本文提出了一种简单的方法，用于使用自由形式分类器（即CAIF采样）基于加权逻辑来控制文本生成。使用任意文本分类器，我们将语言模型逻辑的一小部分调整为指导文本生成，以远离分类器预测。我们试验了避免毒性和情感控制任务，并表明该方法在PPL和DESS准确度指标上基于生成的文本的外部分类器而显着优于最近的PPLM，GEDI和DEXPERTS。此外，与其他方法相比，它更容易实施和调整，并且限制和要求较少。

Reflections on glossy objects contain valuable and hidden information about the surrounding environment. By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e.g. from reflections on the human eye. However, this task is challenging because reflections depend jointly on object geometry, material properties, the 3D environment, and the observer viewing direction. Our approach converts glossy objects with unknown geometry into radiance-field cameras to image the world from the object's perspective. Our key insight is to convert the object surface into a virtual sensor that captures cast reflections as a 2D projection of the 5D environment radiance field visible to the object. We show that recovering the environment radiance fields enables depth and radiance estimation from the object to its surroundings in addition to beyond field-of-view novel-view synthesis, i.e. rendering of novel views that are only directly-visible to the glossy object present in the scene, but not the observer. Moreover, using the radiance field we can image around occluders caused by close-by objects in the scene. Our method is trained end-to-end on multi-view images of the object and jointly estimates object geometry, diffuse radiance, and the 5D environment radiance field.