mlsanityData to 7 Oct 2026

TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection

arXiv 2501.11960 ↗First posted 21 Jan 2025cs.CL cs.AIcited by 6

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Abstract (arXiv)

Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural language processing tasks. Despite the growing adoption of embedding-based methods, their effectiveness and generalizability across diverse application scenarios remain under-explored. To address this, we present TAD-Bench, a comprehensive benchmark designed to systematically evaluate embedding-based approaches for text anomaly detection. TAD-Bench integrates multiple datasets spanning different domains, combining state-of-the-art embeddings from large language models with a variety of anomaly detection algorithms. Through extensive experiments, we analyze the interplay between embeddings and detection methods, uncovering their strengths, weaknesses, and applicability to different tasks. These findings offer new perspectives on building more robust, efficient, and generalizable anomaly detection systems for real-world applications.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 23 of its 53 references that are in the map. First posted on arXiv 21 Jan 2025.

Builds on

23 of 53 references.

References · 23

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11 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Text Anomaly Detection with Simplified Isolation Kernelcited by 5
  2. 2026Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representationscited by 3
  3. 2025Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddingscited by 1
  4. 2025Isolation-based Spherical Ensemble Representations for Tabular Anomaly Detectioncited by 0
  5. 2026SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detectioncited by 0
  6. 2026DiFA: Dual Evidence Fusion and Aggregation for Token-Level Text Anomaly Detectioncited by 0