Summary of Ad-llm: Benchmarking Large Language Models For Anomaly Detection, by Tiankai Yang et al.
AD-LLM: Benchmarking Large Language Models for Anomaly Detection
by Tiankai Yang, Yi Nian, Shawn Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan Rossi, Kaize Ding, Xia Hu, Yue Zhao
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed benchmark, AD-LLM, evaluates the potential of large language models (LLMs) in natural language processing (NLP) anomaly detection tasks. The authors examine three key tasks: zero-shot detection using pre-trained knowledge, data augmentation to improve anomaly detection models, and model selection suggesting unsupervised models. Experiments with different datasets show that LLMs can perform well in zero-shot AD, carefully designed augmentation methods are useful, and explaining model selection remains challenging. The study outlines six future research directions on LLMs for AD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly detection is a crucial machine learning task used to spot issues like spam, misinformation, and unusual user activity. Large language models have improved text generation and summarization tasks, but their potential in anomaly detection hasn’t been explored. This study creates the first benchmark, AD-LLM, to evaluate how LLMs can help with NLP anomaly detection. The researchers test three approaches: using pre-trained knowledge without additional training, generating synthetic data to improve models, and suggesting unsupervised models. They find that LLMs work well in some cases and highlight areas for future research. |
Keywords
» Artificial intelligence » Anomaly detection » Data augmentation » Machine learning » Natural language processing » Nlp » Summarization » Synthetic data » Text generation » Unsupervised » Zero shot
