# Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking

arXiv: https://arxiv.org/abs/2203.15328 · HTML: https://mlsanity.com/paper/2203.15328/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2022.

## Built on by (most cited first, 3 of 3)

- [Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion](https://mlsanity.com/paper/2506.04760/) (2025)
- [MEMORY-VQ: Compression for Tractable Internet-Scale Memory](https://mlsanity.com/paper/2308.14903/) (2023)
- [Where Post-Training Quantization Breaks Text Embedders: A Measured Map Across Four Embedder Families](https://mlsanity.com/paper/2609.16391/) (2026)

## Abstract (arXiv)

Transformer based re-ranking models can achieve high search relevance through context-aware soft matching of query tokens with document tokens. To alleviate runtime complexity of such inference, previous work has adopted a late interaction architecture with pre-computed contextual token representations at the cost of a large online storage. This paper proposes contextual quantization of token embeddings by decoupling document-specific and document-independent ranking contributions during codebook-based compression. This allows effective online decompression and embedding composition for better search relevance. This paper presents an evaluation of the above compact token representation model in terms of relevance and space efficiency.