HELM benchmark

HELM (Liang et al., 2023) emphasises holistic evaluation: a single model is scored across many metrics on many scenarios, with the resulting matrix surfaced as the principal output. This contrasts with single-metric leaderboards and aligns with the multi-property framing of trustworthy AI.

What HELM actually measures

HELM was developed by Stanford’s Center for Research on Foundation Models (CRFM) as an open-source Python framework, not a single fixed test. In its original 2022 release, HELM evaluated models across 16 core scenarios (drawn from tasks such as question answering, summarisation, and text classification) and, where applicable, seven metrics per scenario: accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency. A further 26 targeted-evaluation scenarios probed specific capabilities, including linguistic understanding, world and commonsense knowledge, reasoning, memorisation and copyright risk, disinformation generation, and toxicity generation. The framework has since expanded into several task-specific leaderboards published on the CRFM site (for example HELM Lite, HELM Instruct, and HELM Safety), each running the same underlying multi-metric methodology against a narrower or updated scenario set.

Why “holistic” is the operative distinction

Most earlier LLM leaderboards reported a single headline accuracy figure, which lets a model that is accurate but poorly calibrated, non-robust to input perturbation, or prone to biased or toxic output appear to outperform a model that is more balanced across those properties. HELM’s design deliberately surfaces the full metric-by-scenario matrix rather than collapsing it to one number, so a model’s weaknesses on calibration, robustness, or fairness remain visible alongside its accuracy score.

Known limitations and contamination risk

• Data contamination: because scenario datasets and their answers are often publicly available, a model’s pretraining corpus may overlap with HELM’s evaluation data. The original HELM paper’s own authors state they have only limited visibility into how contaminated any given model is, and disclose what evidence they do have rather than claiming the problem is solved.
• Coverage is scenario-bound: HELM’s score reflects performance on the specific scenarios and prompts it runs. A high HELM score does not guarantee equivalent performance on a deployment-specific task that differs materially from those scenarios.
• Cost and reproducibility trade-off: a full HELM run across many models and scenarios is computationally expensive (the original evaluation involved tens of thousands of GPU hours and millions of API queries), which is part of why narrower derivative leaderboards such as HELM Lite exist alongside the full framework.
• Rapid model turnover: because new model versions are released frequently, a HELM leaderboard snapshot reflects the models evaluated as of that run and can go stale quickly relative to the current commercial model landscape.

Why a research-administration audience encounters HELM

Research offices, library/IT procurement teams, and units evaluating AI writing or research-assistant tools increasingly need a defensible basis for comparing vendor claims about model capability. HELM’s multi-metric, published-methodology design gives procurement and research-integrity reviewers a citable, third-party reference point rather than relying solely on a vendor’s own benchmark disclosures, and its explicit acknowledgement of contamination risk is itself useful context when a vendor cites a single headline accuracy figure without qualification. HELM scores are also sometimes cited in grant proposals or methods sections that rely on a specific LLM, as evidence of the model’s documented strengths and limitations for reproducibility purposes.

Frequently asked questions

Is HELM the same as MMLU?

No. MMLU is a single multiple-choice knowledge benchmark that produces one accuracy figure across academic subjects. HELM is a broader evaluation framework that can incorporate many scenarios, of which an MMLU-style task may be one component, scored across multiple properties rather than accuracy alone.

Who publishes and maintains HELM?

Stanford’s Center for Research on Foundation Models (CRFM) publishes and maintains HELM as an open-source framework and a set of public leaderboards, with results and methodology documented on the CRFM site and in the original 2022/2023 paper.

Can an institution run HELM against its own model or dataset?

Yes; HELM is released as open-source code, so an institution with the compute budget can run the framework against a custom scenario or an internally deployed model, rather than relying only on the published public leaderboard results.

References

• Liang et al., ‘Holistic Evaluation of Language Models’ (Transactions on Machine Learning Research, 2023; arXiv:2211.09110).
• Stanford CRFM, HELM project documentation and leaderboards, crfm.stanford.edu/helm.