DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Overview

This repo contains the source code of DecodingTrust. This research endeavor is designed to help researchers better understand the capabilities, limitations, and potential risks associated with deploying these state-of-the-art Large Language Models (LLMs). See our paper for details.

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li.

This project is organized around the following eight primary areas of trustworthiness, including:

1. Toxicity
2. Stereotype and bias
3. Adversarial robustness
4. Out-of-Distribution Robustness
5. Privacy
6. Robustness to Adversarial Demonstrations
7. Machine Ethics
8. Fairness

Project Structure

This project is structured around subdirectories dedicated to each area of trustworthiness. Each subdir includes scripts, data, and a dedicated README for easy comprehension.

.
├── README.md
├── data/
├── toxicity/
├── stereotype/
├── adv-glue-plus-plus/
├── adv_demonstration/
├── privacy/
├── ood/
├── machine_ethics/
└── fairness/

The data subdir includes our proposed/generated datasets essential for the evaluation of trustworthiness. (⚠️WARNING: This paper contains model outputs that may be considered offensive.)

Getting Started

1. Clone the repository: Start by cloning this repository to your local machine. You can use the following command:

git clone https://github.com/boxin-wbx/DecodingTrust.git
cd DecodingTrust

2. Create a new conda environment (requires python=3.9) and activate it

conda create --name decoding_trust_env python=3.9
conda activate decoding_trust_env

3. Install our customized HELM repo, from which we will call HELM request to different chat models.

git clone https://github.com/danielz02/helm
cd helm
./pre-commit.sh

4. Navigate to the DecodingTrust repo, and install our dependencies

pip install -r requirements.txt

5. Each of the eight areas has its own subdirectory containing the respective code and README.

6. Follow the specific README: Every subdirectory has its own README. Refer to these documents for information on how to run the scripts and interpret the results.

[Important] Candidate models

In our benchmark, to have consistent conclusions and results, currently we mianly focus on evaluating the following two OpenAI models:

• gpt-3.5-turbo-0301
• gpt-4-0314

Note we use gpt-3.5-turbo-0301 (with time stamp) released in March instead of gpt-3.5-turbo for sake of model evolution to ensure reproducibility.

Currently, we have supported evaluating all the causal LLMs hosted in Huggingface. Specifically, we have tested the following open LLMs:

• Llama-v2-7B-Chat
• Vicuna-7BAlpaca-7B
• MPT-7B
• Falcon-7B
• Alpaca-7B
• RedPajama-INCITE-7B-Instruct

Tutorial

We have provided a Tutorial to help you walk through the usage of API to evaluate different trustworthiness perspectives and LLMs.

Useful tips

• Please first evaluate your experiments with ++dry_run=True flags on to check the input / output format, and use gpt-3.5-turbo-0301 to check the generation since it has lower costs.
• Suggesting saving the responses from OpenAI.
• You can check https://arxiv.org/pdf/2302.06476.pdf to know if your performance of ChatGPT is reasonable for standard tasks. Also you may find their task descriptions useful.

File usage

• main.py provides a unified entry point to evaluate all the perspectives and different LLMs with proper configuration
• chat.py provides robust APIs for creating requests to OpenAI Chat Compleition models and Huggingface autoregressive LLMs. Recommend implementing experiments based on this file. If you think chat.py is not good enough and want to make modifications, please let @acphile and @boxinw know.
• utils.py provide auxiliary functions

For other files, please refer to each subdirs for more information.

License

This project is licensed under the CC BY-SA 4.0 - see the LICENSE file for details.

Citation

Please cite the paper as follows if you use the data or code from DecodingTrust:

@article{wang2023decodingtrust,
  title={DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models},
  author={Wang, Boxin and Chen, Weixin and Pei, Hengzhi and Xie, Chulin and Kang, Mintong and Zhang, Chenhui and Xu, Chejian and Xiong, Zidi and Dutta, Ritik and Schaeffer, Rylan and others},
  journal={arXiv preprint arXiv:2306.11698},
  year={2023}
}

Contact

Please reach out to us if you have any questions or suggestions. You can submit an issue or pull request, or send an email to [email protected].

Thank you for your interest in DecodingTrust. We hope our work will contribute to a more trustworthy, fair, and robust AI future.