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 \textit{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 occurred 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.

翻译：当训练强大的AI系统执行复杂任务时，提供对抗优化稳健的训练信号可能颇具挑战性。其中一个问题是\textit{测量篡改}（measurement tampering），即AI系统操纵多个测量值以制造良好结果的假象，而非真正实现预期目标。在本工作中，我们构建了四个新的基于文本的数据集，用于评估大型语言模型上的测量篡改检测技术。具体而言，给定旨在判断某些结果是否发生的文本输入与测量值集合，以及能够准确预测测量值的基础模型，目标是判断所有测量值均指示结果发生的样例中，结果是否真实发生，还是由测量篡改导致。我们展示了在多数数据集上优于简单基线的方法，但并未达到最优性能。我们认为，无论是技术方法还是数据集方面均存在显著改进空间，并对未来针对测量篡改的研究充满期待。