What is HELM?
HELM, short for Holistic Evaluation of Language Models, is a large-scale model evaluation system developed by Stanford University. The evaluation method mainly includes three modules: scenario, adaptation, and metrics. Each evaluation run requires specifying a scenario, a hint for adapting the model, and one or more metrics. It primarily covers English, comprehensively evaluating model performance through accuracy, uncertainty/calibration, robustness, fairness, bias, toxicity, and inference efficiency. It is applicable to tasks such as question answering, information retrieval, and text classification, providing a more comprehensive and systematic evaluation method for language models, helping researchers and developers better understand and optimize model performance.
Main functions of HELM
- Comprehensive evaluation capabilities : HELM supports a variety of language modeling tasks (such as question answering, text classification, information retrieval, text generation, summarization, etc.) and provides a variety of evaluation metrics (including accuracy, robustness, fairness, bias, toxicity, inference efficiency, etc.), enabling a comprehensive evaluation of the performance of language models from multiple dimensions.
- Reproducibility and Transparency : HELM is based on a standardized evaluation process and configuration files, ensuring that different users can obtain consistent evaluation results under the same conditions. Users can view and modify the evaluation code, guaranteeing the transparency and customizability of the evaluation process.
- Multimodal support : HELM supports not only plain text tasks, but also multimodal tasks (such as image description generation, visual question answering, etc.) to evaluate the overall performance of multimodal models.
- Custom extensions : Users can customize evaluation tasks, adaptation strategies, and metrics according to their own needs. HELM provides a flexible extension mechanism to meet specific research or application needs.
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