When is an Embedding Model More Promising than Another?

TL;DR

This paper introduces a theoretical and practical framework for comparing embedding models using information sufficiency, enabling task-agnostic evaluation aligned with downstream task performance.

Contribution

It provides a unified, theoretical basis for embedding comparison and a self-supervised ranking method that does not rely on large labeled datasets.

Findings

The approach correlates well with downstream task performance.

It is applicable to NLP and molecular biology embeddings.

Offers a practical tool for model prioritization.

Abstract

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.