Holistic Evaluation of Language Models (HELM)
Holistic Evaluation of Language Models (HELM) is an open-source Python framework developed by the Stanford Center for Research on Foundation Models (CRFM). It provides a standardized, reproducible, and transparent method for evaluating foundation models, including large language models (LLMs) and multimodal models. The framework entered maintenance mode on June 1, 2026, with ongoing updates focused on stability and critical fixes. HELM is designed to assess models across multiple dimensions beyond traditional accuracy, such as calibration, robustness, fairness, bias, and toxicity, while ensuring full transparency of prompts and outputs.
The framework supports a wide range of datasets and benchmarks in a standardized format, including MMLU-Pro, GPQA, IFEval, and WildBench. It also integrates models from various providers—such as OpenAI, Anthropic (Claude), Google (Gemini), Mistral, and Cohere—through a unified interface, simplifying cross-model comparisons. HELM’s modular architecture allows researchers to extend its capabilities to new domains, such as medicine, finance, and multilingual evaluation.
The project is licensed under the permissive Apache 2.0 license, enabling broad adoption in both academic and industry settings. Since its inception in 2021, HELM has been cited in multiple research papers, including evaluations of vision-language models (VHELM), text-to-image models (HEIM), and domain-specific benchmarks like MedHELM for medical tasks. Its leaderboards serve as a reference for model performance across diverse scenarios, fostering accountability and progress in AI development.
How It Works
For example, running helm-run --run-entries mmlu:subject=philosophy,model=openai/gpt2 --suite my-suite --max-eval-instances 10 processes 10 instances of the MMLU benchmark for philosophy using OpenAI’s GPT-2 model. Results are summarized with helm-summarize, and a local web server (helm-server) visualizes outputs at http://localhost:8000/, including per-instance statistics and model responses.
The framework’s design emphasizes reproducibility by enforcing standardized input formats, output mappings, and metric calculations.
HELM’s architecture includes a unified client interface for API-based models, reducing boilerplate code for researchers. This flexibility extends to custom scenarios, where context fields (e.g., in MadinahQA for reading comprehension) are now correctly included to avoid unsolvable instances.
Use Cases
HELM is primarily used for red-teaming and comprehensive benchmarking of AI models across research and industry. Its leaderboards, such as HELM Capabilities and HELM Safety, provide public comparisons of model performance on tasks like reasoning, world knowledge, and adversarial robustness. For example, the HELM Safety leaderboard evaluates toxicity and bias mitigation, while domain-specific leaderboards (e.g., MedHELM) assess clinical reasoning and regulatory compliance.
The framework’s transparency—including raw prompts, model responses, and scoring logic—enables third-party validation and iterative improvements. In enterprise settings, HELM helps organizations audit models for fairness, efficiency, and alignment with industry-specific requirements (e.g., financial risk assessment or legal compliance). The inclusion of multilingual benchmarks (e.g., Arabic Legal and Finance scenarios) also supports global applications where language and cultural context are critical.
For instance, the Image2Struct benchmark uses HELM to assess vision-language models’ ability to extract structured data from images, while Efficient Benchmarking of Language Models demonstrates how HELM can optimize evaluation workflows for resource-constrained environments.
Key Features & Capabilities
- Standardized Benchmarks and Datasets: HELM consolidates diverse evaluation suites (e.g., MMLU-Pro for multitask reasoning, WildBench for real-world robustness) into a unified format. Each benchmark includes predefined metrics, prompts, and reference outputs, ensuring consistency across evaluations. Recent additions like the Arabic Legal and Finance scenarios expand coverage to non-English domains, addressing gaps in multilingual AI assessment.
- Unified Model Interface: The framework abstracts interactions with API-based models (e.g., OpenAI’s GPT-5.4, Anthropic’s Claude, or Google’s Gemini) and local models (e.g., Hugging Face transformers) through a single client interface. Version 0.5.14 introduced support for 10+ providers, including Cohere and xAI, while version 0.5.15 added GPT-5.4 variants and Mistral’s Llama 4 Maverick, reflecting the rapid evolution of LLM capabilities.
- Comprehensive Metrics Beyond Accuracy: HELM evaluates models on dimensions such as calibration (alignment between predicted and actual confidence), robustness (performance under adversarial or noisy inputs), and toxicity (harmful or biased outputs). For example, the HELM Safety leaderboard quantifies bias and fairness using metrics like demographic parity and equalized odds, while efficiency metrics (e.g., tokens per second) assess computational trade-offs. These metrics are critical for identifying trade-offs in model design, such as sacrificing accuracy for reduced toxicity.
- Transparency and Reproducibility: Every evaluation in HELM logs prompts, model responses, and scoring logic, allowing users to audit results or extend benchmarks. The web UI provides granular insights, including per-instance statistics and response visualizations, while the leaderboards offer aggregated comparisons.
- Users can define custom suites or scenarios, such as regulatory compliance tests, by extending the framework’s modular components. The documentation provides templates for reproducing leaderboard results, including steps for setting up environments and interpreting outputs.
- Domain-Specific Leaderboards: HELM maintains specialized leaderboards for vertical industries, such as medicine (MedHELM), finance, and legal analysis. For example, the Enterprise Benchmarks paper uses HELM to evaluate LLMs on tasks like contract analysis and customer support, highlighting its utility in real-world deployments.