Papers with Anomaly detection
AD-LLM: Benchmarking Large Language Models for Anomaly Detection (2025.findings-acl)
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Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao
| Challenge: | Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. |
| Approach: | They propose a benchmark that evaluates how large language models (LLMs) can help with NLP anomaly detection. |
| Outcome: | The proposed model can perform zero-shot detection without tasks-specific training, data augmentation and model selection, and it can suggest unsupervised AD models. |
DE-CLIP: Few-Shot Anomaly Detection via Difference-Guided Embedding Editing (2026.acl-long)
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| Challenge: | Existing approaches to detect anomalies are limited due to the lack of anomalous samples . |
| Approach: | They propose a framework that edits text embeddings based on the differences between normal and anomalous samples. |
| Outcome: | The proposed framework achieves 96.6% and 96.99% AUROC on MVTec datasets. |
NLP-ADBench: NLP Anomaly Detection Benchmark (2025.findings-emnlp)
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| Challenge: | Anomaly detection (AD) is an important machine learning task, but its effectiveness in detecting harmful content, phishing attempts, and spam reviews is limited. |
| Approach: | They introduce NLP-ADBench, the most comprehensive NLP anomaly detection benchmark to date . it includes eight curated datasets and 19 state-of-the-art algorithms . |
| Outcome: | The NLP-ADBench benchmark includes 19 state-of-the-art methods and 8 curated datasets . no single model dominates across all datasets, indicating need for automated model selection . |
Can LLMs Find a Needle in a Haystack? A Look at Anomaly Detection Language Modeling (2025.findings-emnlp)
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| Challenge: | Anomaly detection (AD) is a problem in machine learning, but it is not always competitive on certain datasets. |
| Approach: | They propose a new approach to Anomaly detection based on large pre-trained language models in three modalities. |
| Outcome: | The proposed model beats baselines on anomaly detection when presented as imbalanced classification problem regardless of the concentration of anomalous samples. |
Enhancing Two Steps Textual Anomaly Detection through Anisotropy Mitigation (2026.acl-long)
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| Challenge: | Recent approaches to anomaly detection focus on embeddings from pre-trained models . however, the geometric properties of pre-training embedders can hinder detection algorithms . |
| Approach: | They propose to apply anomaly detection algorithms to embeddings from pre-trained models to improve accuracy. |
| Outcome: | The proposed approach improves similarity-trained models by adapting embeddings to assumptions made by classical detection algorithms. |