The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values. Current benchmarks, however, fall short of effectively uncovering safety vulnerabilities in LLMs. Despite numerous models achieving high scores and 'topping the chart' in these evaluations, there is still a significant gap in LLMs' deeper alignment with human values and achieving genuine harmlessness. To this end, this paper proposes the first highly adversarial benchmark named Flames, consisting of 2,251 manually crafted prompts, ~18.7K model responses with fine-grained annotations, and a specified scorer. Our framework encompasses both common harmlessness principles, such as fairness, safety, legality, and data protection, and a unique morality dimension that integrates specific Chinese values such as harmony. Based on the framework, we carefully design adversarial prompts that incorporate complex scenarios and jailbreaking methods, mostly with implicit malice. By prompting mainstream LLMs with such adversarially constructed prompts, we obtain model responses, which are then rigorously annotated for evaluation. Our findings indicate that all the evaluated LLMs demonstrate relatively poor performance on Flames, particularly in the safety and fairness dimensions. Claude emerges as the best-performing model overall, but with its harmless rate being only 63.08% while GPT-4 only scores 39.04%. The complexity of Flames has far exceeded existing benchmarks, setting a new challenge for contemporary LLMs and highlighting the need for further alignment of LLMs. To efficiently evaluate new models on the benchmark, we develop a specified scorer capable of scoring LLMs across multiple dimensions, achieving an accuracy of 77.4%. The Flames Benchmark is publicly available on https://github.com/AIFlames/Flames.

翻译：大语言模型在各地区的广泛应用，凸显了评估其与人类价值观对齐的迫切需求。然而，现有基准测试在有效揭示大语言模型安全漏洞方面仍显不足。尽管众多模型在这些评测中取得高分并"登顶榜首"，但大语言模型在深层价值观对齐与实现真正无害性方面仍存在显著差距。为此，本文提出了首个高对抗性基准测试Flames，包含2251条人工构建提示词、约1.87万条经过细粒度标注的模型响应及专用评分器。本框架涵盖公平性、安全性、合法性与数据保护等通用无害性准则，并创新性地融入包含和谐等中国特有价值观的道德维度。基于该框架，我们精心设计了融合复杂场景与越狱方法（多数具备隐式恶意）的对抗性提示词。通过向主流大语言模型输入这类对抗性提示词获取模型响应，继而进行严格标注评估。研究结果表明：所有被评估的大语言模型在Flames上表现均相对较差，尤其在安全性与公平性维度。Claude虽为综合表现最优模型，其无害率仅达63.08%，而GPT-4更仅为39.04%。Flames的复杂度已远超现有基准测试，为当代大语言模型设立全新挑战，凸显进一步提升模型对齐的必要性。为高效评估新模型在该基准上的表现，我们开发了能进行多维度评分（准确率达77.4%）的专用评分器。Flames基准测试现已通过https://github.com/AIFlames/Flames公开提供。