HELM (Holistic Evaluation of Language Models)

The HELM framework, established by the Stanford Center for Research on Foundation Models (CRFM), provides a comprehensive and transparent environment for evaluating Large Language Models. Unlike traditional benchmarks that focus on isolated capabilities, the HELM API and software suite evaluate models across a vast array of scenarios and metrics to ensure a holistic understanding of model behavior. This includes assessments of Accuracy, Calibration, Robustness, Fairness, Bias, Toxicity, and Efficiency.

By utilizing the HELM infrastructure, researchers can access a standardized interface to test models from major providers such as OpenAI, Google, Anthropic, and Meta. The project aims to improve Transparency in the field of Artificial Intelligence by documenting the performance of Foundation Models in a unified leaderboard. Detailed methodology and results are available through the Stanford HELM Project Page and the official GitHub Repository, which hosts the evaluation code and API integrations.

• LLM-Benchmarking
• Foundation-Models
• AI-Safety
• Model-Evaluation