stanford-crfm/helm

Holistic Evaluation of Language Models (HELM) is an open source Python framework created by the Center for Research on Foundation Models (CRFM) at Stanford for holistic, reproducible and transparent evaluation of foundation models, including large language models (LLMs) and multimodal models.

What it solves

HELM provides a standardized, transparent, and reproducible way to evaluate foundation models. It addresses the problem of inconsistent evaluation across different models and benchmarks, allowing for a holistic view of model performance across multiple dimensions including accuracy, bias, toxicity, and efficiency.

How it works

HELM is a Python framework that integrates several components to create a comprehensive evaluation pipeline:

  • Unified Interface: It provides a unified way to access models from various providers (such as OpenAI, Anthropic, and Google).
  • Standardized Benchmarks: It includes a wide array of datasets and benchmarks (e.g., MMLU-Pro, GPQA, IFEval) in a standardized format.
  • Multi-dimensional Metrics: It measures performance using metrics that go beyond simple accuracy, including efficiency, bias, and toxicity.
  • Visualization Tools: It includes a Web UI for inspecting individual prompts and responses, and a web leaderboard for comparing results across models.

Who it’s for

Researchers and developers who need to conduct reproducible and transparent evaluations of LLMs and multimodal models, as well as those looking to compare model performance across standardized benchmarks.

Highlights

  • Holistic Approach: Evaluates models across multiple metrics (accuracy, bias, toxicity, efficiency).
  • Broad Model Support: Unified interface for multiple major AI providers.
  • Extensive Benchmark Library: Supports a variety of domains including medicine, finance, and vision-language tasks.
  • Integrated Visualization: Built-in web server and leaderboards for result analysis.

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