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Fool your (V)LLMs

Code for paper: Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations

Content

Environment

conda create -n MCQ python=3.9
conda activate MCQ
pip install -r requirements.txt

Additionally, please refer to LLaVA and mPLUG-Owl repo for specific installation.

Data

For language task, download MMLU dataset from here and put it in data/MMLU/ directory.

For vision-language task, download ScienceQA dataset from here and put it in data/ScienceQA/ directory. Follow data/preprocess_ScienceQA.ipynb for preprocessing. Only questions with image input are used.

Usage

LLM MCQ

  1. Original option order:
python LLMs_attack.py --data_dir ./data/MMLU/ --engine vicuna7b
  1. Permute option orders:
python LLMs_attack.py --data_dir ./data/MMLU/ --engine vicuna7b --permutation_attack
  1. Reduce the number of options:
python LLMs_attack.py --data_dir ./data/MMLU/ --engine vicuna7b --n_reduced 2
  1. Reduce the number of options and then permute:
python LLMs_attack.py --data_dir ./data/MMLU/ --engine vicuna7b --n_reduced 2 --reduce_attack
  1. Move all ground-truth answers to certain position:
python LLMs_attack.py --data_dir ./data/MMLU/ --engine vicuna7b --position_permute

VLLM MCQ

  1. Original option order:
python VLLMs_attack.py --data_dir ./data/ScienceQA/ --engine instructblip7b
  1. Permute option orders:
python VLLMs_attack.py --data_dir ./data/ScienceQA/ --engine instructblip7b --permutation_attack
  1. Reduce the number of options:
python VLLMs_attack.py --data_dir ./data/ScienceQA/ --engine instructblip7b --n_reduced 2
  1. Reduce the number of options and then permute:
python VLLMs_attack.py --data_dir ./data/ScienceQA/ --engine instructblip7b --n_reduced 2 --reduce_attack
  1. Move all ground-truth answers to certain position:
python VLLMs_attack.py --data_dir ./data/ScienceQA/ --engine instructblip7b --position_permute

Arguments:

  • --ntrain: number of in-context demonstrations.
  • --data_dir: path to the dataset.
  • --engine: which model to use (can use multiple).
  • --n_reduced: specifies the reduced total number of options.
  • --reduce_attack: permute the options after reduction.
  • --use_subset: use subset to test.
  • --permutation_attack: adversarial permutation to the options.
  • --position_permute: move all GT answers to certain position (A/B/C/D).
  • --load_in_8bit: (optional) 8 bit loading to fit large models into GPU memory.

Citation

@article{zong2023fool,
  title={Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations},
  author={Zong, Yongshuo and Yu, Tingyang and Zhao, Bingchen and Chavhan, Ruchika and Hospedales, Timothy},
  journal={arXiv preprint arXiv:2310.01651},
  year={2023}
}