Summary of Jailjudge: a Comprehensive Jailbreak Judge Benchmark with Multi-agent Enhanced Explanation Evaluation Framework, by Fan Liu et al.
JAILJUDGE: A Comprehensive Jailbreak Judge Benchmark with Multi-Agent Enhanced Explanation Evaluation Framework
by Fan Liu, Yue Feng, Zhao Xu, Lixin Su, Xinyu Ma, Dawei Yin, Hao Liu
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a comprehensive benchmark called JAILJUDGE to evaluate the safety of large language models (LLMs) against jailbreak attacks. The benchmark features diverse risk scenarios, including synthetic, adversarial, in-the-wild, and multilingual prompts, along with high-quality human-annotated datasets. To enhance evaluation with explicit reasoning, the paper proposes a MultiAgent framework that enables explainable, fine-grained scoring from 1 to 10. The framework supports the construction of instruction-tuning ground truth and facilitates the development of JAILJUDGE Guard, an end-to-end judge model that provides reasoning and eliminates API costs. Additionally, the paper introduces JailBoost, an attacker-agnostic attack enhancer, and GuardShield, a moderation defense, both leveraging JAILJUDGE Guard. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper addresses the challenge of evaluating LLM defenses by proposing a comprehensive benchmark called JAILJUDGE. The benchmark includes diverse risk scenarios and human-annotated datasets to test the safety of LLMs against jailbreak attacks. The paper also proposes a MultiAgent framework that enables explainable, fine-grained scoring to evaluate the performance of LLMs. |
Keywords
» Artificial intelligence » Instruction tuning
