{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"helm","slug":"helm","name":"HELM","type":"benchmark","url":"https://crfm.stanford.edu/helm","page_url":"https://unfragile.ai/helm","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"helm__cap_0","uri":"capability://data.processing.analysis.multi.scenario.language.model.evaluation.framework","name":"multi-scenario language model evaluation framework","description":"Evaluates language models across 42 diverse scenarios (QA, summarization, toxicity detection, machine translation, etc.) using a unified evaluation harness that standardizes prompt formatting, response collection, and metric computation. The framework abstracts away model-specific API differences through a provider-agnostic interface, allowing fair comparison across proprietary (GPT-4, Claude) and open-source models (Llama, Mistral) by normalizing input/output handling and sampling strategies.","intents":["Compare performance of multiple LLMs across diverse real-world tasks without building custom evaluation pipelines for each model","Understand how a single model performs across different domains and task types to identify capability gaps","Benchmark new models against established baselines using standardized, reproducible evaluation methodology"],"best_for":["AI researchers evaluating model releases and comparing architectural choices","ML engineers selecting models for production deployment across multiple use cases","Model developers iterating on training and fine-tuning with quantified performance feedback"],"limitations":["Scenario coverage is fixed at 42 — custom domain-specific scenarios require forking the codebase or external wrapper","Evaluation is snapshot-based; no continuous monitoring of model drift or performance degradation over time","Scenario selection may not reflect your specific production distribution — results are indicative but not prescriptive for your use case"],"requires":["Python 3.8+","API credentials for models being evaluated (OpenAI, Anthropic, etc.) or local model serving infrastructure","Sufficient compute for running inference across 42 scenarios × multiple model variants"],"input_types":["model identifiers (e.g., 'gpt-4', 'claude-2', 'meta/llama-2-70b')","scenario configuration files (YAML/JSON specifying task, dataset, metrics)"],"output_types":["structured evaluation results (JSON/CSV with per-scenario metrics)","aggregated leaderboards and comparison tables","detailed error analysis and failure case logs"],"categories":["data-processing-analysis","benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helm__cap_1","uri":"capability://data.processing.analysis.calibration.and.confidence.measurement.across.model.outputs","name":"calibration and confidence measurement across model outputs","description":"Measures whether a model's confidence estimates align with actual correctness by computing calibration metrics (expected calibration error, Brier score) across predictions. Compares the model's self-reported confidence (via logit analysis or explicit confidence tokens) against ground-truth accuracy to identify overconfident or underconfident models, which is critical for production systems where miscalibrated confidence can lead to poor downstream decisions.","intents":["Detect if a model is overconfident in wrong answers, which could cause cascading failures in retrieval-augmented generation or multi-step reasoning pipelines","Quantify whether a model's uncertainty estimates are reliable enough for selective prediction (e.g., routing low-confidence queries to human review)","Compare calibration across model families to identify which models are safest for high-stakes applications"],"best_for":["Teams deploying models in high-stakes domains (healthcare, finance, legal) where miscalibrated confidence is costly","Builders of agentic systems that need to know when to defer to human judgment or alternative strategies","Researchers studying model behavior and failure modes beyond accuracy"],"limitations":["Calibration measurement requires ground-truth labels for all test instances — cannot be computed on open-ended generation tasks without human annotation","Different models expose confidence differently (some via logits, some via explicit tokens); normalization across model types may introduce systematic bias","Calibration is task-specific — a well-calibrated model on one scenario may be poorly calibrated on another"],"requires":["Ground-truth labels for evaluation dataset","Model outputs with confidence scores or logit access","Python 3.8+ with scikit-learn or equivalent for metric computation"],"input_types":["model predictions with confidence scores","ground-truth labels","prediction probabilities or logits"],"output_types":["calibration curves (predicted vs actual accuracy)","expected calibration error (ECE) scores","Brier scores and other calibration metrics","confidence distribution histograms"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helm__cap_10","uri":"capability://data.processing.analysis.interactive.results.visualization.and.exploration.dashboard","name":"interactive results visualization and exploration dashboard","description":"Provides web-based interactive dashboards for exploring evaluation results, including scenario-level performance tables, metric comparison charts, demographic breakdowns, and robustness analysis. Users can filter by model, scenario, metric, or demographic group; drill down from aggregate metrics to individual predictions; and export results in multiple formats (CSV, JSON, HTML). Dashboards are generated automatically from evaluation results and hosted on the HELM website for public access.","intents":["Explore evaluation results interactively to understand model strengths and weaknesses across scenarios","Compare models side-by-side on specific metrics or scenarios of interest","Drill down from aggregate metrics to individual predictions to understand failure modes","Share results with stakeholders via interactive dashboards rather than static reports"],"best_for":["Model selection committees exploring results to inform purchasing decisions","Researchers analyzing evaluation results to identify patterns and insights","Teams sharing benchmark results with non-technical stakeholders via interactive dashboards"],"limitations":["Dashboard interactivity is limited to pre-computed results; does not support real-time model evaluation or custom analysis","Large result sets (many models × many scenarios) can be slow to load and navigate","Dashboard design is fixed; users cannot customize visualizations or metrics displayed","Results are read-only; users cannot modify or re-run evaluations through the dashboard"],"requires":["Evaluation results in HELM format (JSON)","Web hosting infrastructure for dashboard","JavaScript/React knowledge for customizing dashboard (optional)"],"input_types":["evaluation results (JSON)","model metadata","scenario metadata"],"output_types":["interactive HTML dashboards","downloadable result files (CSV, JSON)","static visualizations (PNG, SVG)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helm__cap_11","uri":"capability://automation.workflow.reproducible.evaluation.with.version.control.and.result.archiving","name":"reproducible evaluation with version control and result archiving","description":"Ensures reproducibility by versioning scenario definitions, prompt templates, and evaluation code; archiving evaluation results with metadata (model version, evaluation date, hardware configuration); and enabling result replication by re-running evaluations with the same code and data. Evaluation runs are tagged with unique identifiers and stored in a results database, enabling tracking of model performance over time and comparison of results across different evaluation runs.","intents":["Reproduce published results by re-running evaluations with the same scenario definitions and prompt templates","Track model performance over time as new versions are released to measure progress","Compare results across different evaluation runs to understand variability and identify significant changes","Archive evaluation results for compliance and audit purposes"],"best_for":["Researchers publishing benchmark results who need to ensure reproducibility","Teams tracking model performance over time to measure progress and detect regressions","Organizations with compliance requirements for result archiving and audit trails"],"limitations":["Reproducibility depends on consistent evaluation infrastructure (same hardware, software versions); infrastructure changes can affect results","Scenario versioning is manual; no automated detection of scenario changes or incompatibilities","Result archiving adds storage overhead; large-scale evaluations can generate gigabytes of result data","Reproducibility does not guarantee correctness; reproduced results may still be incorrect if the original evaluation was flawed"],"requires":["Version control system (Git) for scenario and code versioning","Results database or file storage for archiving evaluation results","Metadata tracking (model version, evaluation date, hardware configuration)"],"input_types":["scenario definitions (versioned)","evaluation code (versioned)","evaluation metadata"],"output_types":["versioned scenario definitions","archived evaluation results with metadata","result comparison reports","performance tracking dashboards"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helm__cap_2","uri":"capability://data.processing.analysis.robustness.evaluation.via.adversarial.and.distribution.shifted.inputs","name":"robustness evaluation via adversarial and distribution-shifted inputs","description":"Tests model performance under distribution shift and adversarial perturbations by evaluating on perturbed versions of standard test sets (e.g., typos, paraphrases, out-of-distribution examples). Measures robustness as the performance delta between clean and perturbed inputs, identifying models that degrade gracefully vs. catastrophically under realistic noise and adversarial conditions.","intents":["Identify which models are brittle to typos, grammatical errors, or stylistic variations that occur in real user input","Compare robustness across models to select ones suitable for noisy, uncontrolled input environments","Understand failure modes when models encounter out-of-distribution examples or adversarial inputs"],"best_for":["Teams building production systems that must handle messy, user-generated input (chatbots, search, content moderation)","Researchers studying model generalization and adversarial robustness","Model developers optimizing for real-world deployment rather than benchmark gaming"],"limitations":["Perturbation strategies are predefined (typos, paraphrases, etc.) — may not reflect your specific distribution shift","Adversarial robustness evaluation is computationally expensive; full evaluation across all perturbations can require 10x+ inference calls","Robustness is scenario-specific — a model robust to typos may be fragile to semantic paraphrases"],"requires":["Original test set with ground-truth labels","Perturbation generation tools (built-in or external)","Sufficient compute budget for 5-10x inference multiplier"],"input_types":["clean test examples","perturbation specifications (typo rate, paraphrase strategy, etc.)"],"output_types":["per-scenario robustness scores (accuracy on perturbed inputs)","robustness degradation curves (accuracy vs. perturbation intensity)","failure case analysis (which perturbations cause largest accuracy drops)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helm__cap_3","uri":"capability://data.processing.analysis.fairness.and.bias.measurement.across.demographic.groups","name":"fairness and bias measurement across demographic groups","description":"Evaluates model performance disparities across demographic groups (gender, race, age, etc.) by partitioning test sets by demographic attributes and computing per-group accuracy, precision, and recall. Identifies models with significant performance gaps between groups, which indicates potential bias in training data or model behavior that could cause discriminatory outcomes in production.","intents":["Detect if a model performs significantly worse for certain demographic groups, which could cause discriminatory harms in deployment","Compare fairness profiles across models to select ones with more equitable performance","Quantify fairness-accuracy tradeoffs (e.g., does a more accurate model have worse fairness properties?)"],"best_for":["Teams deploying models in regulated domains (hiring, lending, criminal justice) where fairness is legally required","Responsible AI practitioners building equitable systems","Researchers studying bias in language models and mitigation strategies"],"limitations":["Fairness measurement requires demographic labels in test data — often unavailable or sensitive to collect","Demographic categories are reductive and may not capture relevant identity dimensions for your use case","Fairness metrics are context-dependent; a 5% performance gap may be acceptable in some domains but unacceptable in others","Intersectionality is not fully captured — fairness is measured per-demographic, not across intersecting identities"],"requires":["Test set with demographic labels for each example","Ground-truth labels for computing per-group metrics","Ethical review and stakeholder input on fairness definitions"],"input_types":["model predictions","ground-truth labels","demographic attributes (gender, race, age, etc.)"],"output_types":["per-group accuracy, precision, recall tables","fairness gap metrics (max performance difference across groups)","demographic parity and equalized odds measures","fairness-accuracy tradeoff curves"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helm__cap_4","uri":"capability://safety.moderation.toxicity.and.harmful.content.detection.in.model.outputs","name":"toxicity and harmful content detection in model outputs","description":"Evaluates whether model outputs contain toxic, hateful, or otherwise harmful content by running generated text through toxicity classifiers (e.g., Perspective API, local toxicity models). Measures both the rate of toxic outputs and the severity of toxicity, identifying models that are more or less prone to generating harmful content across different scenarios.","intents":["Identify which models are safest for deployment in public-facing applications where toxic output could harm users","Measure whether a model's toxicity varies by scenario (e.g., more toxic in creative writing than Q&A)","Compare toxicity profiles across models to select ones with lower harmful output rates"],"best_for":["Teams deploying models in consumer-facing applications (chatbots, content generation, social media)","Safety researchers studying model behavior and toxicity mitigation","Responsible AI practitioners building guardrails and content filters"],"limitations":["Toxicity detection is imperfect — classifiers have false positives (flagging benign text) and false negatives (missing subtle toxicity)","Toxicity definitions are culturally and contextually dependent; a classifier trained on one culture may misclassify in another","Open-ended generation tasks produce more toxic outputs by design; toxicity rates may not be comparable across task types","Toxicity measurement requires running all outputs through external classifiers, adding latency and cost"],"requires":["Toxicity classifier (Perspective API, local model, or custom classifier)","API credentials for external toxicity services (if using cloud-based classifiers)","Ground-truth toxicity labels for validation (optional but recommended)"],"input_types":["model-generated text outputs"],"output_types":["per-scenario toxicity rates (% of outputs flagged as toxic)","toxicity severity scores (average toxicity level)","toxicity distribution histograms","per-model toxicity profiles"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helm__cap_5","uri":"capability://data.processing.analysis.efficiency.metrics.latency.throughput.and.token.usage.profiling","name":"efficiency metrics: latency, throughput, and token usage profiling","description":"Profiles model efficiency by measuring inference latency, throughput (tokens/second), and token usage (input/output token counts) across scenarios. Computes efficiency metrics like cost-per-task and latency percentiles to enable tradeoff analysis between accuracy and efficiency, helping builders select models that meet both performance and resource constraints.","intents":["Measure inference latency and throughput to ensure models meet SLA requirements for production deployment","Estimate inference costs by combining token usage with model pricing to compare cost-effectiveness across models","Identify efficiency bottlenecks (e.g., which scenarios are most expensive or slow) to optimize prompts or routing"],"best_for":["ML engineers optimizing model selection for cost-constrained or latency-sensitive applications","Builders of real-time systems (chatbots, search) that must meet strict latency budgets","Teams comparing cost-effectiveness of proprietary vs. open-source models"],"limitations":["Latency measurements are environment-dependent — results vary based on hardware, network, and concurrent load","Token usage varies by model and tokenizer; comparisons across model families may not be fair","Efficiency metrics don't account for batching, caching, or other optimization techniques that could change real-world performance","Pricing is static in benchmarks but changes over time; cost comparisons may become stale"],"requires":["Access to model inference endpoints (API or local)","Ability to measure latency and token counts (requires API instrumentation or logging)","Current pricing information for models being evaluated"],"input_types":["model inference requests","scenario prompts and inputs"],"output_types":["per-scenario latency distributions (p50, p95, p99)","throughput metrics (tokens/second)","token usage statistics (input/output token counts)","cost-per-task estimates","efficiency-accuracy tradeoff curves"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helm__cap_6","uri":"capability://data.processing.analysis.scenario.based.evaluation.harness.with.standardized.datasets.and.metrics","name":"scenario-based evaluation harness with standardized datasets and metrics","description":"Provides a modular evaluation framework where each of 42 scenarios is a self-contained test harness with its own dataset, prompt templates, evaluation metrics, and success criteria. Scenarios span diverse tasks (QA, summarization, toxicity detection, machine translation, etc.) and use standardized datasets (SQuAD, CNN/DailyMail, etc.) to enable reproducible, comparable evaluation across models and time.","intents":["Run standardized evaluation on new models without building custom evaluation pipelines for each task","Compare model performance across diverse tasks using consistent methodology and metrics","Reproduce published benchmark results and track model progress over time"],"best_for":["Researchers publishing model papers and needing standardized evaluation methodology","Model developers iterating on training and wanting quick feedback across diverse tasks","Teams adopting HELM as their internal evaluation standard"],"limitations":["Scenario coverage is fixed at 42 — adding custom scenarios requires code changes or external wrappers","Scenarios use public datasets which may not reflect your production distribution","Evaluation is one-time snapshot; no continuous monitoring or drift detection","Some scenarios may have dataset biases or quality issues that affect fairness of comparison"],"requires":["Python 3.8+","Scenario configuration files (YAML/JSON)","Access to public datasets (auto-downloaded or pre-cached)"],"input_types":["scenario identifiers","model configurations","optional: custom prompt templates"],"output_types":["per-scenario evaluation results (accuracy, F1, BLEU, etc.)","scenario-specific error analysis","aggregated metrics across scenarios"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helm__cap_7","uri":"capability://data.processing.analysis.multi.model.comparison.and.leaderboard.generation","name":"multi-model comparison and leaderboard generation","description":"Aggregates evaluation results across multiple models and scenarios to generate comparative leaderboards and ranking tables. Supports filtering, sorting, and visualization of results across different dimensions (by scenario, by metric, by model family) to enable easy comparison and discovery of which models excel in which areas.","intents":["Compare performance of multiple models side-by-side to identify which is best for your use case","Discover which models excel in specific scenarios or metrics (e.g., best for fairness, best for efficiency)","Track model progress over time as new versions are released"],"best_for":["Model selection teams evaluating multiple candidates","Researchers publishing comparative studies","Teams tracking model performance trends over time"],"limitations":["Leaderboards can incentivize benchmark gaming (optimizing for specific scenarios rather than general capability)","Ranking by single metric (e.g., accuracy) obscures multi-dimensional tradeoffs","Leaderboards are static snapshots; they don't reflect real-world performance or deployment context"],"requires":["Evaluation results for multiple models (from running evaluation harness)","Consistent metric definitions across models"],"input_types":["evaluation results (JSON/CSV with per-model, per-scenario metrics)"],"output_types":["leaderboard tables (models × metrics)","filtered/sorted comparison views","visualization dashboards","export formats (CSV, JSON, HTML)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helm__cap_8","uri":"capability://automation.workflow.open.source.reproducibility.and.community.contribution.framework","name":"open-source reproducibility and community contribution framework","description":"Provides open-source codebase with modular architecture enabling researchers and practitioners to reproduce published results, extend evaluation with new scenarios, and contribute improvements back to the community. Uses version control, documentation, and standardized contribution guidelines to ensure reproducibility and enable collaborative development.","intents":["Reproduce published HELM results to verify claims and understand methodology","Extend HELM with custom scenarios or metrics for your specific use case","Contribute improvements, bug fixes, or new scenarios back to the community"],"best_for":["Researchers building on HELM and needing to modify or extend it","Teams adopting HELM as internal evaluation standard and customizing it","Open-source contributors improving the benchmark"],"limitations":["Codebase complexity may be high for non-experts; documentation and examples are essential","Contributing new scenarios requires understanding the framework architecture and conventions","Community contributions may introduce quality or consistency issues if not carefully reviewed"],"requires":["Python 3.8+","Git for cloning and contributing","Understanding of HELM architecture and scenario format"],"input_types":["source code","scenario definitions","documentation and examples"],"output_types":["modified evaluation harness","new scenarios","bug fixes and improvements","documentation updates"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"helm__cap_9","uri":"capability://tool.use.integration.scenario.library.management.and.extensibility","name":"scenario library management and extensibility","description":"Provides a modular scenario library with 42 pre-built scenarios covering diverse tasks (QA, summarization, translation, toxicity detection, etc.). Each scenario is implemented as a pluggable module defining input/output format, evaluation metrics, and optional prompt templates. Enables users to add custom scenarios by implementing a standard scenario interface, allowing evaluation of domain-specific tasks. Scenarios are versioned and documented to ensure reproducibility and clarity.","intents":["Evaluate models on domain-specific tasks by implementing custom scenarios for specialized applications","Extend HELM with new scenarios to cover emerging tasks or domains not in the standard library","Reproduce published results by using the same scenario definitions as prior work","Contribute new scenarios to the HELM community to benefit other researchers"],"best_for":["Researchers extending HELM with custom scenarios for specialized domains","Teams evaluating models on proprietary or domain-specific tasks","Community contributors adding new scenarios to the HELM library"],"limitations":["Custom scenario implementation requires understanding the HELM scenario interface and metric computation patterns","Scenario quality depends on test data quality and metric design; poorly designed scenarios can produce misleading results","Scenario versioning and documentation are manual; no automated validation of scenario correctness","Custom scenarios may not be compatible with all models (e.g., scenarios requiring structured outputs may not work with text-only models)"],"requires":["Python 3.8+ and HELM library","Understanding of HELM scenario interface and metric computation","Test data and ground-truth labels for custom scenarios","Metric implementation (or use of pre-built metrics)"],"input_types":["scenario definitions (Python classes implementing scenario interface)","test data (inputs and ground-truth labels)","metric specifications"],"output_types":["scenario modules (Python code)","scenario documentation","evaluation results for custom scenarios"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":61,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API credentials for models being evaluated (OpenAI, Anthropic, etc.) or local model serving infrastructure","Sufficient compute for running inference across 42 scenarios × multiple model variants","Ground-truth labels for evaluation dataset","Model outputs with confidence scores or logit access","Python 3.8+ with scikit-learn or equivalent for metric computation","Evaluation results in HELM format (JSON)","Web hosting infrastructure for dashboard","JavaScript/React knowledge for customizing dashboard (optional)","Version control system (Git) for scenario and code versioning"],"failure_modes":["Scenario coverage is fixed at 42 — custom domain-specific scenarios require forking the codebase or external wrapper","Evaluation is snapshot-based; no continuous monitoring of model drift or performance degradation over time","Scenario selection may not reflect your specific production distribution — results are indicative but not prescriptive for your use case","Calibration measurement requires ground-truth labels for all test instances — cannot be computed on open-ended generation tasks without human annotation","Different models expose confidence differently (some via logits, some via explicit tokens); normalization across model types may introduce systematic bias","Calibration is task-specific — a well-calibrated model on one scenario may be poorly calibrated on another","Dashboard interactivity is limited to pre-computed results; does not support real-time model evaluation or custom analysis","Large result sets (many models × many scenarios) can be slow to load and navigate","Dashboard design is fixed; users cannot customize visualizations or metrics displayed","Results are read-only; users cannot modify or re-run evaluations through the dashboard","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-05-05T11:48:10.238Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=helm","compare_url":"https://unfragile.ai/compare?artifact=helm"}},"signature":"7kk7AR5bZyJXcV+JYA5SdjFoTJ6+SAIwWrOHV56TXEUVzO/EJN2JmBr9KVs8vagjWeaqTKHX5Kq7wqHw6oGeAw==","signedAt":"2026-06-22T01:04:44.300Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/helm","artifact":"https://unfragile.ai/helm","verify":"https://unfragile.ai/api/v1/verify?slug=helm","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}