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SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models

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arxiv 2402.05044 v4 pith:E65HEJQ2 submitted 2024-02-07 cs.CL cs.AIcs.CRcs.LG

Lijun Li , Bowen Dong , Ruohui Wang , Xuhao Hu , Wangmeng Zuo , Dahua Lin , Yu Qiao , Jing Shao This is my paper
classification cs.CLcs.AIcs.CRcs.LG
keywords salad-benchdefensesafetyattackensuringevaluationlargellms
verification ladder T0 review T1 audit T2 compute T3 formal
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In the rapidly evolving landscape of Large Language Models (LLMs), ensuring robust safety measures is paramount. To meet this crucial need, we propose \emph{SALAD-Bench}, a safety benchmark specifically designed for evaluating LLMs, attack, and defense methods. Distinguished by its breadth, SALAD-Bench transcends conventional benchmarks through its large scale, rich diversity, intricate taxonomy spanning three levels, and versatile functionalities.SALAD-Bench is crafted with a meticulous array of questions, from standard queries to complex ones enriched with attack, defense modifications and multiple-choice. To effectively manage the inherent complexity, we introduce an innovative evaluators: the LLM-based MD-Judge for QA pairs with a particular focus on attack-enhanced queries, ensuring a seamless, and reliable evaluation. Above components extend SALAD-Bench from standard LLM safety evaluation to both LLM attack and defense methods evaluation, ensuring the joint-purpose utility. Our extensive experiments shed light on the resilience of LLMs against emerging threats and the efficacy of contemporary defense tactics. Data and evaluator are released under https://github.com/OpenSafetyLab/SALAD-BENCH.

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Cited by 11 Pith papers

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