Summary of Anomaly Detection Of Tabular Data Using Llms, by Aodong Li et al.
Anomaly Detection of Tabular Data Using LLMs
by Aodong Li, Yunhan Zhao, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models have shown promise in understanding long contexts and mathematical reasoning. This study explores using these models as zero-shot batch-level anomaly detectors, showing they can identify hidden outliers in a data batch without additional training. Pre-trained LLMs can discover low-density data regions, demonstrating their potential in detecting anomalies. To improve LLM performance on this task, we propose simple yet effective data-generating processes and an end-to-end fine-tuning strategy. Our experiments on the ODDS benchmark show that GPT-4 has comparable performance to state-of-the-art transductive methods and that our synthetic dataset and fine-tuning strategy can effectively align LLMs for anomaly detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand a lot of information. This study shows how these models can find unusual data points, called anomalies, without needing extra training. The researchers used these models to look at big groups of data and found some unusual things. They also came up with ways to make the models better at finding anomalies. This is important because it can help us find problems or mistakes in big datasets. |
Keywords
* Artificial intelligence * Anomaly detection * Fine tuning * Gpt * Zero shot
