Agentic Design

Patterns
🎓

HELM Agent Evaluation Framework(HELM-AE)

Stanford CRFM's Holistic Evaluation of Language Models extended for agent capabilities, measuring 7 metrics across multimodal tasks, tool use, and simulation environments.

Complexity: highEvaluation and Monitoring

🎯 30-Second Overview

Pattern: Stanford CRFM's holistic framework evaluating agents across 7 metrics and 42 scenarios

Why: Moves beyond accuracy to comprehensive assessment including fairness, robustness, and efficiency trade-offs

Key Insight: Reveals critical trade-offs between metrics and ensures non-accuracy dimensions aren't second-class citizens

⚡ Quick Implementation

1Install:pip install crfm-helm for holistic evaluation framework
2Configure:Set up scenarios across 16 core domains with 7 metrics
3Evaluate:Run comprehensive assessment including multimodal and tool use
4Analyze:Review holistic metrics beyond accuracy (fairness, robustness, etc.)
5Compare:Benchmark against 30+ models with standardized evaluation
Example: helm-run --model gpt-4 --scenarios core --metrics all --output evaluation_report.json

The 7 Core Metrics

1

Accuracy

Traditional performance measurement across task scenarios

2

Calibration

Whether the model knows what it doesn't know - confidence alignment

3

Robustness

Performance under perturbations (e.g., typos, input variations)

4

Fairness

Performance consistency across different groups and demographics

5

Bias

Systematic unfairness detection in model outputs and decisions

6

Toxicity

Generation of harmful, offensive, or dangerous content

7

Efficiency

Computational resource usage and inference speed optimization

📋 Do's & Don'ts

✅Evaluate across all 7 metrics, not just accuracy for holistic assessment
✅Use standardized scenarios (16 core + 26 targeted) for consistency
✅Include multimodal and tool use capabilities in evaluation
✅Test robustness with perturbations and fairness across groups
✅Leverage HELM Lite for streamlined yet comprehensive evaluation
❌Focus solely on accuracy - other metrics reveal critical trade-offs
❌Skip calibration testing if model exposes probability outputs
❌Ignore efficiency metrics for production deployment decisions
❌Use custom scenarios without standardized comparison baselines
❌Overlook bias and toxicity assessment for responsible deployment

🚦 When to Use

Use When

  • • Comprehensive model comparison across multiple dimensions
  • • Academic research requiring standardized evaluation
  • • Enterprise deployment decisions needing holistic assessment
  • • Responsible AI evaluation including bias and toxicity
  • • Multi-modal agent capability assessment

Avoid When

  • • Quick single-metric performance checks
  • • Domain-specific benchmarks outside HELM scenarios
  • • Real-time evaluation needs (computationally intensive)
  • • Custom evaluation scenarios without standardization needs
  • • Budget-constrained evaluation (requires significant compute)

📊 Key Metrics

Holistic Score
Aggregate performance across all 7 dimensions
Scenario Coverage
Performance across 16 core + 26 targeted scenarios
Trade-off Analysis
Correlation patterns between different metrics
Multimodal Capability
Text-to-image and vision-language performance
Tool Use Proficiency
External API integration and plugin effectiveness
Simulation Environment Success
End-to-end task completion in realistic settings

💡 Top Use Cases

Academic Model Comparison: Standardized evaluation across 30+ models with transparent methodology
Enterprise AI Selection: Holistic assessment beyond accuracy for responsible deployment decisions
Responsible AI Development: Comprehensive bias, fairness, and toxicity evaluation frameworks
Multimodal Agent Testing: Vision-language and tool use capability assessment for complex applications
Research Benchmarking: Reproducible evaluation framework for foundation model research publications

References & Further Reading

Deepen your understanding with these curated resources

Official HELM Resources

HELM GitHub Repository (Stanford CRFM)

Holistic Evaluation of Language Models (arXiv:2211.09110)

HELM Official Website

HELM Documentation

HELM Extensions & Updates

HELM Lite: Lightweight Yet Broad Benchmark (Stanford CRFM 2023)

MedHELM: Medical Domain Evaluation Extension

Everything About HELM - Comprehensive Guide (Medium)

HELM Installation and Setup Guide

Research & Analysis

Holistic Evaluation Paper (OpenReview)

HELM Research Paper (ResearchGate)

HELM Paper (Annals of NY Academy of Sciences)

Stanford CRFM Research Publications

Enterprise & Industry Applications

HELM Enterprise LLM Evaluation (Snorkel AI)

HELM Glossary and Overview (Klu.ai)

Emergence AI Appropriateness Evaluation Model

Center for Research on Foundation Models

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