Embedders play a central role in machine learning, projecting any object into
numerical representations that can, in turn, be leveraged to perform various
downstream tasks. The evaluation of embedding models typically depends on
domain-specific empirical approaches utilizing downstream tasks, primarily
because of the lack of a standardized framework for comparison. However,
acquiring adequately large and representative datasets for conducting these
assessments is not always viable and can prove to be prohibitively expensive
and time-consuming. In this paper, we present a unified approach to evaluate
embedders. First, we establish theoretical foundations for comparing embedding
models, drawing upon the concepts of sufficiency and informativeness. We then
leverage these concepts to devise a tractable comparison criterion (information
sufficiency), leading to a task-agnostic and self-supervised ranking procedure.
We demonstrate experimentally that our approach aligns closely with the
capability of embedding models to facilitate various downstream tasks in both
natural language processing and molecular biology. This effectively offers
practitioners a valuable tool for prioritizing model trials.

本研究提出了一种统一的评估嵌入模型的方法，通过建立理论基础并利用信息充分性的概念进行比较，为自动排序过程提供了一种可操作的标准，展示了实验证明该方法与嵌入模型在自然语言处理和分子生物学中支持各种下游任务的能力密切相关，为实践者提供了一种有价值的模型试验优先级工具。