Awesome
<!--intro-start-->Holistic Evaluation of Language Models
<img src="https://github.com/stanford-crfm/helm/raw/main/src/helm/benchmark/static/images/helm-logo.png" alt="" width="800"/>
Welcome! The crfm-helm Python package contains code used in the Holistic Evaluation of Language Models project (paper, website) by Stanford CRFM. This package includes the following features:
- Collection of datasets in a standard format (e.g., NaturalQuestions)
- Collection of models accessible via a unified API (e.g., GPT-3, MT-NLG, OPT, BLOOM)
- Collection of metrics beyond accuracy (efficiency, bias, toxicity, etc.)
- Collection of perturbations for evaluating robustness and fairness (e.g., typos, dialect)
- Modular framework for constructing prompts from datasets
- Proxy server for managing accounts and providing unified interface to access models
To get started, refer to the documentation on Read the Docs for how to install and run the package.
Holistic Evaluation of Text-To-Image Models
<img src="https://github.com/stanford-crfm/helm/raw/heim/src/helm/benchmark/static/heim/images/heim-logo.png" alt="" width="800"/>Significant effort has recently been made in developing text-to-image generation models, which take textual prompts as input and generate images. As these models are widely used in real-world applications, there is an urgent need to comprehensively understand their capabilities and risks. However, existing evaluations primarily focus on image-text alignment and image quality. To address this limitation, we introduce a new benchmark, Holistic Evaluation of Text-To-Image Models (HEIM).
We identify 12 different aspects that are important in real-world model deployment, including:
- image-text alignment
- image quality
- aesthetics
- originality
- reasoning
- knowledge
- bias
- toxicity
- fairness
- robustness
- multilinguality
- efficiency
By curating scenarios encompassing these aspects, we evaluate state-of-the-art text-to-image models using this benchmark. Unlike previous evaluations that focused on alignment and quality, HEIM significantly improves coverage by evaluating all models across all aspects. Our results reveal that no single model excels in all aspects, with different models demonstrating strengths in different aspects.
This repository contains the code used to produce the results on the website and paper.