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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:

Toxicity
Stereotype and bias
Adversarial robustness
Out-of-Distribution Robustness
Privacy
Robustness to Adversarial Demonstrations
Machine Ethics
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.

.
├── data
│   ├── adv_demonstration
│   │   ├── backdoor
│   │   │   ├── experiment1
│   │   │   ├── experiment2
│   │   │   └── experiment3
│   │   ├── counterfactual
│   │   │   ├── control_raising
│   │   │   ├── control_raising_cf
│   │   │   ├── irregular_form
│   │   │   ├── irregular_form_cf
│   │   │   ├── main_verb
│   │   │   ├── main_verb_cf
│   │   │   ├── snli_hypothesis
│   │   │   ├── snli_hypothesis_cf
│   │   │   ├── snli_premise
│   │   │   ├── snli_premise_cf
│   │   │   ├── syntactic_category
│   │   │   └── syntactic_category_cf
│   │   ├── generations
│   │   │   ├── backdoor
│   │   │   ├── counterfactual
│   │   │   └── spurious
│   │   ├── README.md
│   │   └── spurious
│   │       ├── adverb
│   │       ├── embedded_under_verb
│   │       ├── l_relative_clause
│   │       ├── passive
│   │       ├── PP
│   │       └── s_relative_clause
│   ├── adv-glue-plus-plus
│   │   ├── data
│   │   │   ├── advglue_plus_plus.json
│   │   │   ├── alpaca.json
│   │   │   ├── stable-vicuna.json
│   │   │   └── vicuna.json
│   │   └── README.md
│   ├── fairness
│   │   ├── data
│   │   │   └── adult
│   │   ├── data_generation.py
│   │   ├── datasets
│   │   │   ├── abstract_dataset.py
│   │   │   ├── adult.py
│   │   │   └── __init__.py
│   │   ├── fairness_data
│   │   │   ├── adult_0_200_test_base_rate_0.0.jsonl
│   │   │   ├── adult_0_200_test_base_rate_0.5.jsonl
│   │   │   ├── adult_0_200_test_base_rate_1.0.jsonl
│   │   │   ├── adult_0_200_train_br_0.0_test_br_0.0.jsonl
│   │   │   ├── adult_0_200_train_br_0.0_test_br_0.5.jsonl
│   │   │   ├── adult_0_200_train_br_0.0_test_br_1.0.jsonl
│   │   │   ├── adult_0_8.jsonl
│   │   │   ├── adult_16_200_train_br_0.0_test_br_0.5.jsonl
│   │   │   ├── adult_32_200_train_base_rate_0.0.jsonl
│   │   │   ├── adult_32_200_train_base_rate_0.5.jsonl
│   │   │   ├── adult_32_200_train_base_rate_1.0.jsonl
│   │   │   ├── adult_32_200_train_br_0.0_test_br_0.0.jsonl
│   │   │   ├── adult_32_200_train_br_0.0_test_br_0.5.jsonl
│   │   │   ├── adult_32_200_train_br_0.5_test_br_0.0.jsonl
│   │   │   ├── adult_32_200_train_br_1.0_test_br_0.0.jsonl
│   │   │   ├── gt_labels_adult_0_200_test_base_rate_0.0.npy
│   │   │   ├── gt_labels_adult_0_200_test_base_rate_0.5.npy
│   │   │   ├── gt_labels_adult_0_200_test_base_rate_1.0.npy
│   │   │   ├── gt_labels_adult_0_8.npy
│   │   │   ├── gt_labels_adult_16_200_train_br_0.0_test_br_0.5.npy
│   │   │   ├── gt_labels_adult_32_200_train_base_rate_0.0.npy
│   │   │   ├── gt_labels_adult_32_200_train_base_rate_0.5.npy
│   │   │   ├── gt_labels_adult_32_200_train_base_rate_1.0.npy
│   │   │   ├── gt_labels_adult_32_200_train_br_0.0_test_br_0.5.npy
│   │   │   ├── sensitive_attr_adult_0_200_test_base_rate_0.0.npy
│   │   │   ├── sensitive_attr_adult_0_200_test_base_rate_0.5.npy
│   │   │   ├── sensitive_attr_adult_0_200_test_base_rate_1.0.npy
│   │   │   ├── sensitive_attr_adult_0_8.npy
│   │   │   ├── sensitive_attr_adult_16_200_train_br_0.0_test_br_0.5.npy
│   │   │   ├── sensitive_attr_adult_32_200_train_base_rate_0.0.npy
│   │   │   ├── sensitive_attr_adult_32_200_train_base_rate_0.5.npy
│   │   │   ├── sensitive_attr_adult_32_200_train_base_rate_1.0..npy
│   │   │   └── sensitive_attr_adult_32_200_train_br_0.0_test_br_0.5.npy
│   │   ├── generations
│   │   │   ├── gpt-3.5-turbo-0301_adult_0_200_8.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult_0_200.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult_0.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult_15.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult_16_200.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult_30.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult_32_200.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult_32_60.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult_32.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult_34.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult_4_200.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult_8_200.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_adult.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_compas_0_60.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_compas_4_8.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_german_0_60.jsonl
│   │   │   ├── gpt-3.5-turbo-0301_german_0_80.jsonl
│   │   │   ├── gpt-4-0314_adult_0_200_8.jsonl
│   │   │   ├── gpt-4-0314_adult_0_200.jsonl
│   │   │   ├── gpt-4-0314_adult_0_40_8.jsonl
│   │   │   ├── gpt-4-0314_adult_0.jsonl
│   │   │   ├── gpt-4-0314_adult_15.jsonl
│   │   │   ├── gpt-4-0314_adult_16_200.jsonl
│   │   │   ├── gpt-4-0314_adult_30.jsonl
│   │   │   ├── gpt-4-0314_adult_32_200.jsonl
│   │   │   ├── gpt-4-0314_adult_32_60.jsonl
│   │   │   ├── gpt-4-0314_adult_32.jsonl
│   │   │   ├── gpt-4-0314_adult_34.jsonl
│   │   │   ├── gpt-4-0314_adult_4_200.jsonl
│   │   │   ├── gpt-4-0314_adult_8_200.jsonl
│   │   │   ├── gpt-4-0314_adult.jsonl
│   │   │   ├── gpt-4-0314_compas_0_60.jsonl
│   │   │   ├── gpt-4-0314_german_0_60.jsonl
│   │   │   ├── gpt-4-0314_german_0_80.jsonl
│   │   │   ├── gpt-4-0314_german_16_60.jsonl
│   │   │   └── gpt-4-0314_german_32_60.jsonl
│   │   └── README.md
│   ├── machine_ethics
│   │   ├── ethics
│   │   │   ├── commonsense
│   │   │   ├── deontology
│   │   │   ├── justice
│   │   │   ├── README.txt
│   │   │   ├── utilitarianism
│   │   │   └── virtue
│   │   ├── jiminy_subset.csv
│   │   ├── jiminy_test.json
│   │   ├── jiminy_train.json
│   │   └── README.md
│   ├── ood
│   │   ├── generations
│   │   │   ├── knowledge
│   │   │   └── styles
│   │   ├── knowledge
│   │   │   └── knowledge.json
│   │   ├── knowledge.json
│   │   ├── README.md
│   │   ├── style.json
│   │   └── styles
│   │       └── style.json
│   ├── privacy
│   │   ├── enron_data
│   │   │   ├── context.pkl
│   │   │   ├── email2name.pkl
│   │   │   ├── email_freq.pkl
│   │   │   ├── five_shot_non_domain.pkl
│   │   │   ├── five_shot.pkl
│   │   │   ├── name2email.pkl
│   │   │   ├── one_shot_non_domain.pkl
│   │   │   ├── one_shot.pkl
│   │   │   ├── two_shot_non_domain.pkl
│   │   │   └── two_shot.pkl
│   │   ├── generations
│   │   │   ├── enron
│   │   │   ├── pii
│   │   │   └── understanding
│   │   └── README.md
│   ├── stereotype
│   │   ├── dataset
│   │   │   ├── stereotype_bias_data.jsonl
│   │   │   ├── system_prompts.csv
│   │   │   └── user_prompts.csv
│   │   ├── generations
│   │   │   ├── benign
│   │   │   ├── chavinlo
│   │   │   ├── lmsys
│   │   │   ├── meta-llama
│   │   │   ├── mosaicml
│   │   │   ├── openai
│   │   │   ├── README.md
│   │   │   ├── targeted
│   │   │   ├── tiiuae
│   │   │   ├── together
│   │   │   └── untargeted
│   │   └── README.md
│   └── toxicity
│       ├── generations
│       │   ├── realtoxicityprompts
│       │   ├── system_prompts
│       │   └── user_prompts
│       ├── README.md
│       ├── toxicity_prompts.py
│       └── user_prompts
│           ├── nontoxic.jsonl
│           ├── toxic.jsonl
│           ├── toxic.jsonl.gpt3.n=25.out1.split.annotated.jsonl.all.jsonl
│           ├── toxic.jsonl.gpt3.n=25.out1.split.annotated.jsonl.filtered.jsonl
│           ├── toxic.jsonl.gpt4.n=25.out1.split.annotated.jsonl.all.jsonl
│           └── toxic.jsonl.gpt4.n=25.out1.split.annotated.jsonl.filtered.jsonl
├── Dockerfile
├── dry_run.sh
├── LICENSE
├── pyproject.toml
├── README.md
├── requirements.txt
├── setup.cfg
├── src
│   └── dt
│       ├── chat.py
│       ├── configs
│       │   ├── adv_demonstration
│       │   ├── advglue
│       │   ├── configs.py
│       │   ├── config.yaml
│       │   ├── fairness
│       │   ├── __init__.py
│       │   ├── machine_ethics
│       │   ├── ood
│       │   ├── privacy
│       │   ├── stereotype
│       │   └── toxicity
│       ├── conversation.py
│       ├── __init__.py
│       ├── main.py
│       ├── perspectives
│       │   ├── adv_demonstration
│       │   ├── advglue
│       │   ├── fairness
│       │   ├── __init__.py
│       │   ├── machine_ethics
│       │   ├── ood
│       │   ├── privacy
│       │   ├── stereotype
│       │   └── toxicity
│       ├── response.py
│       └── utils.py
└── Tutorial.md

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

(Conda +) Pip

For now, we suggest installing DecodingTrust by cloning our repository and install it in editable mode. This will keep the data, code, and configurations in the same place.

git clone https://github.com/AI-secure/DecodingTrust.git && cd DecodingTrust
pip install -e .

Please note that this will install PyTorch with pip. If your system does not have a CUDA version compatible with the PyTorch pip wheel. To install PyTorch with Conda first, as shown below.

conda create --name dt-test python=3.9 pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
conda activate dt-test
pip install "decoding-trust @ git+https://github.com/AI-secure/DecodingTrust.git"

It is also possible to install DecodingTrust as a standalone package, but you will need to clone our repository again to run it with our data.

conda create --name dt-test python=3.9
conda activate dt-test
pip install "decoding-trust @ git+https://github.com/AI-secure/DecodingTrust.git"

Support for the `ppc64le` Architecture

We also support the ppc64le architecture of IBM Power-9 platforms. To install on this platform, please first make sure you have the following conda channels so that we can utilize pre-built packages.

--add channels 'defaults'   # lowest priority
--add channels 'https://public.dhe.ibm.com/ibmdl/export/pub/software/server/ibm-ai/conda-early-access/'
--add channels 'https://public.dhe.ibm.com/ibmdl/export/pub/software/server/ibm-ai/conda/'
--add channels 'https://opence.mit.edu'
--add channels 'https://ftp.osuosl.org/pub/open-ce/current/'
--add channels 'conda-forge'   # highest priority

Then, install the following pre-built packages.

mamba create --name dt-test python==3.9 pytorch=2.0.1 torchvision=0.15.2 spacy=3.5.3 scipy=1.10.1 fairlearn~=0.9.0 scikit-learn~=1.1.2 pandas~=2.0.3 pyarrow~=11.0.0 rust -c conda-forge

Finally, install DecodingTrust with pip as usual.

Docker / Singularity

To use DecodingTrust with docker, simply pull the following docker image.

sudo docker pull danielz01/decoding-trust
docker run -it \
    -v /path/on/host:/path/in/container \
    --gpus all \
    decoding-trust/v1.0:latest [arg1 arg2 ...]

To use it in through singularity or apptainer container environments on HPC environments, simply run the following.

module load singularity  # Change it to whatever module name your singularity / apptainer environment was given
singularity pull decoding-trust-v1.0.sif docker://danielz01/decoding-trust
singularity exec --nv --bind /path/on/host:/path/in/container decoding-trust-v1.0.sif [arg1 arg2]

We will also have a container build for ppc64le platforms soon. Stay tuned!

Notes

Each of the eight areas has its own subdirectory containing the respective code and README.
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 mainly 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 the sake of model evolution to ensure reproducibility.

Currently, we have supported evaluating all the causal LLMs hosted in Huggingface or hosted locally. 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 Completion 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},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  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 boxinw2@illinois.edu.

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