AAAI'25: MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector

Wenjie Fu¹, Huandong Wang², Chen Gao², Guanghua Liu¹, Yong Li², Tao Jiang¹*

¹ Huazhong University of Science and Technology   ² Tsinghua University

• AAAI'25: MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector
  • Overview
  • A Quick Glance on ChatGLM*
  • Requirements
  • WikiMIA-24 Dataset
  • Running all baselines
  • Running MIA-Tuner
  • Reproducing All Experiment in Our Paper
  • Citation

This is the official implementation of the paper "MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector". The proposed MIA-Tuner is implemented as follows.

Overview

Instructing aligned and unaligned LLMs themselves to detect texts that have been seen during the pre-training phase.

A Quick Glance on ChatGLM*

MIA-Tuner.mp4

*Please refer ./inference_on_zhipuai for reproducing.

Requirements

• torch>=2.2.0
• accelerate==0.32.1
• transformers==4.42.4
• huggingface_hub==0.23.4
• datasets==2.20.0
• deepeval==0.21.73
• langchain==0.2.14
• Wikipedia_API==0.6.0
• numpy>=1.24.4
• scikit-learn>=1.1.3
• pyyaml>=6.0
• tqdm>=4.64.1

Dependency can be installed with the following command:

pip install -r requirements.txt

WikiMIA-24 Dataset

We provide a collection of related datasets in 🤗 Huggingface.

Running all baselines

In this repo, we provide an all-in-one script run_baselines.py for running all exiting baselines in one commond.

python run_baselines.py --model ${model} --dataset ${DATASET_NAME} --block_size ${BLOCK_SIZE}

Running MIA-Tuner

• Aligned LLMs

  accelerate launch mia_hybrid.py -m ${model} --unaligned_model -d ${DATASET_NAME} \
  --block_size ${BLOCK_SIZE} --epochs ${EPOCHS} --batch_size ${BATCH_SIZE} --learning_rate ${LEARNING_RATE} \
  --gradient_accumulation_steps ${GRADIENT_ACCUMULATION_STEPS}

• Unligned LLMs

  accelerate launch mia_hybrid.py -m ${model} --unaligned_model -d ${DATASET_NAME} \
  --block_size ${BLOCK_SIZE} --epochs ${EPOCHS} --batch_size ${BATCH_SIZE} --learning_rate ${LEARNING_RATE} \
  --gradient_accumulation_steps ${GRADIENT_ACCUMULATION_STEPS}

Reproducing All Experiment in Our Paper

All scripts for reproducing results in our paper can be found in ./exp_scripts

• ./exp_scripts/main_exp.sh
• ./exp_scripts/main_exp_baselines.sh
• ./exp_scripts/analysis_training_scale.sh
• ./exp_scripts/analysis_model_size.sh
• ./exp_scripts/analysis_text_length.sh

Citation

Please consider to cite our paper if you find MIA-Tuner helpful in your research

@inproceedings{fu2024membership,
    title={{MIA}-Tuner: Adapting Large Language Models as Pre-training Text Detector},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    author = {Fu, Wenjie and Wang, Huandong and Gao, Chen and Liu, Guanghua and Li, Yong and Jiang, Tao},
    year = {2025},
    address = {Philadelphia, Pennsylvania, USA}
}