HowieHwong/TrustLLM
📊 DatasetHowieHwong
A comprehensive benchmark and framework for evaluating the trustworthiness of Large Language Models.
TrustLLM serves as a foundational benchmark for assessing the trustworthiness of Large Language Models across multiple dimensions. As LLMs become increasingly integrated into critical infrastructure, ensuring their reliability is paramount. This project defines a comprehensive taxonomy of trust, evaluating models on truthfulness, safety, fairness, robustness, privacy, machine ethics, transparency, and accountability.
The framework includes a large-scale dataset and a standardized evaluation pipeline that allows users to test various LLMs against these metrics. By providing a rigorous methodology, TrustLLM helps identify vulnerabilities in model behavior, such as hallucinations, bias, or susceptibility to adversarial attacks. It is designed to be extensible, allowing the community to contribute new evaluation tasks and datasets as the field of AI safety evolves. The project is highly relevant for developers aiming to deploy production-grade AI that adheres to safety standards and ethical guidelines.
💡Highlights
- ├─Evaluates 8 dimensions of trust
- ├─ICML 2024 benchmark framework
- └─Standardized safety assessment
🎯For
- ├─AI Safety Researchers
- ├─LLM Developers
- └─Ethical AI Auditors