@inproceedings{yang-etal-2025-ad,
title = "{AD}-{LLM}: Benchmarking Large Language Models for Anomaly Detection",
author = "Yang, Tiankai and
Nian, Yi and
Li, Li and
Xu, Ruiyao and
Li, Yuangang and
Li, Jiaqi and
Xiao, Zhuo and
Hu, Xiyang and
Rossi, Ryan A. and
Ding, Kaize and
Hu, Xia and
Zhao, Yue",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.79/",
doi = "10.18653/v1/2025.findings-acl.79",
pages = "1524--1547",
ISBN = "979-8-89176-256-5",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T AD-LLM: Benchmarking Large Language Models for Anomaly Detection
%A Yang, Tiankai
%A Nian, Yi
%A Li, Li
%A Xu, Ruiyao
%A Li, Yuangang
%A Li, Jiaqi
%A Xiao, Zhuo
%A Hu, Xiyang
%A Rossi, Ryan A.
%A Ding, Kaize
%A Hu, Xia
%A Zhao, Yue
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F yang-etal-2025-ad
%X 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.
%R 10.18653/v1/2025.findings-acl.79
%U https://aclanthology.org/2025.findings-acl.79/
%U https://doi.org/10.18653/v1/2025.findings-acl.79
%P 1524-1547
Markdown (Informal)
[AD-LLM: Benchmarking Large Language Models for Anomaly Detection](https://aclanthology.org/2025.findings-acl.79/) (Yang et al., Findings 2025)
ACL
- Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, and Yue Zhao. 2025. AD-LLM: Benchmarking Large Language Models for Anomaly Detection. In Findings of the Association for Computational Linguistics: ACL 2025, pages 1524–1547, Vienna, Austria. Association for Computational Linguistics.