Enhancing Lexicon-Based Text Embeddings with Large Language Models

Recent large language models (LLMs) have demonstrated exceptional performance on general-purpose text embedding tasks. While dense embeddings have dominated related research, we introduce the first lexicon-based embeddings (LENS) leveraging LLMs that achieve competitive performance on these tasks. LENS consolidates the vocabulary space through token embedding clustering to handle the issue of token redundancy in LLM vocabularies. To further improve performance, we investigate bidirectional attention and various pooling strategies. Specifically, LENS simplifies lexical matching with redundant vocabularies by assigning each dimension to a specific token cluster, where semantically similar tokens are grouped together. Extensive experiments demonstrate that LENS outperforms dense embeddings on the Massive Text Embedding Benchmark (MTEB), delivering compact representations with dimensionality comparable to dense counterparts. Furthermore, LENS inherently supports efficient embedding dimension pruning without any specialized objectives like Matryoshka Representation Learning. Notably, combining LENS with dense embeddings achieves state-of-the-art performance on the retrieval subset of MTEB (i.e., BEIR).