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HELM (Holistic Evaluation of Language Models)

What it is

HELM (Holistic Evaluation of Language Models) is an open-source evaluation framework developed by Stanford University's Center for Research on Foundation Models (CRFM). It is designed to provide a comprehensive, transparent, and multi-dimensional assessment of Large Language Models (LLMs).

What problem it solves

LLM evaluation is often narrow, focusing only on accuracy for a few tasks. HELM addresses this by evaluating models across a wide range of "scenarios" (tasks) and "metrics" (accuracy, fairness, safety, efficiency, etc.), providing a holistic view of model behavior rather than just a single score.

Where it fits in the stack

Benchmarking. It is a major framework used by researchers and engineers to perform deep-dive evaluations of foundation models.

Typical use cases

  • Holistic Model Assessment: Evaluating a new model version across accuracy, safety, and bias simultaneously.
  • Comparison of Foundation Models: Using standardized scenarios to compare models like GPT-4, Claude, and Llama on equal footing.
  • Safety and Fairness Auditing: Specifically checking for toxicity and bias in model responses across different demographics.

Getting started

Installation

It is recommended to install HELM into a virtual environment with Python >= 3.10.

# Install the base HELM package
pip install crfm-helm

# Install additional dependencies for multimodal (VHELM/HEIM) support
pip install "crfm-helm[vlm]"

Hello-world task

Evaluate GPT-2 on a small subset of the MMLU philosophy subject:

# Run the benchmark (limited to 10 instances)
helm-run --run-entries mmlu:subject=philosophy,model=openai/gpt2 --suite my-suite --max-eval-instances 10

# Summarize the results
helm-summarize --suite my-suite

# View the results in the web UI
helm-server --suite my-suite
The results will be available at http://localhost:8000/.

CLI Reference

HELM provides three primary CLI tools for the evaluation lifecycle:

helm-run

Executes the evaluation. You can specify what to run using the --run-entries flag (for quick commands) or a --conf-file (for complex batch runs).

# Execute a specific scenario and model
helm-run --run-entries med_qa:model=openai/gpt2 --suite med-suite --max-eval-instances 10

# Execute using a configuration file
helm-run --conf-file run_entries.conf --suite production-suite

helm-summarize

Processes the raw outputs from helm-run into a summary format that can be visualized or compared.

# Summarize a completed suite
helm-summarize --suite med-suite

helm-server

Launches a local web server to browse the results, inspect individual prompts, and view the leaderboard.

# Start the UI server
helm-server --suite med-suite

Specialized Evaluations

HELM has expanded beyond general text models into specialized domains:

  • VHELM (Vision-Language Models): Evaluates VLMs on visual perception, reasoning, and safety. Uses benchmarks like MMMU and aggregates results across 9 distinct aspects.
  • HEIM (Holistic Evaluation of Text-To-Image Models): Focuses on image generation models, measuring alignment, quality, and aesthetics.
  • MedHELM: A specialized version for medical tasks, incorporating datasets like MedQA to assess model performance in clinical contexts.
  • AIR-Bench (2026): Integrated into HELM v0.5+, AIR-Bench focuses on Agentic Intelligence and Reasoning, evaluating how models perform when given multi-step tasks that require tool use and external environmental interaction.

Strengths

  • Multi-dimensional: Moves beyond simple accuracy to include metrics like calibration, robustness, and fairness.
  • Scenario-Metric Grid: Uses a systematic approach to ensure broad coverage of tasks.
  • Transparency: Provides full visibility into the prompts used and the individual model responses.
  • LiteLLM Integration: HELM v0.5.14+ supports LiteLLM as a backend, enabling benchmarking of any model compatible with the OpenAI API via a local proxy.
  • Active Research: Regularly updated by Stanford with new datasets and the latest models.

Limitations

  • High Complexity: Setting up and running full HELM evaluations is computationally expensive and complex.
  • API Dependency: Many scenarios require access to external model APIs, which can incur high costs.
  • Learning Curve: The framework's modularity makes it powerful but also harder to master than simpler evaluation scripts.

When to use it

  • When you need a highly rigorous, academic-grade evaluation of a foundation model.
  • When you are concerned with safety, bias, or robustness in addition to raw performance.
  • When participating in or reproducing results for major LLM leaderboards.

When not to use it

  • For quick, "vibe-check" style evaluations of a specific application prompt.
  • If you have very limited compute or budget for API calls.
  • For evaluating specific RAG pipelines (consider RAGAS instead).

Licensing and cost

  • Open Source: Yes (Apache 2.0)
  • Cost: Free (but compute/API costs for running evals apply)
  • Self-hostable: Yes

Sources / References

Contribution Metadata

  • Last reviewed: 2026-05-28
  • Confidence: high