← AI Terminology
HELM - Holistic Evaluation of Language Models
HELM is a living evaluation framework from Stanford CRFM that measures LLMs across many scenarios and metrics (accuracy, calibration, robustness, fairness, bias, toxicity, efficiency).
It emphasises multi-metric, transparent reporting.
It emphasises multi-metric, transparent reporting.
Why It Matters in AI
Accuracy-only leaderboards hide harms and brittleness. HELM popularised holistic scorecards for foundation models — important for governance, procurement, and responsible release.
Key Points
| Aspect | Description |
|---|---|
| Org | Stanford CRFM |
| Use | Compare models beyond one number |
| Idea | Many scenarios × many metrics |
| Related | HELM lite/modifications; other scorecards |
| Audience | Researchers, policymakers, enterprises |
| Transparency | Standardised prompts, versioned results |
Simple Analogy
A full medical checkup with bloodwork, stress test, and lifestyle review — not only weighing yourself on a scale.
Common Usage Examples
- Read HELM model scorecards
- Adopt multi-metric internal evals
- Track toxicity + accuracy together
- Cite HELM scenarios in model cards
Summary
In short: HELM holistically evaluates LLMs across accuracy, robustness, fairness, and more — multi-metric scorecards instead of single leaderboards.