Holistic Evaluation of Language Models

Overview

Holistic Evaluation of Language Models (HELM) is a framework from Stanford for evaluating language models across multiple dimensions. It combines standardized scenarios and metrics to assess accuracy, calibration, robustness, fairness, and other properties in a single benchmark.

Best for

Best for
Researchers and engineers who need a rigorous, standardized way to assess and compare language model capabilities and limitations.

Use cases

• Comparing the strengths and weaknesses of different language models on a common set of tasks
• Identifying specific failure modes or biases in a model before deployment
• Establishing a reproducible evaluation protocol for research publications

Notes

Holistic Evaluation of Language Models (HELM) is a framework from Stanford for evaluating language models across multiple dimensions. It combines standardized scenarios and metrics to assess accuracy, calibration, robustness, fairness, and other properties in a single benchmark.

Use cases

• Comparing the strengths and weaknesses of different language models on a common set of tasks
• Identifying specific failure modes or biases in a model before deployment
• Establishing a reproducible evaluation protocol for research publications

Pros

• Covers a broad range of metrics beyond simple accuracy, giving a multidimensional view of model quality
• Provides a standardized, well-documented methodology that enables fair comparisons across models
• Open-source framework with community contributions, free to use and extend

Cons

• Evaluation can be computationally expensive and time-consuming for large models
• The static scenario set may not reflect all real-world use cases or recent task innovations
• Results are only as reliable as the underlying data and can be affected by dataset contamination

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