JudgeZoo

Co-authored and open-sourced Python library providing standardized implementations of common LLM safety evaluators via a consistent API, improving reproducibility and enabling fair benchmarking of LLM safety models.

Overview

JudgeZoo addresses a critical need in the LLM safety research community by providing a unified interface for various safety evaluation methods. The library standardizes how researchers can evaluate and compare different LLM safety approaches.

Key Features

Standardized API: Consistent interface across different safety evaluators, making it easy to swap between different evaluation methods.

Reproducibility: Standardized implementations ensure that research results can be reliably reproduced across different studies.

Fair Benchmarking: Enables objective comparison between different LLM safety models and approaches.

Extensible Design: Modular architecture allows for easy addition of new evaluation methods as the field evolves.

Impact

This work contributes to the broader effort of making AI systems safer and more reliable by providing researchers with the tools they need to rigorously evaluate safety mechanisms. The standardized approach helps build confidence in safety evaluations and facilitates collaboration across the research community.

The library is designed to be used by researchers working on LLM robustness, AI safety, and related fields where consistent evaluation methodology is crucial for advancing the state of the art.