Flames：中文大语言模型的价值对齐基准测试
Flames: Benchmarking Value Alignment of LLMs in Chinese
AI总结：
本文提出中文价值对齐基准Flames，结合无害性原则与和谐等中国道德维度设计对抗性提示，评估17个主流大模型并开发轻量级评分器，揭示了现有模型在安全与公平维度的缺陷。
摘要：
大语言模型（LLMs）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，现有基准无法有效揭示LLMs的安全漏洞。尽管许多模型在这些评估中取得高分并“名列前茅”，但LLMs在更深层次上与人类价值观对齐及实现真正无害方面仍存在显著差距。为此，本文提出了一个名为Flames的价值对齐基准，它既包含常见的无害性原则，也包含一个融合了和谐等特定中国价值观的独特道德维度。相应地，我们精心设计了结合复杂场景和越狱方法的对抗性提示，其中大部分带有隐含恶意。通过提示17个主流LLMs，我们获取了模型响应并对其进行了严格标注以进行详细评估。研究结果表明，所有被评估的LLMs在Flames上表现均相对较差，尤其是在安全和公平维度上。我们还开发了一个轻量级的专用评分器，能够跨多维度对LLMs进行评分，以高效评估该基准上的新模型。Flames的复杂性已远超现有基准，为当代LLMs设定了新挑战，并凸显了进一步对齐LLMs的必要性。我们的基准已在https://github.com/AIFlames/Flames公开发布。
英文摘要：
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 a value alignment benchmark named Flames, which encompasses both common harmlessness principles and a unique morality dimension that integrates specific Chinese values such as harmony. Accordingly, we carefully design adversarial prompts that incorporate complex scenarios and jailbreaking methods, mostly with implicit malice. By prompting 17 mainstream LLMs, we obtain model responses and rigorously annotate them for detailed evaluation. Our findings indicate that all the evaluated LLMs demonstrate relatively poor performance on Flames, particularly in the safety and fairness dimensions. We also develop a lightweight specified scorer capable of scoring LLMs across multiple dimensions to efficiently evaluate new models on the benchmark. 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. Our benchmark is publicly available at https://github.com/AIFlames/Flames.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
