LLMs and task-specific models team up to catch anomalies better
CoLLaTe, proposed in this paper, framework enables LLMs and task-specific models to work together for better time series anomaly detection by combining their unique strengths.
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https://arxiv.org/abs/2501.05675
Original Problem 🤔:
→ LLMs excel at incorporating expert knowledge but struggle with value fluctuations in time series data
→ Task-specific models are great at pattern detection but can't easily adapt to new domains without modifications
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Solution in this Paper 🛠️:
→ CoLLaTe framework aligns different score interpretations between LLMs and task-specific models using a half-Gaussian distribution
→ Uses set-up-pitch prompting to improve LLM performance by incorporating domain expertise
→ Implements collaborative loss function to prevent error accumulation between models
→ Employs conditional network to combine judgments using data representation as condition
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Key Insights 💡:
→ LLMs and task-specific models have complementary strengths in anomaly detection
→ Alignment between different model interpretations is crucial for effective collaboration
→ Error accumulation can be mitigated through careful loss function design
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Results 📊:
→ Achieved highest F1 scores across 4 datasets compared to state-of-the-art methods
→ Demonstrated effective collaboration between LLMs and task-specific models
→ Successfully validated theoretical properties through experiments
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