SafetyBench: Evaluating the Safety of Large Language Models
SafetyBench evaluates LLM safety; GPT-4 excels.
Key Findings
Methodology
SafetyBench uses multiple-choice questions to evaluate LLM safety across seven categories, including offensiveness and bias. It comprises 11,435 questions and supports bilingual testing.
Key Results
- GPT-4 excels in zero-shot and few-shot settings, achieving an average accuracy of 89.2%.
- Other models show significant gaps, especially in physical health and ethics.
- Safety understanding correlates with generation abilities; GPT-4 performs well in generation tasks.
Significance
SafetyBench provides a comprehensive benchmark for LLM safety evaluation, addressing the gap in existing tools. It aids in identifying model flaws and improving safety.
Technical Contribution
SafetyBench offers an efficient automated evaluation method with multilingual support, significantly reducing the cost and time of manual evaluation. Its diverse question types ensure comprehensive safety assessment.
Novelty
SafetyBench is the first to use multiple-choice questions for LLM safety evaluation, providing multilingual support and addressing limitations of existing benchmarks.
Limitations
- Some questions may have translation inaccuracies due to cultural differences.
- Automated evaluation may not fully match the precision of manual evaluation.
Future Work
Future work could expand question categories, add more language support, and optimize translation quality to improve accuracy.
AI Executive Summary
With the rapid development of large language models, their safety concerns have garnered increasing attention. Existing evaluation tools often focus on specific aspects, lacking comprehensive safety standards. SafetyBench provides a comprehensive benchmark for evaluating LLM safety through multiple-choice questions across seven categories.
Testing 25 popular Chinese and English LLMs, results show GPT-4 excels in zero-shot and few-shot settings, achieving an average accuracy of 89.2%. Other models show significant gaps, especially in physical health and ethics.
SafetyBench not only offers an efficient automated tool for LLM safety evaluation but also supports bilingual testing, significantly reducing the cost and time of manual evaluation. Future work could expand question categories, add more language support, and optimize translation quality to improve accuracy.
Deep Analysis
Background
Large language models have made significant advances in NLP, yet their safety remains a crucial research area. Existing tools often focus on specific aspects like bias or toxicity, lacking comprehensive evaluation standards.
Core Problem
Evaluating LLM safety is challenging; existing tools fail to cover all safety categories comprehensively. A standard capable of assessing various safety issues is needed to identify potential model flaws.
Innovation
SafetyBench uses multiple-choice questions to evaluate LLM safety, covering seven categories and supporting bilingual testing, offering an efficient automated evaluation method.
Methodology
- �� Use 11,435 multiple-choice questions for evaluation
- �� Cover seven safety categories including offensiveness and bias
- �� Support bilingual testing to ensure comprehensive and accurate assessment
Experiments
Test 25 popular Chinese and English LLMs in zero-shot and few-shot settings, using accuracy as the primary metric, showing GPT-4 excels.
Results
GPT-4 stands out with an average accuracy of 89.2%. Other models show significant gaps, especially in physical health and ethics.
Applications
SafetyBench can be used to evaluate LLM safety, helping developers identify potential flaws and improve model safety.
Limitations & Outlook
Some questions may have translation inaccuracies due to cultural differences; automated evaluation may not fully match the precision of manual evaluation. Future work should optimize translation quality to improve accuracy.
Plain Language Accessible to non-experts
Imagine a school exam where teachers give students a set of multiple-choice questions to test their knowledge. These questions cover various topics like math, history, and science. SafetyBench is like such an exam, but it's designed to test the safety of large language models. By answering these questions, we can understand how models perform in handling different safety issues, such as whether they generate offensive content or unfair results.
ELI14 Explained like you're 14
Imagine you're playing a game with lots of levels, each with different challenges. SafetyBench is like those game levels, but it's designed to test the safety of large language models. Through these levels, we can see how models perform in handling different safety issues, like whether they generate offensive content or unfair results. Isn't that cool?
Glossary
SafetyBench
A benchmark for evaluating the safety of large language models.
Used to test model performance in handling different safety issues.
Large Language Model (LLM)
A large-scale machine learning model capable of generating and understanding natural language.
Subject of study, evaluating its safety.
Zero-shot Setting
The ability of a model to make predictions without any examples.
Evaluating model performance on unknown problems.
Few-shot Setting
The ability of a model to make predictions with only a few examples.
Evaluating model performance with limited information.
Offensive Content
Language or behavior that may offend or harm others.
Evaluating the safety of model-generated content.
Open Questions Unanswered questions from this research
- 1 How to improve translation quality to reduce errors from cultural differences?
- 2 How to expand question categories to cover more safety issues?
Applications
Immediate Applications
Model Safety Evaluation
Developers can use SafetyBench to quickly evaluate model safety and identify potential flaws.
Long-term Vision
Safety Enhancement
Continual evaluation and improvement to enhance the overall safety of large language models.
Abstract
With the rapid development of Large Language Models (LLMs), increasing attention has been paid to their safety concerns. Consequently, evaluating the safety of LLMs has become an essential task for facilitating the broad applications of LLMs. Nevertheless, the absence of comprehensive safety evaluation benchmarks poses a significant impediment to effectively assess and enhance the safety of LLMs. In this work, we present SafetyBench, a comprehensive benchmark for evaluating the safety of LLMs, which comprises 11,435 diverse multiple choice questions spanning across 7 distinct categories of safety concerns. Notably, SafetyBench also incorporates both Chinese and English data, facilitating the evaluation in both languages. Our extensive tests over 25 popular Chinese and English LLMs in both zero-shot and few-shot settings reveal a substantial performance advantage for GPT-4 over its counterparts, and there is still significant room for improving the safety of current LLMs. We also demonstrate that the measured safety understanding abilities in SafetyBench are correlated with safety generation abilities. Data and evaluation guidelines are available at \url{https://github.com/thu-coai/SafetyBench}{https://github.com/thu-coai/SafetyBench}. Submission entrance and leaderboard are available at \url{https://llmbench.ai/safety}{https://llmbench.ai/safety}.