宪法人工智能测验

当您参加测验时，就会出现答案和解释。

1. 宪法人工智能中的“宪法”是什么？
   • 用户与本公司之间的合法合同
   • 模型的神经网络架构
   • 模型用来批评和指导其自身反应的一套明确的书面原则
   • 禁用词汇列表
2. What annotation burden does the original Constitutional AI study aim to reduce?
   • Every possible source of human judgment or training data
   • All model memory during inference
   • The need to evaluate any model behavior
   • Reliance on human labels identifying harmful outputs
3. 在宪法人工智能的监督阶段，模型对自己的输出做了什么？
   • 删除不解释
   • 根据原则对其进行批评，然后对其进行修改以使其更好
   • 将其翻译成另一种语言
   • 为了安全起见对其进行加密
4. 强化学习阶段的RLAIF代表什么？
   • 从人工智能反馈中强化学习
   • 内部函数之间的递归学习
   • 降低延迟的人工智能框架
   • 微调中的随机标签分配
5. What transparency benefit can written constitutional principles provide?
   • A complete display of every learned internal preference
   • Proof that every future response follows all principles
   • Automatic elimination of the preference model
   • An inspectable statement of intended guidance used in the process
6. Which broader research idea does using AI-generated supervision support?
   • Eliminating every evaluation by definition
   • Exploring how AI can help supervise other AI systems
   • Guaranteeing zero cost for all future training
   • Automatically increasing every model’s vocabulary
7. Does editing a constitution document alone retroactively change the weights of an already saved trained model?
   • No; a training or runtime process must use the changed guidance
   • Yes; all saved checkpoints instantly rewrite themselves
   • Yes; written words directly become new neural weights without execution
   • No model process can ever consume written principles
8. Which human comparison data did the original Constitutional AI RL experiments retain alongside AI harmlessness preferences?
   • Only human GPU-temperature measurements
   • No human comparison data of any kind
   • Human helpfulness comparisons
   • A mandatory human label for every harmlessness pair