Adversarial Math Word Problem Generation

This is the official repository for Adversarial Math Word Problem Generation . This repository contains the code for generating adversarial math problems, attacking the models, and analyzing the generated adversarial examples.

Installation

Install all required packages using pip install -r requirements.txt.

Input Data Format

The original math problems should be formatted in a json file with "Problem" and "final_ans" fields:

[
    {
        "Problem": "<Problem 0>",
        "final_ans": "<Answer 0>"
        ...
    },
    {
        "Problem": "<Problem 1>",
        "final_ans": "<Answer 1>"
        ...
    },
    ...
]

An input data example from GSM8K is provided on data/example/input_example.json

Generation

Go to generation folder and run main.py to generate adversarial examples. The script will call GPT-4-turbo once (~$0.01) for each original problem to generate python code. Here is an example command for generating 20 adversarial math problems:

python main.py \
  --action generation \
  --output_directory ../generated \
  --original_question_file ../data/example/input_example.json \
  --openai_key_path PATH/TO/OPENAI/APIKEY/IN/TXT \
  --code_directory ../data/example/ode \
  --m 20 

You can specifiy your own generation settings, file structure/path, etc. in config.py. The final generated adversarial examples will be saved in ../generated/adversarial_example.json. We release all the generated adversarial examples for our experiments in data/experimental_data folder.

Attack

To attack the models generated adversarial examples, go to attack folder and run attack.py. An example command for attacking meta-math/MetaMath-70B-V1.0 is:

python attack.py \
  --model meta-math/MetaMath-70B-V1.0 \
  --adversarial_data_path ../generated/adversarial_example.json \
  --main_output_directory_path ../results 

The model outputs are saved in ../results. The script will generate model output summary and save it in model_output_summary and all incorrect results in incorrect_problems.

Analysis

To analyze the generated adversarial examples, run main.py in the generation folder with --action analysis. The script will compare model's incorrect predictions with the ground truth. An example command for analyzing MetaMath-70B-V1.0 is:

python main.py \
  --action analysis \
  --output_directory ../analysis \
  --model_name MetaMath-70B-V1.0 \
  --model_incorrect_file /data/example/incorrect_problems/MetaMath-70B-V1.0.json 

You can specifiy your own analysis settings, show plots, etc. in config.py. The final analysis results will be saved in ../analysis/

Citation

If you find this code useful, we'd appreciate it if you cite the following paper:

@misc{xie2024adv,
      title={Adversarial Math Word Problem Generation}, 
      author={Roy Xie and Chengxuan Huang and Junlin Wang and Bhuwan Dhingra},
      year={2024},
      eprint={2402.17916},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}