mlsanityData to 14 Oct 2026

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

arXiv 2205.10569 ↗First posted 21 May 2022cs.IR cs.CLcited by 20

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Abstract (arXiv)

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.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 22 of its 28 references that are in the map. First posted on arXiv 21 May 2022.

Builds on

22 of 28 references.

References · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2016Layer Normalizationcited by 5,669
  5. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
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  8. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 849
  9. 2019Passage Re-ranking with BERTcited by 645
  10. 2017Generative Adversarial Networks: An Overviewcited by 590
  11. 2021Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrievalcited by 203
  12. 2021RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-rankingcited by 176
10 more
  1. 2021Condenser: a Pre-training Architecture for Dense Retrievalcited by 162
  2. 2020Embedding-based Retrieval in Facebook Searchcited by 156
  3. 2017A Deep Relevance Matching Model for Ad-hoc Retrievalcited by 126
  4. 2021Adversarial Retriever-Ranker for dense text retrievalcited by 83
  5. 2021Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipelinecited by 65
  6. 2022ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrievalcited by 41
  7. 2021Pre-trained Language Model for Web-scale Retrieval in Baidu Searchcited by 31
  8. 2021B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrievalcited by 25
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  10. 2021YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Rankercited by 1

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Citing papers per year: 2022: 7; 2023: 3; 2024: 4; 2025: 4; 2026: 2.

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  1. 2024mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrievalcited by 236
  2. 2022RankT5: Fine-Tuning T5 for Text Ranking with Ranking Lossescited by 115
  3. 2022In Defense of Cross-Encoders for Zero-Shot Retrievalcited by 21
  4. 2022ConTextual Masked Auto-Encoder for Dense Passage Retrievalcited by 16
  5. 2023ESGReveal: An LLM-based approach for extracting structured data from ESG reportscited by 15
  6. 2025GRADA: Graph-based Reranking against Adversarial Documents Attackcited by 10
  7. 2022Query-as-context Pre-training for Dense Passage Retrievalcited by 9
  8. 2022HYRR: Hybrid Infused Reranking for Passage Retrievalcited by 6
  9. 2025ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Rerankingcited by 6
  10. 2023CoT-MAE v2: Contextual Masked Auto-Encoder with Multi-view Modeling for Passage Retrievalcited by 5
  11. 2024KET-QA: A Dataset for Knowledge Enhanced Table Question Answeringcited by 5
  12. 2022Retrieval Oriented Masking Pre-training Language Model for Dense Passage Retrievalcited by 4
8 more
  1. 2022Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typingcited by 4
  2. 2024UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrievalcited by 3
  3. 2024Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrievalcited by 2
  4. 2025OrdRankBen: A Novel Ranking Benchmark for Ordinal Relevance in NLPcited by 2
  5. 2023Hybrid Retrieval and Multi-stage Text Ranking Solution at TREC 2022 Deep Learning Trackcited by 0
  6. 2025CREST: Improving Interpretability and Effectiveness of Troubleshooting at Ericsson through Criterion-Specific Trouble Report Retrievalcited by 0
  7. 2026Understand and Accelerate Memory Processing Pipeline for Large Language Model Inferencecited by 0
  8. 2026Checking Fact with Better Retrieval: Dynamic Contrastive Learning for Evidence Retrievalcited by 0