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LLM Alignment Benchmarking Dashboard
Operationalizing LLM safety metrics mapping to ISO/IEC 42001, NIST AI RMF, and the EU AI Act.
Risk Management
Transparency
Fairness
Safety
Robustness
Accountability
Detailed Metrics & Status
| LLM | Live Status | Risk (%) | Transparency (%) | Fairness (%) | Safety (%) | Robustness (%) | Accountability (%) |
|---|
Methodology & Data Rigor
This dashboard is designed to provide a scientifically grounded and reliable view of Large Language Model (LLM) alignment. Rather than relying on transient and often unreliable “live” scoring via simple prompts, we aggregate data from established, gold-standard research benchmarks.
Data Sources
- Transparency: Sourced from the Foundation Model Transparency Index (FMTI) by Stanford CRFM. This index evaluates companies on indicators regarding data, compute, and policy transparency.
- Fairness & Robustness: Sourced from the Holistic Evaluation of Language Models (HELM) project. HELM conducts massive-scale testing across thousands of prompts to determine bias, toxicity, and adversarial robustness.
- Safety: Aggregated from HELM’s safety specific metrics and cross-referenced with TrustLLM benchmarks.
Live Verification
The “Live Sanity Check” feature on this dashboard performs a real-time connectivity test to ensure the models are operational. While the scores are static (updated with research releases), the status indicators confirm that the models are online and accessible via their respective APIs.
Methodology & Data Rigor This dashboard is designed to provide a scientifically grounded and reliable view of Large Language Model (LLM) alignment. Rather than relying on transient and often unreliable “live” scoring via simple prompts, we aggregate data from established, gold-standard research benchmarks.
Data Sources
- Safety: Aggregated from HELM’s safety specific metrics and cross-referenced with TrustLLM benchmarks.
- Transparency: Sourced from the Foundation Model Transparency Index (FMTI) by Stanford CRFM. This index evaluates companies on indicators regarding data, compute, and policy transparency.
- Fairness & Robustness: Sourced from the Holistic Evaluation of Language Models (HELM) project. HELM conducts massive-scale testing across thousands of prompts to determine bias, toxicity, and adversarial robustness.
Live Verification The “Live Sanity Check” feature on this dashboard performs a real-time connectivity test to ensure the models are operational. While the scores are static (updated with research releases), the status indicators confirm that the models are online and accessible via their respective APIs.