When training powerful AI systems to perform complex tasks, it may be
challenging to provide training signals which are robust to optimization. One
concern is measurement tampering, where the AI system manipulates multiple
measurements to create the illusion of good results instead of achieving the
desired outcome. In this work, we build four new text-based datasets to
evaluate measurement tampering detection techniques on large language models.
Concretely, given sets of text inputs and measurements aimed at determining if
some outcome occurred, as well as a base model able to accurately predict
measurements, the goal is to determine if examples where all measurements
indicate the outcome actually had the outcome occur, or if this was caused by
measurement tampering. We demonstrate techniques that outperform simple
baselines on most datasets, but don't achieve maximum performance. We believe
there is significant room for improvement for both techniques and datasets, and
we are excited for future work tackling measurement tampering.

针对大型语言模型，建立四个新的基于文本的数据集，评估测量篡改检测技术，展示了超越简单基准的技术，并表明在技术和数据集方面仍有改进的空间。