DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

DecodingTrust is the Adversarial GLUE Benchmark. DecodingTrust aims at providing a thorough assessment of trustworthiness in GPT models.

This research endeavor is designed to help researchers and practitioners better understand the capabilities, limitations, and potential risks involved in deploying these state-of-the-art Large Language Models (LLMs).
This project is organized around the following eight primary perspectives of trustworthiness, including:

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

Paper: https://arxiv.org/abs/2306.11698
Repo: https://github.com/AI-secure/DecodingTrust