mlsanityData to 7 Oct 2026

Anomaly Detection of Tabular Data Using LLMs

arXiv 2406.16308 ↗First posted 24 Jun 2024cs.LG cs.AI cs.CLcited by 12

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Large language models (LLMs) have shown their potential in long-context understanding and mathematical reasoning. In this paper, we study the problem of using LLMs to detect tabular anomalies and show that pre-trained LLMs are zero-shot batch-level anomaly detectors. That is, without extra distribution-specific model fitting, they can discover hidden outliers in a batch of data, demonstrating their ability to identify low-density data regions. For LLMs that are not well aligned with anomaly detection and frequently output factual errors, we apply simple yet effective data-generating processes to simulate synthetic batch-level anomaly detection datasets and propose an end-to-end fine-tuning strategy to bring out the potential of LLMs in detecting real anomalies. Experiments on a large anomaly detection benchmark (ODDS) showcase i) GPT-4 has on-par performance with the state-of-the-art transductive learning-based anomaly detection methods and ii) the efficacy of our synthetic dataset and fine-tuning strategy in aligning LLMs to this task.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 15 of its 28 references that are in the map. First posted on arXiv 24 Jun 2024.

Builds on

15 of 28 references.

References · 15

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  5. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  6. 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,359
  7. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  8. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
  9. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  10. 2022Can Foundation Models Wrangle Your Data?cited by 173
  11. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  12. 2023Semantic Anomaly Detection with Large Language Modelscited by 53
3 more
  1. 2024Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Reviewcited by 50
  2. 2022Latent Outlier Exposure for Anomaly Detection with Contaminated Datacited by 48
  3. 2023Deep Anomaly Detection under Labeling Budget Constraintscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 4.

12 papers.

  1. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  2. 2024Large Language Models for Anomaly and Out-of-Distribution Detection: A Surveycited by 24
  3. 2026Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMscited by 9
  4. 2024On the Potential of Open-Vocabulary Models for Object Detection in Unusual Street Scenescited by 5
  5. 2025Out-of-Distribution Detection using Synthetic Data Generationcited by 4
  6. 2025Foundation Models for Anomaly Detection: Vision and Challengescited by 3
  7. 2025Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMscited by 1
  8. 2025Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Modelscited by 1
  9. 2025Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddingscited by 1
  10. 2026CausalTAD: Injecting Causal Knowledge into Large Language Models for Tabular Anomaly Detectioncited by 1
  11. 2026ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflowscited by 0
  12. 2026EdgeLM: Edge Demonstrations for Language Models' Table Understandingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis
  2. 2023TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models
  3. 2024Can LLMs Serve As Time Series Anomaly Detectors?
  4. 2026Why Large Language Models Fail at Tabular Prediction
  5. 2026MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models
  6. 2024Retrieval Augmented Deep Anomaly Detection for Tabular Data
  7. 2024Can LLMs Understand Time Series Anomalies?
  8. 2025Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets