AnoLLM: Large Language Models for Tabular Anomaly Detection (ICLR 2025)

This repository contains the implementation of the paper:

AnoLLM: Large Language Models for Tabular Anomaly Detection

International Conference on Learning Representations (ICLR 2025)

Che-Ping Tsai, Ganyu Teng, Phil Wallis, Wei Ding.

Introduction

AnoLLM is a novel framework that leverages large language models (LLMs) for unsupervised tabular anomaly detection. It can effectively handle mixed-type tabular data (e.g., continuous/numerical, discrete/categorical, and texts) by adapting a pre-trained LLM with serialized tabular data in the text format. During inference, AnoLLM assigns anomaly scores based on the negative log-likelihood generated by the LLM. Our empirical results indicate that AnoLLM delivers the best performance on six benchmark datasets with mixed feature types.

Installing Dependencies

Python version: 3.10

Create environment

conda create -n anollm python=3.10
conda activate anollm

Install packages

pip install -r requirements.txt

Install Torch, ensuring that the version you choose is compatible with your CUDA version.

pip install torch==2.3.1

Overwrite pyod version to avoid bugs

pip install pyod==2.0.1

Rerun our experiments

1. Download the following datasets from Kaggle and put them to data/[dataset_name]/
2. vifd (Vehicle Insurance Fraud Detection)
3. fraudecom (Fraud E-commerce)
4. Run the corresponding scripts for each experiment: bash scripts/exp1-mixed_benchmark/run_anollm.sh bash scripts/exp1-mixed_benchmark/run_baselines.sh bash scripts/exp2-odds/run_anollm.sh bash scripts/exp2-odds/run_baselines.sh bash scripts/exp3-binning_effect/run_binning_odds.sh bash scripts/exp4-model_size/run_anollm_1.7B_mixed.sh bash scripts/exp4-model_size/run_anollm_1.7B_odds.sh

Using your own datasets

To use a custom dataset, create a dataframe with the following structure: {feature_name:feature_values}. Please refer to load_dataset() function in src/data_utils.py for further guidance.

Training Models

For AnoLLM, we use the following command:

CUDA_VISIBLE_DEVICES=$TRAIN_GPUS torchrun --nproc_per_node=$n_train_node train_anollm.py --dataset $dataset --n_splits $n_splits --split_idx 0  --binning standard --setting semi_supervised --max_steps 2000 --batch_size $batch_size --model $model

Check the argument parser in train_anollm.py for options for datasets and models

For baselines, we use the following command:

CUDA_VISIBLE_DEVICES=0 python evaluate_baselines.py --dataset $dataset --n_splits $n_splits --normalize  --setting  semi_supervised --split_idx $split_idx 

Check the argument parser in evaluate_baselines.py for options for datasets

Evaluation

To evaluate AnoLLM, we use the following command:

CUDA_VISIBLE_DEVICES=$INFERENCE_GPUS  torchrun --nproc_per_node=$n_test_node evaluate_anollm.py --dataset $dataset --n_splits $n_splits --split_idx 0  --setting semi_supervised --batch_size $eval_batch_size  --n_permutations $n_permutations --model $model --binning standard  

We evaluate the quality of synthetic data using metrics from various aspects.

python src/get_results.py --dataset $dataset --n_splits $n_splits --setting semi_supervised

License

This project is licensed under the Apache-2.0 License.

Acknowledgement

Baselines were adapted from https://github.com/vicliv/DTE. Part of the code was adapted from https://github.com/kathrinse/be_great. Thanks to all the authors for their great works!

Reference

@inproceedings{tsai2025anollm,
  title={AnoLLM: Large Language Models for Tabular Anomaly Detection},
  author={Tsai, Che-Ping and Teng, Ganyu and Wallis, Phil and Ding, Wei},
  booktitle={The thirteenth International Conference on Learning Representations},
  year={2025},
  note={Accepted, to appear},
}