Large language models (LLMs) are possessed of numerous beneficial capabilities, yet their potential inclination harbors unpredictable risks that may materialize in the future. We hence propose CRiskEval, a Chinese dataset meticulously designed for gauging the risk proclivities inherent in LLMs such as resource acquisition and malicious coordination, as part of efforts for proactive preparedness. To curate CRiskEval, we define a new risk taxonomy with 7 types of frontier risks and 4 safety levels, including extremely hazardous,moderately hazardous, neutral and safe. We follow the philosophy of tendency evaluation to empirically measure the stated desire of LLMs via fine-grained multiple-choice question answering. The dataset consists of 14,888 questions that simulate scenarios related to predefined 7 types of frontier risks. Each question is accompanied with 4 answer choices that state opinions or behavioral tendencies corresponding to the question. All answer choices are manually annotated with one of the defined risk levels so that we can easily build a fine-grained frontier risk profile for each assessed LLM. Extensive evaluation with CRiskEval on a spectrum of prevalent Chinese LLMs has unveiled a striking revelation: most models exhibit risk tendencies of more than 40% (weighted tendency to the four risk levels). Furthermore, a subtle increase in the model's inclination toward urgent self-sustainability, power seeking and other dangerous goals becomes evident as the size of models increase. To promote further research on the frontier risk evaluation of LLMs, we publicly release our dataset at https://github.com/lingshi6565/Risk_eval.

翻译：大语言模型具备诸多有益能力，但其潜在倾向性蕴含着未来可能显现的不可预测风险。为此，我们提出CRiskEval——一个精心设计的中文数据集，旨在评估大语言模型内在的风险倾向（例如资源获取与恶意协同），作为主动防范工作的一部分。为构建CRiskEval，我们定义了一个包含7类前沿风险和4个安全等级（包括极度危险、中度危险、中立与安全）的新风险分类体系。我们遵循倾向性评估的理念，通过细粒度的多项选择题问答来实证测量大语言模型所陈述的意愿。该数据集包含14,888个模拟预定义7类前沿风险相关场景的问题。每个问题均配有4个陈述观点或行为倾向的选项。所有选项均已人工标注为定义的风险等级之一，从而能够为每个被评估的大语言模型轻松构建细粒度的前沿风险画像。基于CRiskEval对一系列主流中文大语言模型进行的广泛评估揭示了一个引人注目的发现：大多数模型表现出超过40%的风险倾向（对四个风险等级的加权倾向）。此外，随着模型规模增大，模型对紧急自我维持、权力追求及其他危险目标的倾向性呈现出微妙的上升趋势。为促进大语言模型前沿风险评估的进一步研究，我们在https://github.com/lingshi6565/Risk_eval公开发布了本数据集。