Introduction
HELM (Holistic Evaluation of Language Models) is an open-source evaluation framework from Stanford CRFM designed for comprehensive, reproducible, and transparent evaluation of foundation and multimodal models. It provides standardized datasets, benchmarks, and multi-dimensional metrics, along with leaderboards and visualization tools.
Key Features
- Standardized datasets and benchmarks such as MMLU-Pro, GPQA, and IFEval.
- Multi-dimensional metrics covering accuracy, efficiency, bias, and safety.
- Web UI and leaderboards for inspecting individual prompts and comparing models.
- Reproducible pipelines and tooling to run, summarize, and share evaluation suites.
Use Cases
- Research: reproduce published benchmark results and compare model behavior across dimensions.
- Engineering benchmarks: perform comprehensive evaluation and safety checks before releases.
- Diagnostics & visualization: analyze sample-level outputs to debug and improve models.
Technical Highlights
- Modular architecture for plugging new tasks and integrating external model providers.
- CLI and Python API for scripted and large-scale evaluations.
- Active maintenance, detailed documentation, and citation guidance for academic use.