Awesome

Awesome LLMs for Anomaly and OOD Detection

Tracking advancements in "Large Language Models for Anomaly and Out-of-Distribution Detection", based on our detailed survey found at Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey.

Table of Contents

• Introduction
• Taxonomy
  • LLMs for Augmentation
  • LLMs for Detection
  • LLMs for Explanation
• Citation

Introduction

Detecting anomalies or out-of-distribution (OOD) samples is critical for maintaining the reliability and trustworthiness of machine learning systems. Recently, Large Language Models (LLMs) have demonstrated their effectiveness not only in natural language processing but also in broader applications due to their advanced comprehension and generative capabilities. The integration of LLMs into anomaly and OOD detection marks a significant shift from the traditional paradigm in the field. This survey focuses on the problem of anomaly and OOD detection under the context of LLMs. We propose a new taxonomy to categorize existing approaches into three classes based on the role played by LLMs. Following our proposed taxonomy, we further discuss the related work under each of the categories and finally discuss potential challenges and directions for future research in this field.

Taxonomy

LLMs for Augmentation

Exploring how LLMs support the augmentation of detection capabilities without being direct detectors.

LLMs for Detection

Highlighting how LLMs directly contribute to detecting anomalies and out-of-distribution samples.

LLMs for Explanation

Detailing how LLMs aid in explaining the detection results, enhancing understanding and trust.

Citation

If you find this work useful, please cite our survey paper:

@article{xu2024large,
      title={Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey}, 
      author={Ruiyao Xu and Kaize Ding},
      journal={arXiv preprint arXiv:2409.01980},
      year={2024},
}