# HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking

Source: [HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking](<https://www.sota2.com/research/paper/68fa6c971e209131cea34b6f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T04:15:25.869Z

Authors: Yanzhao Zhang, Dingkun Long, Guangwei Xu, Pengjun Xie

Published: 2022-05-21T11:38:33.000Z

## Abstract

Deep pre-trained language models (e,g. BERT) are effective at large-scale text retrieval task. Existing text retrieval systems with state-of-the-art performance usually adopt a retrieve-then-reranking architecture due to the high computational cost of pre-trained language models and the large corpus size. Under such a multi-stage architecture, previous studies mainly focused on optimizing single stage of the framework thus improving the overall retrieval performance. However, how to directly couple multi-stage features for optimization has not been well studied. In this paper, we design Hybrid List Aware Transformer Reranking (HLATR) as a subsequent reranking module to incorporate both retrieval and reranking stage features. HLATR is lightweight and can be easily parallelized with existing text retrieval systems so that the reranking process can be performed in a single yet efficient processing. Empirical experiments on two large-scale text retrieval datasets show that HLATR can efficiently improve the ranking performance of existing multi-stage text retrieval methods.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Text Retrieval on MS MARCO Passage](<https://www.sota2.com/research/sota/text-retrieval-on-ms-marco-passage.md>): Text Retrieval; MS MARCO Passage; MRR@10: 0.437.

- [Text Retrieval on MS MARCO Document](<https://www.sota2.com/research/sota/text-retrieval-on-ms-marco-document.md>): Text Retrieval; MS MARCO Document; MRR@10: 45.9.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.10569>)

[PDF](<https://arxiv.org/pdf/2205.10569.pdf>)

[Implementation](<https://github.com/Alibaba-NLP/HLATR>)