Model selection for a given target task can be costly, as it may entail
extensive annotation of the quality of outputs of different models. We
introduce DiffUse, an efficient method to make an informed decision between
candidate text generation models. DiffUse reduces the required amount of
preference annotations, thus saving valuable time and resources in performing
evaluation. DiffUse intelligently selects instances by clustering embeddings
that represent the semantic differences between model outputs. Thus, it is able
to identify a subset of examples that are more informative for preference
decisions. Our method is model-agnostic, and can be applied to any text
generation model. Moreover, we propose a practical iterative approach for
dynamically determining how many instances to annotate. In a series of
experiments over hundreds of model pairs, we demonstrate that DiffUse can
dramatically reduce the required number of annotations -- by up to 75% -- while
maintaining high evaluation reliability.

DiffUse 是一种高效的方法，可在候选文本生成模型之间做出明智决策，降低了所需的偏好注释数量并提高了评估可靠性。

文本生成的标签有效模型选择

Label-Efficient Model Selection for Text Generation

Shape from Polarization (SfP) estimates surface normals using photos captured
at different polarizer rotations. Fundamentally, the SfP model assumes that
light is reflected either diffusely or specularly. However, this model is not
valid for many real-world surfaces exhibiting a mixture of diffuse and specular
properties. To address this challenge, previous methods have used a sequential
solution: first, use an existing algorithm to separate the scene into diffuse
and specular components, then apply the appropriate SfP model. In this paper,
we propose a new method that jointly uses viewpoint and polarization data to
holistically separate diffuse and specular components, recover refractive
index, and ultimately recover 3D shape. By involving the physics of
polarization in the separation process, we demonstrate competitive results with
a benchmark method, while recovering additional information (e.g. refractive
index).

本文提出了一种新的使用视点和极化数据来完整分离漫反射和镜面反射组件、恢复折射率和最终恢复三维形状的方法，在分离过程中涉及了极化的物理特性，证明了与基准方法有竞争力的结果，并恢复了其他信息（例如折射率）