SafetyBench
SafetyBench is a comprehensive benchmark designed specifically for evaluating the safety of large language models (LLMs). This GitHub repository contains resources, data sets, and guidelines for conducting safety evaluations. The benchmark includes over 11,000 diverse multiple-choice questions categorized into various safety concerns, providing researchers with a robust framework to assess LLM performance in terms of safety.
Key Features:
- Extensive Dataset: Contains 11,435 diverse questions in both English and Chinese, covering six categories of safety.
- Evaluation Framework: Offers detailed instructions on how to evaluate and submit results using the benchmark.
- Submissions and Leaderboards: Users can submit their findings for LLM safety assessments and view current leaderboards for performance comparisons.
- Integration: SafetyBench is integrated into SuperBench, facilitating comparative evaluations among different LLMs.
Benefits:
- Promotes understanding of LLM safety capabilities.
- Encourages responsible deployment by identifying safety flaws in AI models.
- Provides an open-source platform for continuous improvement in LLM safety evaluations.
Highlights:
- Accepted at ACL 2024 conference, reinforcing its academic credibility.
- Accessible data and code for easy adoption in research and development.
