ASSERT

ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models

Paper Link: https://arxiv.org/abs/2310.09624
Authors: Alex Mei*, Sharon Levy*, William Yang Wang

Setup

• This project is based on python v3.11.4 and pip v23.1.2
• Install venv: https://realpython.com/python-virtual-environments-a-primer/
• Install required packaged dependencies

Dependencies

• To install package dependencies, run pip3 install -r requirements.txt
• To update package dependencies, run pip3 freeze > requirements.txt

Contents

Data

• data/safetext/* holds the source dataset SafeText
  • paired_samples.json contains the original SafeText dataset
  • safe_samples.json and unsafe_samples.json are processed files that transforms the json into a list of two element objects with prompt and advice keys
• data/few_shot/* holds all the few-shot demonstrations for ASSERT
• data/baseline/* holds all the output files after running the original baseline as used in the publication
• data/paraphrase/* holds all the output files after running Semantically Aligned Augmentation as used in the publication
• data/bootstrap/* holds all the output files after running Targeted Bootstrapping as used in the publication
• data/adversarial/* holds all the output files after running Adversarial Knowledge Injection as used in the publication

Modules

• models/eval.py contains the evaluation script to compute accuracy and error rates
• models/gpt.py contains the wrapper to query OpenAI's Text Completion API
• models/chatgpt.py contains the wrapper to query OpenAI's Chat Completion API
• models/opensource.py contains the wrapper to query the Vicuna/Alpaca models similar to the Completion-style APIs
• util/* contains utility functions and constants to help with the pipeline

Main

• baseline.py is the file to run the baseline pipeline
• semantic.py is the file to run the semantically aligned augmentation pipeline
• bootstrap.py is the file to run the targeted bootstrapping pipeline
• adversarial.py is the file to run the adversarial knowledge injection pipeline

Environment Variables

Add a .env file to the root of the project with the following variables:

• OPENAI_API_KEY: API key for OpenAI Access
• VICUNA_PATH: path to location of pre-trained Vicuna Model Checkpoint
• ALPACA_PATH: path to location of pre-trained Alpaca Model Checkpoint
• CUDA_VISIBLE_DEVICES: comma separated list of GPU IDs to use for CUDA

Usage

• Create a new venv with python3 -m venv .venv
• Activate venv with source .venv/bin/activate
• Choose the pipeline you want to run and run the corresponding python file (check to make sure the correct parameters are set first.)

Attribution

When using resources based on our project, please cite the following paper, to appear in EMNLP 2023:

@misc{mei2023assert,
      title={ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models}, 
      author={Alex Mei and Sharon Levy and William Yang Wang},
      year={2023},
      eprint={2310.09624},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}