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：在训练强大的AI系统执行复杂任务时，提供对优化过程鲁棒的训练信号可能具有挑战性。其中一个担忧是“测量篡改”（measurement tampering），即AI系统操纵多项测量结果以营造良好结果的假象，而非真正实现预期目标。在本研究中，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测量篡改检测技术的效果。具体而言，给定一组文本输入和旨在判断是否发生某种结果的测量值，以及一个能够准确预测测量值的基础模型，目标是判断那些所有测量值均指示结果发生的样本是否确实发生了该结果，还是由测量篡改导致。我们验证了在大多数数据集上优于简单基准的技术，但尚未达到最佳性能。我们认为，无论是技术还是数据集都存在显著的改进空间，并对未来应对测量篡改的研究充满期待。