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HELM

LLMs

HELM (Holistic Evaluation of Language Models) is Stanford's evaluation framework that organizes benchmarking around scenarios, adaptation and metrics, covering accuracy, robustness, fairness, toxicity and more.

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Editor's note

Suited to researchers and engineering teams needing multi-dimensional, reproducible LLM evaluation; not for users who only want a quick single-score leaderboard.

Decision facts

“Not verified” means evidence is insufficient, not that the capability is absent.

CategoryLLMs
Evidence statusVerification level not recorded
PlatformsNot verified
AvailabilityAvailable
Chinese UINot verified
Mainland ChinaNot verified
Commercial useNot verified

What is HELM

HELM (Holistic Evaluation of Language Models) is Stanford's evaluation framework that organizes benchmarking around scenarios, adaptation and metrics, covering accuracy, robustness, fairness, toxicity and more.

Key features of HELM

  • Running holistic multi-task, multi-metric evaluation of language models
  • Detecting fairness, bias and toxicity risks in models
  • Reproducing model-comparison experiments from papers
  • Defining custom scenarios and metrics for specific research needs

Good for

  • Clean, rigorous modular design around scenarios, adaptation and metrics
  • Broad metric coverage: accuracy, calibration, robustness, fairness, toxicity and efficiency
  • Open-source code with standardized pipelines for reproducible results

Watch out

  • Evaluation mainly covers English, with limited support for other languages
  • Configuration and execution require technical expertise
  • Full evaluation runs are computationally expensive

How to use HELM

  1. Install via pip install helm or from the GitHub source
  2. Create a YAML config defining scenarios, adaptation and metrics
  3. Run evaluation with helm run specifying config and model
  4. Inspect the generated report to analyze per-metric results
  5. Extend Scenario or Metric classes for customization when needed

Who HELM is for

Difficulty: Intermediate

  • Running holistic multi-task, multi-metric evaluation of language models
  • Detecting fairness, bias and toxicity risks in models
  • Reproducing model-comparison experiments from papers
  • Defining custom scenarios and metrics for specific research needs

FAQ

How is a HELM evaluation organized?

Each run specifies a scenario, an adaptation prompt for the model, and one or more metrics, forming the three modules of scenario, adaptation and metrics.

Does HELM support multimodal tasks?

Yes. Beyond text-only tasks, HELM supports multimodal tasks such as image captioning and visual question answering.

Are HELM results reproducible?

Yes. Standardized pipelines and configuration files ensure consistent results under the same conditions, and the evaluation code is open for inspection and modification.

Sources and verification

Evidence status: Verification level not recorded

Sources: crfm.stanford.edu (opens in a new tab)
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