HELM (Holistic Evaluation of Language Models)
HELM standardizes evaluation across many scenarios and reports multiple metrics such as accuracy, robustness, calibration, fairness, and efficiency, giving a holistic model profile rather than a single score. Its breadth aids comparability but makes it costly and hard to summarize.
HELM (Holistic Evaluation of Language Models), from Stanford, is a framework and living leaderboard that evaluates language models broadly and transparently. Its premise is that a single accuracy number is insufficient: models should be measured across many scenarios and along several dimensions at once.
What It Measures
HELM evaluates models across a large, structured set of scenarios spanning question answering, summarization, reasoning, sentiment, toxicity, information retrieval, and more. Crucially, for each scenario it reports multiple metrics rather than just accuracy: robustness to perturbations, calibration, fairness across groups, bias, toxicity, and efficiency such as inference cost and latency.
The goal is a holistic, multi-metric profile of a model so trade-offs become visible, for example a model that is accurate but poorly calibrated or biased.
Methodology
HELM standardizes prompting, decoding, and metric computation so that all models are run under the same conditions, which is its main contribution to comparability. It defines scenarios as task plus dataset plus metrics, and runs every model on the same matrix.
The project is maintained as an open, versioned leaderboard with specialized editions such as HELM Lite, HELM Classic, and domain-specific variants like medical or safety. Results, prompts, and raw model outputs are published for transparency and reproducibility.
How to Interpret Results
Do not reduce HELM to one number; its value is the multi-metric view. Read across columns to understand trade-offs, and pick the metrics that match your use case, prioritizing calibration and fairness for high-stakes deployments or efficiency for cost-sensitive ones.
Because HELM fixes the evaluation protocol, its cross-model comparisons are more apples-to-apples than scores scraped from disparate papers. Use the specific edition relevant to your domain, and note the version since scenarios evolve.
Limitations
The breadth that makes HELM valuable also makes it expensive to run and complex to summarize, and not every model is evaluated on every scenario. Standardized prompting may underrepresent a model that benefits from bespoke prompting. Some metrics, such as fairness and toxicity, are inherently contested and depend on definitions. As with any public suite, included datasets carry contamination risk over time.
Practical Use
Use HELM when you need comparable, multi-metric results under a standardized protocol rather than scores scraped from inconsistent papers. Choose the edition that matches your domain and the metrics that match your risk profile, prioritizing calibration and fairness for high-stakes uses or efficiency for cost-sensitive ones. Read across the metric columns to see trade-offs, and note the version, since scenarios evolve. Do not collapse HELM into one number, which discards the holistic view that is its entire purpose.