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HELM Agent Evaluation Framework(HELM-AE)
Stanford CRFM's Holistic Evaluation of Language Models extended for agent capabilities. The worked example is a dated benchmark snapshot, not a current model recommendation.
In 30 seconds
- What
- Measures agent performance across seven dimensions: accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency, including tool use and API interaction in standardized scenarios.
- When to use
- Comparing multiple agents before deployment or tracking performance regressions across model versions and tool integrations.
- Watch out
- Benchmark scores don't predict real-world performance; your actual tasks may differ significantly from the 42 test scenarios.
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HELM Agent Evaluation Framework: Overview
Stanford CRFM's Holistic Evaluation of Language Models extended for agent capabilities. The worked example is a dated benchmark snapshot, not a current model recommendation.
- Holistic 7-metric evaluation (accuracy, calibration, robustness, fairness, bias, toxicity, efficiency)
- Multimodal task assessment
- Tool use and API interaction evaluation
- Simulation environment testing
- Standardized benchmark format
- Open-source evaluation framework
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References
The papers, specifications, and repositories this pattern is based on.
- Holistic Evaluation of Language Models (HELM) - Stanford CRFM
- arXiv:2211.09110 - HELM FrameworkarXiv:2211.09110
- crfm.stanford.edu/helm
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