# Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations

arXiv: https://arxiv.org/abs/2601.17786 · HTML: https://mlsanity.com/paper/2601.17786/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security.
- Builds on 28 of its 56 references that are in the map.
- First posted on arXiv 25 Jan 2026.

## Placed in

- LLM safety and security / [Safety alignment, refusal and guardrails](https://mlsanity.com/safety/safety-alignment-refusal-and-guardrails/)

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Generative Adversarial Active Learning for Unsupervised Outlier Detection](https://mlsanity.com/paper/1809.10816/) (2018)
- [Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation](https://mlsanity.com/paper/2507.18224/) (2025)
- [Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2505.23352/) (2025)
- [N24News: A New Dataset for Multimodal News Classification](https://mlsanity.com/paper/2108.13327/) (2021)
- [BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks](https://mlsanity.com/paper/2508.08127/) (2025)
- [DATE: Detecting Anomalies in Text via Self-Supervision of Transformers](https://mlsanity.com/paper/2104.05591/) (2021)
- [A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection](https://mlsanity.com/paper/2502.13308/) (2025)
- [NLP-ADBench: NLP Anomaly Detection Benchmark](https://mlsanity.com/paper/2412.04784/) (2024)
- [CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey](https://mlsanity.com/paper/2504.14280/) (2025)
- [A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models](https://mlsanity.com/paper/2509.06609/) (2025)
- [OFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Models](https://mlsanity.com/paper/2601.12996/) (2026)
- [From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach](https://mlsanity.com/paper/2202.05525/) (2022)
- [Bisecle: Binding and Separation in Continual Learning for Video Language Understanding](https://mlsanity.com/paper/2507.00469/) (2025)
- [Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time](https://mlsanity.com/paper/2511.07023/) (2025)
- [Few-shot Anomaly Detection in Text with Deviation Learning](https://mlsanity.com/paper/2308.11780/) (2023)
- [Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation](https://mlsanity.com/paper/2407.19944/) (2024)
- [A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03545/) (2021)
- [TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection](https://mlsanity.com/paper/2501.11960/) (2025)
- [Text Anomaly Detection with Simplified Isolation Kernel](https://mlsanity.com/paper/2510.13197/) (2025)
- [Regularized Contrastive Partial Multi-view Outlier Detection](https://mlsanity.com/paper/2408.07819/) (2024)

## Built on by (most cited first, 3 of 3)

- [DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learning](https://mlsanity.com/paper/2603.16459/) (2026)
- [SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection](https://mlsanity.com/paper/2609.08200/) (2026)
- [DiFA: Dual Evidence Fusion and Aggregation for Token-Level Text Anomaly Detection](https://mlsanity.com/paper/2609.22136/) (2026)

## Abstract (arXiv)

Text anomaly detection (TAD) plays a critical role in various language-driven real-world applications, including harmful content moderation, phishing detection, and spam review filtering. While two-step "embedding-detector" TAD methods have shown state-of-the-art performance, their effectiveness is often limited by the use of a single embedding model and the lack of adaptability across diverse datasets and anomaly types. To address these limitations, we propose to exploit the embeddings from multiple pretrained language models and integrate them into MCA2, a multi-view TAD framework. MCA2 adopts a multi-view reconstruction model to effectively extract normal textual patterns from multiple embedding perspectives. To exploit inter-view complementarity, a contrastive collaboration module is designed to leverage and strengthen the interactions across different views. Moreover, an adaptive allocation module is developed to automatically assign the contribution weight of each view, thereby improving the adaptability to diverse datasets. Extensive experiments on 10 benchmark datasets verify the effectiveness of MCA2 against strong baselines. The source code of MCA2 is available at https://github.com/yankehan/MCA2.