Introduction

I’ve worked on anomaly detection problems for a while now, and one obstacle I consistently face is extreme imbalance in the data. When only a fraction of a percent of your records are "anomalies," most standard methods — like oversampling or undersampling — just don’t cut it. In my experience, these approaches either lead to overfitting (repeating the same rare examples too often) or throw away valuable data. That’s where a newer technique I’ve been exploring comes in: using Large Language Model (LLM) embeddings to spot subtle irregularities.

The Problem with Imbalanced Data

Even though embeddings are typically associated with text (thanks to tools like BERT, GPT, or other transformer models), I’ve found that the same idea — representing data in a dense, meaningful vector space — works wonders for detecting outliers across various data types. Let me walk you through the thinking and process behind this method.

The Solution: LLM Embeddings for Anomaly Detection

Those experienced in anomaly or rare event detection are keenly aware of the data’s extreme imbalance. In many scenarios:

Less than 1% of the dataset represents rare or critical events, forming the minority class.
99% or more of the dataset falls under the normal or unknown category — the majority class.

Conclusion

In conclusion, using LLM embeddings to detect anomalies in imbalanced data is a promising approach. By representing data in a dense, meaningful vector space, we can identify subtle irregularities that standard methods may miss. This technique has the potential to revolutionize the way we approach anomaly detection in a wide range of domains.

FAQs

Q: Can LLM embeddings be used for text data only?
A: No, LLM embeddings can be applied to various data types, not just text.
Q: Is oversampling or undersampling effective for imbalanced data?
A: No, these methods can lead to overfitting or data loss.
Q: How does LLM embeddings differ from other anomaly detection methods?
A: LLM embeddings represent data in a dense, meaningful vector space, making it more effective for detecting subtle irregularities.