Summary of Large Language Models For Anomaly and Out-of-distribution Detection: a Survey, by Ruiyao Xu et al.
Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey
by Ruiyao Xu, Kaize Ding
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper presents a survey on anomaly and out-of-distribution (OOD) detection using Large Language Models (LLMs). LLMs have shown promise in various applications beyond natural language processing, making them suitable for integrating into anomaly detection methods. The authors propose a new taxonomy that categorizes existing approaches into two classes based on the role played by LLMs. They discuss related work under each category and highlight potential challenges and directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly and out-of-distribution detection is crucial to ensure machine learning systems are reliable and trustworthy. Large Language Models (LLMs) have shown great success in various tasks, including anomaly detection. This paper reviews the current state of LLM-based approaches for detecting anomalies and OOD samples. It proposes a new way to group existing methods and discusses what’s been done so far. |
Keywords
» Artificial intelligence » Anomaly detection » Machine learning » Natural language processing
