AD-LLM: Benchmarking Large Language Models for Anomaly Detection
This work addresses the understudied problem of applying LLMs to anomaly detection for tasks like fraud detection and misinformation, though it is incremental as it focuses on benchmarking rather than new methods.
The paper introduces AD-LLM, the first benchmark to evaluate large language models (LLMs) for anomaly detection in NLP, finding that LLMs perform well in zero-shot detection and data augmentation but struggle with model selection.
Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. Within natural language processing (NLP), AD helps detect issues like spam, misinformation, and unusual user activity. Although large language models (LLMs) have had a strong impact on tasks such as text generation and summarization, their potential in AD has not been studied enough. This paper introduces AD-LLM, the first benchmark that evaluates how LLMs can help with NLP anomaly detection. We examine three key tasks: (i) zero-shot detection, using LLMs' pre-trained knowledge to perform AD without tasks-specific training; (ii) data augmentation, generating synthetic data and category descriptions to improve AD models; and (iii) model selection, using LLMs to suggest unsupervised AD models. Through experiments with different datasets, we find that LLMs can work well in zero-shot AD, that carefully designed augmentation methods are useful, and that explaining model selection for specific datasets remains challenging. Based on these results, we outline six future research directions on LLMs for AD.