vanta-research/grounded-meta-awareness

Grounded Meta-Awareness Dataset是一个精选的对话示例数据集，包含1,187个示例，展示了关于AI能力、局限性和本质的诚实、校准的自我意识。该数据集旨在微调语言模型，使其能够准确讨论自身功能，避免过度宣称或不必要的回避。数据集训练模型以深思熟虑和准确的方式回答关于其工作原理、能做和不能做的事情以及关于意识、情感和理解等哲学问题。示例展示了平衡的方法：技术上准确而不轻视，诚实承认局限而不过度回避，哲学上参与而不做出无根据的宣称。关键特征包括技术准确性、诚实不确定性、校准信心、实践基础和详细解释。数据集设计用于监督微调（SFT）语言模型，以提高准确自我描述能力、对哲学问题的诚实回答、抵抗过度宣称和过度回避的能力，以及清晰沟通AI系统实际工作原理的能力。

The Grounded Meta-Awareness Dataset is a curated conversational example dataset containing 1,187 instances that demonstrate honest, calibrated self-awareness regarding AI's capabilities, limitations and nature. This dataset is designed for fine-tuning language models to enable them to accurately discuss their own functions, avoid overclaiming and unnecessary evasion. It trains models to answer questions about how they operate, what they can and cannot do, as well as philosophical topics such as consciousness, emotion and understanding in a thoughtful and accurate manner. The examples showcase a balanced approach: balancing technical accuracy without being dismissive, honest acknowledgment of limitations without excessive evasion, and philosophical engagement without making unwarranted claims. Key features include technical accuracy, honest recognition of uncertainty, calibrated confidence, practical grounding, and detailed explanations. The dataset is intended for supervised fine-tuning (SFT) of language models, to improve their accurate self-description capabilities, honest responses to philosophical questions, resistance to overclaiming and over-evasion, and ability to clearly communicate the actual working principles of AI systems.