II · THE IDEA · ARTIFICIAL INTELLIGENCE
Holistic Evaluation of Language Models (HELM)
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A model can top one leaderboard and still fail elsewhere. HELM asks what else was measured, under which conditions, and what was left out of the score.
At a glance
- What it is
- A framework and living benchmark for evaluating language models holistically
- Core idea
- Many scenarios, many metrics, and explicit attention to risks
- Not only
- A single accuracy number on a fixed test set
- Stance
- Standardise what is measured and surface what is missing
Think of school reports. One child gets a single overall mark; another gets separate marks for reading, maths, conduct, attendance and how they treat classmates. The second report is harder to brag about in one sentence, but it is far clearer when you must choose a team or spot a problem.
HELM tries to do the second kind of report for language models. Instead of one accuracy number, it evaluates models across many scenarios — different jobs you might actually ask a model to do — and scores each with several metrics, including ones tied to risk and cost, not only correctness. The point is not to crown a permanent winner. It is to show a profile: where a model is strong, where it is weak, and what nobody measured yet.
That profile matters because two models with the same headline score can fail in very different ways. One might be accurate but overconfident. Another might be fine on clean test questions and fragile when the wording shifts. A single leaderboard hides those differences. HELM is built to keep them in sight, and to keep adding scenarios as the ways people use models change.
HELM (Holistic Evaluation of Language Models) is a framework and living benchmark that evaluates language models across a broad inventory of scenarios with multi-metric reporting and explicit risk-related measurement, rather than collapsing quality into a single accuracy aggregate on a narrow task suite.
Core mechanics of the framing: 1. Scenarios specify what is being evaluated — task distributions, document types, and use-conditions under a documented protocol (prompting, adaptation method, decoding assumptions). A score is always conditional on that scenario definition. 2. Metrics are multi-dimensional per scenario. Correctness sits alongside other axes such as calibration, robustness, fairness-related measures, efficiency and risk-related behaviours, so Pareto trade-offs remain visible instead of being flattened into one ranking. 3. Standardisation targets the evaluation recipe itself: shared scenario and metric implementations reduce confounds from ad hoc scripts, preprocessing differences and incompatible prompting setups when models are compared. 4. Living benchmark practice means the suite is intended to be extended and re-run as models, prompting norms and socially important failure modes change, rather than frozen as a permanent leaderboard.
Limitations a working evaluator should not paper over: scenario coverage is still a finite sample of deployment space; metric choice encodes normative judgements; contamination and prompt sensitivity can still distort results; and risk metrics are only as good as their operationalisations. HELM improves legibility and breadth of comparison; it does not certify safety or general competence outside the measured surface.
Look closer
Scenarios before slogans
HELM organises evaluation around scenarios: concrete use-settings in which a model is asked to do something specific. The point is not a single abstract notion of language ability, but a spread of tasks and conditions that look more like the ways people actually deploy models. A result is always a result on something, under some protocol, and the something is meant to be visible.
Metrics in the plural
Accuracy is only one axis. The framework insists on multi-metric reporting so that trade-offs stay in view: a model that gains on one measure can lose on another, and collapsing everything into a single ranking hides that structure. Calibration, robustness, fairness-related measures, efficiency and risk-related behaviours sit beside correctness rather than underneath it.
A benchmark that does not pretend to finish
Calling HELM a living benchmark is not empty phrasing in the paper's framing. Models change, prompting practice changes, and the set of socially important failure modes changes. The evaluation surface is designed to be extended and re-run, so that comparisons remain possible without freezing the field's questions at one moment's leaderboard.
The story
For years, language-model evaluation often meant a small set of benchmarks and a headline number. That habit is understandable: shared test sets make comparison easy, and a single score travels well on a talk slide. It is also incomplete. Two models with similar aggregate accuracy can differ sharply in how they fail, how sensitive they are to small input changes, how unevenly they behave across groups or dialects, how costly they are to run, and how readily they produce harmful text. If only one number is published, those differences disappear from the public record.
HELM — Holistic Evaluation of Language Models — is a response to that narrowing. It treats evaluation as a structured map rather than a race. The map has at least three layers that matter together. First, scenarios: the situations in which a model is evaluated, chosen to reflect a range of uses rather than a single academic task family. Second, metrics: multiple measurements per scenario, so that better is always better-at-something, not a silent composite. Third, risks: behaviours and harms that matter in deployment even when they do not show up as ordinary task error.
The practical effect of that design is to make incompleteness visible. No evaluation covers every use or every harm. A holistic framing does not claim to. It claims something more modest and more useful: that what you chose to measure, and what you did not, should be easy to see, and that results should be reported in a form that allows trade-offs to remain legible. A model that is strong on accuracy and weak on calibration is not good or bad in the abstract; it is a particular profile. HELM is built to expose profiles.
Standardisation is the other half of the idea. Raw capability demos and one-off evaluation scripts are hard to compare because small differences in prompting, decoding, preprocessing or metric code can move the numbers. HELM pushes toward shared scenario definitions and shared metric implementations so that differences between models are less confounded by differences between evaluation recipes. The aim is not to declare a permanent ranking of intelligence, but to make multi-dimensional comparison repeatable enough to be useful as the model landscape shifts.
That is why the living part matters. A fixed suite ages quickly: new model behaviours appear, new deployment contexts matter, and old test items leak into training data. HELM is framed as a continuing process of specifying scenarios, running models under documented conditions, publishing multi-metric results, and extending the map where important blank regions remain. The benchmark is less a trophy case than a public workbook — one that tries to keep the field honest about breadth, cost and risk, not only about the next point gain on a familiar accuracy curve.
Why it mattered then
When large language models began to be compared in public at scale, the easiest story was a leaderboard. HELM arrived as a corrective to that compression. It mattered in its own moment because it gave researchers, practitioners and institutions a shared vocabulary for saying that evaluation had been too narrow, and a concrete structure — scenarios, metrics, risks — for widening it without abandoning comparability. Instead of only asking which model won, it made it normal to ask under which conditions, on which axes, and with which harms still unmeasured.
Why it matters now
Models are still compared by headline scores, and deployment decisions still often rest on a thin slice of behaviour. HELM remains relevant because the pressure toward a single number has not gone away; if anything it has intensified as products ship faster. A multi-metric, multi-scenario frame is still one of the clearer ways to keep trade-offs and blind spots in view when a model is strong in a demo and uncertain everywhere else. It also remains a reminder that evaluation is part of the research surface, not a final exam administered once.
The surprising detail
The most distinctive move is not a clever new task. It is the refusal to let taxonomy do the quiet work of erasure. By insisting that scenarios, metrics and risks be enumerated side by side, HELM makes the absence of a measurement into a visible fact about an evaluation, rather than an invisible default. The benchmark's ambition is as much about what it forces into the open as about any one score it produces.
What is disputed
HELM standardises a broad evaluation surface, but no suite can exhaust every deployment context or harm. Which scenarios and risk metrics are in or out remains a judgement that evolves with use, and scores are only as meaningful as the protocols and datasets behind them. Treat the framework as structured breadth, not as a complete moral or technical certificate.
Remember this
HELM is a map of scenarios, metrics and risks — built so trade-offs and missing measurements stay visible, not a single race to one accuracy number.
Test yourself
A lab reports that Model A beats Model B by two points on a familiar accuracy benchmark and calls A strictly better. Using HELM's framing, what two kinds of information would you demand before accepting that conclusion, and why might A still be the worse choice for a real deployment?
First, multi-metric results on the same scenarios: calibration, robustness, efficiency, fairness-related measures and risk behaviours can move in the opposite direction from accuracy, so a two-point gain may hide a costly regression elsewhere. Second, coverage across scenarios rather than a single task family: strength on one academic benchmark need not transfer to the use-settings that matter in deployment. A could still be the worse choice if it is less calibrated, more brittle under small input changes, more expensive to serve, or more prone to harmful outputs in the scenarios that resemble the actual product — precisely the profile differences a one-number comparison erases.
Go deeper
- [2211.09110] Holistic Evaluation of Language Models · arxiv.org
- [2303.06388] Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast · arxiv.org
Image: Original diagram, The Daily Triptych. Licence: Original work. Source.