2D Matryoshka Sentence Embeddings

TL;DR

This paper introduces 2D Matryoshka Sentence Embeddings, a flexible model that adaptively adjusts embedding sizes and Transformer layers, improving efficiency and applicability across diverse NLP tasks.

Contribution

It proposes a novel 2D embedding model supporting elastic sizes and layers, enhancing flexibility and efficiency over previous fixed-layer, fixed-size methods.

Findings

Achieves comparable accuracy with smaller embeddings

Supports dynamic adjustment of layers and sizes

Demonstrates effectiveness on STS and downstream tasks

Abstract

Common approaches rely on fixed-length embedding vectors from language models as sentence embeddings for downstream tasks such as semantic textual similarity (STS). Such methods are limited in their flexibility due to unknown computational constraints and budgets across various applications. Matryoshka Representation Learning (MRL) \cite{aditya2022matryoshka} encodes information at finer granularities, i.e., with lower embedding dimensions, to adaptively accommodate \emph{ad hoc} tasks. Similar accuracy can be achieved with a smaller embedding size, leading to speedups in downstream tasks. Despite its improved efficiency, MRL still requires traversing all Transformer layers before obtaining the embedding, which remains the dominant factor in time and memory consumption. This prompts consideration of whether the fixed number of Transformer layers affects representation quality and whether using intermediate layers for sentence representation is feasible. In this paper, we introduce a novel sentence embedding model called \textit{Two-dimensional Matryoshka Sentence Embedding} (2DMSE)\footnote{Our code is available at \url{https://github.com/SeanLee97/AnglE/blob/main/README_2DMSE.md}.}. It supports elastic settings for both embedding sizes and Transformer layers, offering greater flexibility and efficiency than MRL. We conduct extensive experiments on STS tasks and downstream applications. The experimental results demonstrate the effectiveness of our proposed model in dynamically supporting different embedding sizes and Transformer layers, allowing it to be highly adaptable to various scenarios.