Holistic Evaluation of Language Models (HELM)

• Holistic Evaluation of Language Models (HELM) is a comprehensive framework that assesses models using a two-dimensional taxonomy of diverse scenarios and metrics.
• It systematically measures performance aspects like accuracy, calibration, robustness, fairness, toxicity, and efficiency across real-world tasks.
• HELM’s standardized approach fosters transparency and reproducibility, enabling direct comparisons and exposing underexplored evaluation gaps.

LLMs underpin major advancements in natural language processing systems, yet their complex and often opaque behaviors pose challenges for comprehensive evaluation. Holistic Evaluation of LLMs (HELM) is an evaluation paradigm designed to systematically assess LLMs across a broad spectrum of scenarios and desiderata, emphasizing transparency, reproducibility, and the illumination of trade-offs between critical qualities. Rather than focusing exclusively on any single performance metric—such as accuracy—HELM stipulates a top-down multi-metric approach, explicitly defining and measuring a wide array of capabilities as manifested in real and diverse use cases (2211.09110).

1. Taxonomy of Scenarios and Metrics

HELM is grounded in a two-dimensional taxonomy that makes explicit both the range of use cases relevant to LLMs (scenarios) and the metrics that capture desiderata beyond mere correctness:

• Scenarios: Encompass fundamental language tasks such as translation, summarization, and question answering, with explicit attention paid to coverage of underrepresented cases (e.g., neglected dialects or emerging applications).
• Metrics: Extend far beyond accuracy to include calibration (the alignment of predicted probabilities with actual correctness), robustness (performance under distributional shifts), fairness, bias, toxicity, and efficiency.

A key contribution is the top–down articulation of what is measured—a strategy intended to surface what is missing or underrepresented in model evaluation. The taxonomy is visually summarized in HELM’s figure “fig_taxonomy_v1” and is explicitly incorporated into its modular evaluation toolkit.

2. Multi-Metric Evaluation Approach

Within each scenario, HELM evaluates models using seven primary metrics:

• Accuracy: Fraction of correct model responses,

$$\text{Accuracy} = \frac{\text{Number of correct responses}}{\text{Total cases}}$$

• Calibration: Measures how closely a model’s predicted probabilities match actual outcome frequencies—essential for trust in model uncertainty quantification.
• Robustness: Assesses performance stability under input perturbations or distributional shifts.
• Fairness and Bias: Quantifies disparities, particularly around demographic or social subgroups.
• Toxicity: Evaluates a model’s propensity for harmful or unsafe output.
• Efficiency: Captures resource requirements, especially for real-time or large-scale deployment.

By subjecting each scenario to all applicable metrics (feasible for 87.5% of core scenarios in the initial HELM release), the methodology ensures that critical concerns—such as responsible behavior, reliability, and resource use—are foregrounded. These metrics are visualized together (see HELM’s “fig_multi_metric”) with many metrics scored for every use case, exposing trade-offs and failures not visible in single-metric benchmarks.

3. Targeted and Large-Scale Evaluation Protocol

Beyond core scenarios, HELM includes targeted evaluations that probe specific aspects, such as reasoning ability, disinformation susceptibility, or model robustness to adversarial inputs. Each evaluation run in HELM is formally specified by its “evaluation primitive”—a tuple of (scenario, adaptation procedure, metrics), ensuring clarity in what is being measured, how the model is prepared, and which metric(s) matter in the given context.

A distinctive feature is HELM’s large-scale, standardized benchmarking campaign—evaluating 30 prominent LLMs (open, limited-access, and closed-release) on all 42 scenarios, 21 of which were previously absent from mainstream benchmarks. Prior to HELM, models were, on average, evaluated on only 17.9% of HELM’s core scenarios, with little overlap across studies. HELM closes this gap, achieving 96.0% uniform scenario coverage across models. The resulting dataset facilitates direct, “apples-to-apples” comparisons and exposes critical untested areas.

4. Major Empirical Findings

HELM’s coordinated evaluation surfaced 25 top-level findings, many of which shape both the research understanding and practical deployment of LLMs:

• Trade-offs: Many models perform well in accuracy at the cost of calibration, robustness, or efficiency. For example, high-performing models may remain poorly calibrated or display fairness issues in minority scenarios.
• Calibration Shortcomings: Widespread over- or under-confidence reveals incomplete uncertainty quantification, with practical consequences for risk-sensitive domains.
• Fairness and Bias Patterns: Some models achieve partial mitigations of bias or fairness concerns in certain contexts, but disparities often remain or shift depending on the scenario.
• Toxicity and Safety: HELM’s evaluations reveal nontrivial risks around toxic outputs in both mainstream and edge scenarios, reaffirming the need for continual, scenario-specific safety checks.
• Resource–Performance Balance: The tension between efficiency and model performance is explicitly measurable, informing decisions in resource-constrained or cost-sensitive applications.

These findings are intended as a high-level portrait of the state of the art, helping to direct future research toward well-identified weaknesses or gaps.

5. Transparency, Toolkit, and Community Engagement

A central tenet of HELM is reproducibility and transparency. The framework provides:

• Explicit evaluation taxonomy and documentation: Releasing the precise definitions of each scenario, adaptation, and metric.
• Public release of raw inputs and model outputs: All model prompts and completions are published, inviting further community analysis.
• Modular, open-source toolkit: The infrastructure enables researchers and practitioners to run, extend, and adapt HELM evaluations to new scenarios or metrics with minimal friction.

This openness is designed to ensure that results can be scrutinized, extended, and improved as rapidly as the field evolves.

6. Implications, Limitations, and Future Development

HELM’s holistic approach redefines best practice in LLM evaluation by centering multidimensional, scenario-driven assessment rather than singular leaderboards or narrow benchmarks. The clarity over what is (and is not) being measured exposes new research frontiers, such as neglected dialects, underexplored safety metrics, and new efficiency trade-offs.

However, evolving threats such as test set leakage, overfitting through public benchmark exposure, or unintended metric gaming indicate caveats. Subsequent analyses have highlighted the need to couple open benchmarks with private or dynamic test sets to preserve benchmarking integrity, as high leaderboard performance on public datasets may not always translate to real-world effectiveness (2507.00460).

HELM is conceived as a living benchmark—a continually updated resource that adapts to new scenarios, models, and desiderata as the field advances. Its methodology and openness provide a template for further specialty benchmarks, domain-specific extensions, and broader holistic assessments serving the needs of both academic research and the deployment of reliable language technologies.