Anomaly Detection" presents a novel collaborative framework, CoLLaTe, aimed at enhancing anomaly detection in time series data by leveraging the complementary strengths of LLMs and task-specific models. This research addresses the challenges imposed by data sparsity in anomaly detection tasks, particularly when faced with limited data capturing diverse normal patterns, which often undercuts the performance of traditional task-specific models.

The proposed CoLLaTe framework seeks to integrate the generalization capability and expertise incorporation of LLMs with the sensitivity to pattern fluctuations inherent in task-specific models. Central to this framework are two key innovations: an alignment module to bridge the expression discrepancies between LLMs and smaller models, and a collaborative loss function to mitigate error accumulation across models.

Key Contributions and Methodology

1. Collaboration Process Formalization: The paper first formalizes the collaboration process between LLMs and task-specific models, identifying critical challenges such as expression misalignment and error accumulation. These challenges denote the divergent ways in which these models interpret anomaly scores and the compound effect of errors during predictions.
2. Alignment Module: To harmonize these interpretation discrepancies, the paper introduces an alignment module that adjusts the distribution of anomaly scores from both models, ensuring congruent representation of anomaly severity. By aligning the score distributions, CoLLaTe aims to offset divergent interpretations, thus enabling cohesive collaboration.
3. Collaborative Loss Function: A bespoke loss function is designed to prevent error accumulation visible with classical loss functions like Mean Squared Error (MSE). The introduced loss function emphasizes maintaining the rank ordering of anomaly severity between time slots and prevents the accentuation of error through cumulative computations.
4. Experimental Validation: Extensive experiments validate the efficacy of CoLLaTe on datasets across different domains, including cloud service and aircraft monitoring. CoLLaTe consistently outperforms existing LLM-based and task-specific models alone, underscoring the advantages of a hybrid collaborative approach.

Implications and Future Directions

The CoLLaTe framework proposes a promising direction for anomaly detection by integrating diverse model architectures, offering a flexible approach adaptable across varied domains and datasets with diverse availability of training samples. The demonstrated reduction in performance degradation due to sparse monitoring data indicates a robust application potential in real-time monitoring and safety-critical fields.

This research creates a foundational basis for further exploration into hybrid model architectures for anomaly detection and other predictive tasks. Future developments could focus on improving the alignment of semantic features in other complex data types or enhancing real-time processing efficiency. Moreover, exploring the potential for adaptive learning where the models self-adjust based on evolving data patterns could further solidify its application in dynamic environments.

In conclusion, the CoLLaTe framework advances the integration capabilities of LLMs and task-specific models, providing a viable solution to leverage the strengths of both models and overcome individual limitations. Its contribution to anomaly detection is significant, suggesting new opportunities for cross-disciplinary applications within the field of AI and machine learning.