DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT
  Models
Authors: name: Boxin Wang, name: Weixin Chen, name: Hengzhi Pei, name: Chulin Xie, name: Mintong Kang, name: Chenhui Zhang, name: Chejian Xu, name: Zidi Xiong, name: Ritik Dutta, name: Rylan Schaeffer, name: Sang T. Truong, name: Simran Arora, name: Mantas Mazeika, name: Dan Hendrycks, name: Zinan Lin, name: Yu Cheng, name: Sanmi Koyejo, name: Dawn Song, name: Bo Li

Generative Pre-trained Transformer (GPT) models have exhibited exciting
progress in capabilities, capturing the interest of practitioners and the
public alike. Yet, while the literature on the trustworthiness of GPT models
remains limited, practitioners have proposed employing capable GPT models for
sensitive applications to healthcare and finance - where mistakes can be
costly. To this end, this work proposes a comprehensive trustworthiness
evaluation for large language models with a focus on GPT-4 and GPT-3.5,
considering diverse perspectives - including toxicity, stereotype bias,
adversarial robustness, out-of-distribution robustness, robustness on
adversarial demonstrations, privacy, machine ethics, and fairness. Based on our
evaluations, we discover previously unpublished vulnerabilities to
trustworthiness threats. For instance, we find that GPT models can be easily
misled to generate toxic and biased outputs and leak private information in
both training data and conversation history. We also find that although GPT-4
is usually more trustworthy than GPT-3.5 on standard benchmarks, GPT-4 is more
vulnerable given jailbreaking system or user prompts, potentially due to the
reason that GPT-4 follows the (misleading) instructions more precisely. Our
work illustrates a comprehensive trustworthiness evaluation of GPT models and
sheds light on the trustworthiness gaps. Our benchmark is publicly available at
https://decodingtrust.github.io/.