HELM (Holistic Evaluation of Language Models)

HELM, from Stanford, reframes evaluation as a matrix rather than a single leaderboard number. It runs many models across many scenarios (question answering, summarization, sentiment, and more) and, crucially, along many metrics at once — not just accuracy but calibration, robustness, fairness, bias, toxicity, and efficiency — so the trade-offs that a lone accuracy figure hides become visible side by side.

Its design principles are explicitness and coverage. Scenarios are organized by task, domain, and language; every model runs under a standardized prompting protocol so numbers are comparable; and each (model, scenario) cell is measured on the full metric suite where applicable. This taxonomy makes gaps auditable — you can see that a model strong on accuracy is poorly calibrated, or robust on clean inputs yet brittle under perturbation, rather than collapsing all of that into one rank.

Holistic breadth costs compute and curation, and the metric weights are ultimately value judgments — there is no canonical way to flatten seven axes into one ordering. HELM's lasting contribution is less a score than a discipline: report many scenarios and many metrics transparently, and let readers weigh the axes that matter for their use.