# Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey

arXiv: https://arxiv.org/abs/2409.01980 · HTML: https://mlsanity.com/paper/2409.01980/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 114 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 30 of 61)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [Out-of-Distribution Detection with Deep Nearest Neighbors](https://mlsanity.com/paper/2204.06507/) (2022)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks](https://mlsanity.com/paper/2305.11175/) (2023)
- [ReAct: Out-of-distribution Detection With Rectified Activations](https://mlsanity.com/paper/2111.12797/) (2021)
- [Scaling Out-of-Distribution Detection for Real-World Settings](https://mlsanity.com/paper/1911.11132/) (2019)
- [CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances](https://mlsanity.com/paper/2007.08176/) (2020)
- [Exploring the Limits of Out-of-Distribution Detection](https://mlsanity.com/paper/2106.03004/) (2021)

## Built on by (most cited first, 27 of 27)

- [Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances](https://mlsanity.com/paper/2409.11884/) (2024)
- [AD-LLM: Benchmarking Large Language Models for Anomaly Detection](https://mlsanity.com/paper/2412.11142/) (2024)
- [BadThink: Triggered Overthinking Attacks on Chain-of-Thought Reasoning in Large Language Models](https://mlsanity.com/paper/2511.10714/) (2025)
- [Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models](https://mlsanity.com/paper/2410.20199/) (2024)
- [CMOOD: Concept-based Multi-label OOD Detection](https://mlsanity.com/paper/2411.13578/) (2024)
- [Out-of-Distribution Detection using Synthetic Data Generation](https://mlsanity.com/paper/2502.03323/) (2025)
- [VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models](https://mlsanity.com/paper/2511.07299/) (2025)
- [MirageBackdoor: A Stealthy Attack that Induces Think-Well-Answer-Wrong Reasoning](https://mlsanity.com/paper/2604.06840/) (2026)
- [Foundation Models for Anomaly Detection: Vision and Challenges](https://mlsanity.com/paper/2502.06911/) (2025)
- [Text Meets Topology: Rethinking Out-of-distribution Detection in Text-Rich Networks](https://mlsanity.com/paper/2508.17690/) (2025)
- [VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion](https://mlsanity.com/paper/2511.08173/) (2025)
- [CoAct: Co-Active LLM Preference Learning with Human-AI Synergy](https://mlsanity.com/paper/2604.17501/) (2026)
- [Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems](https://mlsanity.com/paper/2510.21254/) (2025)
- [A Principled Approach to Unsupervised Anomaly Detection](https://mlsanity.com/paper/2609.21800/) (2026)
- [Breaking Semantic Hegemony: Decoupling Principal and Residual Subspaces for Generalized OOD Detection](https://mlsanity.com/paper/2602.05360/) (2026)
- [Managing Uncertainty in LLM-based Multi-Agent System Operation](https://mlsanity.com/paper/2602.23005/) (2026)
- [LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs](https://mlsanity.com/paper/2603.20293/) (2026)
- [Adaptive and Robust Data Poisoning Detection and Sanitization in Wearable IoT Systems using Large Language Models](https://mlsanity.com/paper/2511.02894/) (2025)
- [JT-DA: Enhancing Data Analysis with Tool-Integrated Table Reasoning Large Language Models](https://mlsanity.com/paper/2512.06859/) (2025)
- [From Exposure to Internalization: Dual-Stream Calibration for In-context Clinical Reasoning](https://mlsanity.com/paper/2604.06262/) (2026)
- [Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection](https://mlsanity.com/paper/2606.30009/) (2026)
- [Influence of Prompt Engineering on Small Language Models for Guarded Query Routing](https://mlsanity.com/paper/2607.24801/) (2026)
- [SABRE: A Multi-Agent Approach for Selecting Out-of-Distribution Detectors Under a Budget](https://mlsanity.com/paper/2608.02959/) (2026)
- [On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models](https://mlsanity.com/paper/2608.10530/) (2026)
- [Distributional sentiment modeling and anomaly detection for consumer complaint assessment](https://mlsanity.com/paper/2609.31653/) (2026)
- [The Privacy Fallacy of Crowdsourced Fine-Tuning: Extracting Proprietary Data via Topic-Based Poisoning](https://mlsanity.com/paper/2609.33985/) (2026)
- [Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting](https://mlsanity.com/paper/2610.17399/) (2026)

## Abstract (arXiv)

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 two 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. We also provide an up-to-date reading list of relevant papers.