GuardBench

⚡️ Introduction

GuardBench is a Python library for the evaluation of guardrail models, i.e., LLMs fine-tuned to detect unsafe content in human-AI interactions. GuardBench provides a common interface to 40 evaluation datasets, which are downloaded and converted into a standardized format for improved usability. It also allows to quickly compare results and export LaTeX tables for scientific publications. GuardBench's benchmarking pipeline can also be leveraged on custom datasets.

GuardBench was featured in EMNLP 2024. The related paper is available here.

GuardBench has a public leaderboard available on HuggingFace.

You can find the list of supported datasets here. A few of them requires authorization. Please, read this.

If you use GuardBench to evaluate guardrail models for your scientific publications, please consider citing our work.

✨ Features

• 40 datasets for guardrail models evaluation.
• Automated evaluation pipeline.
• User-friendly.
• Extendable.
• Reproducible and sharable evaluation.
• Exportable evaluation reports.

🔌 Requirements

bash python>=3.10

💾 Installation

bash pip install guardbench

💡 Usage

```python from guardbench import benchmark

def moderate( conversations: list[list[dict[str, str]]], # MANDATORY! # additional kwargs as needed ) -> list[float]: # do moderation # return list of floats (unsafe probabilities)

benchmark( moderate=moderate, # User-defined moderation function modelname="My Guardrail Model", batchsize=32, datasets="all", # Note: you can pass additional kwargs for moderate ) ```

📖 Examples

• Follow our tutorial on benchmarking Llama Guard with GuardBench.
• More examples are available in the scripts folder.

📚 Documentation

Browse the documentation for more details about: - The datasets and how to obtain them. - The data format used by GuardBench. - How to use the Report class to compare models and export results as LaTeX tables. - How to leverage GuardBench's benchmarking pipeline on custom datasets.

🏆 Leaderboard

You can find GuardBench's leaderboard here. If you want to submit your results, please contact us. <!-- All results can be reproduced using the provided scripts. -->

👨‍💻 Authors

• Elias Bassani (European Commission - Joint Research Centre)

🎓 Citation

bibtex @inproceedings{guardbench, title = "{G}uard{B}ench: A Large-Scale Benchmark for Guardrail Models", author = "Bassani, Elias and Sanchez, Ignacio", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.1022", doi = "10.18653/v1/2024.emnlp-main.1022", pages = "18393--18409", }

🎁 Feature Requests

Would you like to see other features implemented? Please, open a feature request.

📄 License

GuardBench is provided as open-source software licensed under EUPL v1.2.