When is an Embedding Model More Promising than Another?
This provides practitioners with a tool to prioritize model trials without needing large datasets, though it is incremental as it builds on existing evaluation concepts.
The paper tackles the lack of a standardized framework for evaluating embedding models by proposing a unified, task-agnostic approach based on theoretical concepts of sufficiency and informativeness, which experimentally aligns with downstream task performance in NLP and molecular biology.
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.