Efficient Sentence Representation Learning via Knowledge Distillation with Maximum Coding Rate Reduction

Abstract

Addressing the demand for effective sentence representation in natural language inference problems, this paper explores the utility of pre-trained large language models in computing such representations. Although these models generate high-dimensional sentence embeddings, a noticeable performance disparity arises when they are compared to smaller models. The hardware limitations concerning space and time necessitate the use of smaller, distilled versions of large language models. In this study, we investigate the knowledge distillation of Sentence-BERT, a sentence representation model, by introducing an additional projection layer trained on the novel Maximum Coding Rate Reduction (MCR²) objective designed for general-purpose manifold clustering. Our experiments demonstrate that the distilled language model, with reduced complexity and sentence embedding size, can achieve comparable results on semantic retrieval benchmarks, providing a promising solution for practical applications.