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HELM

Holistic Evaluation of Language Models (HELM) from Stanford CRFM: an open framework for reproducible, transparent model evaluation and benchmark management.

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.

Comments

HELM
Resource Info
Author Stanford CRFM
Added Date 2025-10-02
Open Source Since 2021-11-29
Tags
Evaluation Open Source