对比11种提示策略，发现简单示例提示更安全高效
ProMoral-Bench: Evaluating Prompting Strategies for Moral Reasoning and Safety in LLMs
- 用11种提示范式测试4类大模型的道德与安全表现
- 示例引导的简洁提示比复杂推理更稳定且节省计算资源
- 提出统一评分标准，适合安全对齐研究者使用
提示设计显著影响大语言模型的道德能力与安全对齐，但现有评估分散于不同数据集和模型。我们提出ProMoral-Bench，一个统一基准，评估11种提示范式在四个大模型家族中的表现。基于ETHICS、Scruples、WildJailbreak及新提出的鲁棒性测试ETHICS-Contrast，采用自研的统一道德安全得分（UMSS）进行评估。结果表明，紧凑的示例引导提示优于复杂的多阶段推理，具有更高的UMSS得分和更强鲁棒性，且消耗更少令牌。多轮推理在扰动下易失效，而少样本示例能持续提升道德稳定性与越狱抵抗能力。ProMoral-Bench建立了标准化的、成本可控的提示工程框架。
原文摘要 · Abstract (English)
Prompt design significantly impacts the moral competence and safety alignment of large language models (LLMs), yet empirical comparisons remain fragmented across datasets and models.We introduce ProMoral-Bench, a unified benchmark evaluating 11 prompting paradigms across four LLM families. Using ETHICS, Scruples, WildJailbreak, and our new robustness test, ETHICS-Contrast, we measure performance via our proposed Unified Moral Safety Score (UMSS), a metric balancing accuracy and safety. Our results show that compact, exemplar-guided scaffolds outperform complex multi-stage reasoning, providing higher UMSS scores and greater robustness at a lower token cost. While multi-turn reasoning proves fragile under perturbations, few-shot exemplars consistently enhance moral stability and jailbreak resistance. ProMoral-Bench establishes a standardized framework for principled, cost-effective prompt engineering.
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