SafeDialBench: A Fine-Grained Safety Evaluation Benchmark for Large Language Models in Multi-Turn Dialogues with Diverse Jailbreak Attacks
SafeDialBench evaluates LLM safety across 22 scenarios and 7 attack strategies.
Key Findings
Methodology
SafeDialBench employs a two-tier safety taxonomy covering six dimensions like fairness and legality. It generates over 4000 dialogues in English and Chinese using seven attack strategies, such as reference attack and purpose reverse. The framework measures models' abilities to identify and handle unsafe information.
Key Results
- Yi-34B-Chat and GLM4-9B-Chat excel in safety, while Llama3.1-8B-Instruct and o3-mini show vulnerabilities.
- Yi-34B-Chat has the lowest attack success rate, while Baichuan2-7B-Chat has the highest.
- GPT-3.5 turbo achieves over 80% agreement with human expert evaluations.
Significance
SafeDialBench provides a fine-grained safety evaluation tool for LLMs in multi-turn dialogues, addressing existing benchmarks' limitations. It considers multiple attack strategies and evaluates the models' ability to handle unsafe information, significantly impacting academic and industrial safety research.
Technical Contribution
SafeDialBench introduces an innovative evaluation framework offering fine-grained safety capability assessment. Compared to existing methods, it expands evaluation dimensions and dialogue scenarios, enhancing understanding of safety in multi-turn dialogues.
Novelty
This is the first benchmark to evaluate LLM safety using multiple attack strategies in multi-turn dialogues. It provides a more comprehensive assessment of models' safety capabilities compared to previous work.
Limitations
- SafeDialBench may lack coverage in specific dialogue scenarios, affecting comprehensive evaluation.
- The evaluation framework relies on human annotation, which may introduce subjective bias.
Future Work
Future work can expand the diversity of dialogue scenarios and optimize the evaluation framework to reduce human intervention. Additionally, exploring more attack strategies could enhance comprehensive evaluation.
AI Executive Summary
With the rapid advancement of large language models (LLMs), their safety has become a critical issue. Existing benchmarks primarily focus on single-turn dialogues or single attack methods, failing to evaluate models' capabilities in identifying and handling unsafe information in detail. SafeDialBench addresses this gap by generating over 4000 multi-turn dialogues in English and Chinese across 22 scenarios and employing seven attack strategies.
The innovation of SafeDialBench lies in its two-tier safety taxonomy, covering six dimensions such as fairness, legality, and morality. It evaluates models' abilities to identify, handle unsafe information, and maintain consistency when facing attacks. Experimental results show that Yi-34B-Chat and GLM4-9B-Chat excel in safety, while Llama3.1-8B-Instruct and o3-mini exhibit vulnerabilities.
SafeDialBench not only fills the gaps in existing benchmarks but also provides new directions for future research. Future work can expand the diversity of dialogue scenarios, optimize the evaluation framework, and explore more attack strategies to enhance comprehensive evaluation and accuracy.
Deep Analysis
Background
The widespread application of large language models in dialogue systems has raised concerns about their safety. Existing safety benchmarks like COLD and BeaverTails focus on single-turn dialogues, failing to adequately evaluate safety in multi-turn dialogues. SafeDialBench provides a more comprehensive safety evaluation tool through multi-turn dialogues and multiple attack strategies.
Core Problem
Existing benchmarks fail to adequately evaluate safety in multi-turn dialogues, especially in identifying and handling unsafe information. SafeDialBench aims to fill this gap by comprehensively evaluating models' safety capabilities through multiple attack strategies and dialogue scenarios.
Innovation
The core innovation of SafeDialBench lies in its two-tier safety taxonomy and multiple attack strategies. By covering 22 dialogue scenarios and employing seven attack strategies, SafeDialBench provides a fine-grained safety evaluation tool to assess models' safety capabilities in multi-turn dialogues.
Methodology
- �� Design a two-tier safety taxonomy covering six dimensions.
- �� Generate over 4000 multi-turn dialogues in English and Chinese across 22 scenarios.
- �� Employ seven attack strategies, such as reference attack and purpose reverse.
- �� The evaluation framework measures models' abilities to identify and handle unsafe information.
Experiments
The experiments involved 19 large language models, including four closed-source models and 15 open-source models. The evaluation framework measured the models' performance across six safety dimensions, with evaluations conducted by GPT-3.5 turbo and human experts.
Results
Experimental results show that Yi-34B-Chat and GLM4-9B-Chat excel in safety, while Llama3.1-8B-Instruct and o3-mini exhibit vulnerabilities. GPT-3.5 turbo achieves over 80% agreement with human expert evaluations.
Applications
SafeDialBench can be used to evaluate the safety of dialogue systems, helping developers identify and fix safety vulnerabilities in models, thereby improving the reliability and trustworthiness of dialogue systems.
Limitations & Outlook
SafeDialBench may lack coverage in specific dialogue scenarios, affecting comprehensive evaluation. Additionally, the evaluation framework relies on human annotation, which may introduce subjective bias. Future work can expand the diversity of dialogue scenarios and optimize the evaluation framework to reduce human intervention.
Plain Language Accessible to non-experts
Imagine you're in a complex conversation, trying to ensure the other person doesn't say something inappropriate. SafeDialBench acts like a smart assistant, helping you identify and handle potential dangerous information in the dialogue. It tests dialogue systems' response capabilities in different situations through various strategies and scenarios, ensuring the safety of the conversation.
ELI14 Explained like you're 14
Imagine you're playing a game with many levels, each with different challenges. SafeDialBench is like this game; it tests large language models' performance in different dialogue scenarios, ensuring they don't say something they shouldn't. Through these tests, we can better understand the models' safety and help them become smarter and safer.
Glossary
Large Language Model
An AI model capable of generating and understanding natural language, widely used in dialogue systems.
SafeDialBench evaluates these models' safety in multi-turn dialogues.
Safety Taxonomy
A framework for categorizing and evaluating safety risks in dialogues, covering multiple dimensions.
One of the core innovations of SafeDialBench.
Multi-Turn Dialogue
A dialogue form involving multiple turns, closer to real user interactions.
SafeDialBench evaluates model safety through multi-turn dialogues.
Attack Strategy
Different methods used to test model safety, such as reference attack and purpose reverse.
SafeDialBench uses seven attack strategies to evaluate models.
Evaluation Framework
A tool for measuring models' abilities to identify and handle unsafe information.
The evaluation framework is one of SafeDialBench's technical contributions.
Open Questions Unanswered questions from this research
- 1 How to improve the accuracy of the evaluation framework without increasing human intervention?
- 2 What new attack strategies can be explored to further test model safety?
Applications
Immediate Applications
Dialogue System Safety Evaluation
Developers can use SafeDialBench to evaluate and improve the safety of dialogue systems, ensuring the safety of user interactions.
Long-term Vision
Comprehensive Safety Framework
SafeDialBench can serve as a foundation for developing a comprehensive safety framework, applicable to safety evaluations in more fields.
Abstract
With the rapid advancement of Large Language Models (LLMs), the safety of LLMs has been a critical concern requiring precise assessment. Current benchmarks primarily concentrate on single-turn dialogues or a single jailbreak attack method to assess the safety. Additionally, these benchmarks have not taken into account the LLM's capability of identifying and handling unsafe information in detail. To address these issues, we propose a fine-grained benchmark SafeDialBench for evaluating the safety of LLMs across various jailbreak attacks in multi-turn dialogues. Specifically, we design a two-tier hierarchical safety taxonomy that considers 6 safety dimensions and generates more than 4000 multi-turn dialogues in both Chinese and English under 22 dialogue scenarios. We employ 7 jailbreak attack strategies, such as reference attack and purpose reverse, to enhance the dataset quality for dialogue generation. Notably, we construct an innovative assessment framework of LLMs, measuring capabilities in detecting, and handling unsafe information and maintaining consistency when facing jailbreak attacks. Experimental results across 17 LLMs reveal that Yi-34B-Chat and GLM4-9B-Chat demonstrate superior safety performance, while Llama3.1-8B-Instruct and o3-mini exhibit safety vulnerabilities.