# Towards Robust Ranker for Text Retrieval

arXiv: https://arxiv.org/abs/2206.08063 · HTML: https://mlsanity.com/paper/2206.08063/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 64 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 30 of 43)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2010.08191/) (2020)
- [Unsupervised Label Noise Modeling and Loss Correction](https://mlsanity.com/paper/1904.11238/) (2019)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [Combating noisy labels by agreement: A joint training method with co-regularization](https://mlsanity.com/paper/2003.02752/) (2020)
- [Overview of the TREC 2019 deep learning track](https://mlsanity.com/paper/2003.07820/) (2020)
- [Document Expansion by Query Prediction](https://mlsanity.com/paper/1904.08375/) (2019)
- [Decoupling "when to update" from "how to update"](https://mlsanity.com/paper/1706.02613/) (2017)
- [Sparse, Dense, and Attentional Representations for Text Retrieval](https://mlsanity.com/paper/2005.00181/) (2020)
- [Does label smoothing mitigate label noise?](https://mlsanity.com/paper/2003.02819/) (2020)
- [Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval](https://mlsanity.com/paper/2108.05540/) (2021)
- [Multi-Stage Document Ranking with BERT](https://mlsanity.com/paper/1910.14424/) (2019)
- [RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking](https://mlsanity.com/paper/2110.07367/) (2021)
- [Condenser: a Pre-training Architecture for Dense Retrieval](https://mlsanity.com/paper/2104.08253/) (2021)
- [Optimizing Dense Retrieval Model Training with Hard Negatives](https://mlsanity.com/paper/2104.08051/) (2021)
- [Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation](https://mlsanity.com/paper/2010.02666/) (2020)
- [SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval](https://mlsanity.com/paper/2109.10086/) (2021)

## Built on by (most cited first, 30 of 37)

- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [LLMs are Also Effective Embedding Models: An In-depth Overview](https://mlsanity.com/paper/2412.12591/) (2024)
- [Fine-Grained Distillation for Long Document Retrieval](https://mlsanity.com/paper/2212.10423/) (2022)
- [Large Language Models are Strong Zero-Shot Retriever](https://mlsanity.com/paper/2304.14233/) (2023)
- [MemoryMamba: Memory-Augmented State Space Model for Defect Recognition](https://mlsanity.com/paper/2405.03673/) (2024)
- [Task-Agnostic Detector for Insertion-Based Backdoor Attacks](https://mlsanity.com/paper/2403.17155/) (2024)
- [Literature-Grounded Novelty Assessment of Scientific Ideas](https://mlsanity.com/paper/2506.22026/) (2025)
- [GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval](https://mlsanity.com/paper/2310.20158/) (2023)
- [LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval](https://mlsanity.com/paper/2208.13661/) (2022)
- [Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2407.06992/) (2024)
- [KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation](https://mlsanity.com/paper/2401.08376/) (2024)
- [DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers](https://mlsanity.com/paper/2505.22584/) (2025)
- [Exploiting Diffusion Prior for Out-of-Distribution Detection](https://mlsanity.com/paper/2406.11105/) (2024)
- [Adam: Dense Retrieval Distillation with Adaptive Dark Examples](https://mlsanity.com/paper/2212.10192/) (2022)
- [Contextualizing Search Queries In-Context Learning for Conversational Rewriting with LLMs](https://mlsanity.com/paper/2502.15009/) (2025)
- [End-To-End Training and Testing Gamification Framework to Learn Human Highway Driving](https://mlsanity.com/paper/2404.10849/) (2024)
- [Optimizing Psychological Counseling with Instruction-Tuned Large Language Models](https://mlsanity.com/paper/2406.13617/) (2024)
- [Improving Visual Storytelling with Multimodal Large Language Models](https://mlsanity.com/paper/2407.02586/) (2024)
- [Large Vision-Language Models for Remote Sensing Visual Question Answering](https://mlsanity.com/paper/2411.10857/) (2024)
- [Multi-Facet Blending for Faceted Query-by-Example Retrieval](https://mlsanity.com/paper/2412.01443/) (2024)
- [Incomplete In-context Learning](https://mlsanity.com/paper/2505.07251/) (2025)
- [MPCAR: Multi-Perspective Contextual Augmentation for Enhanced Visual Reasoning in Large Vision-Language Models](https://mlsanity.com/paper/2508.12400/) (2025)
- [Analyzing Multi-Head Attention on Trojan BERT Models](https://mlsanity.com/paper/2406.16925/) (2024)
- [LLMs for Enhanced Agricultural Meteorological Recommendations](https://mlsanity.com/paper/2408.04640/) (2024)
- [Consultation on Industrial Machine Faults with Large language Models](https://mlsanity.com/paper/2410.03223/) (2024)
- [Leveraging Large Language Models for Comparative Literature Summarization with Reflective Incremental Mechanisms](https://mlsanity.com/paper/2412.02149/) (2024)
- [Reasoning-Aware Query-Focused Summarization over Multi-Table Data](https://mlsanity.com/paper/2412.08970/) (2024)
- [Leveraging Retrieval-Augmented Tags for Large Vision-Language Understanding in Complex Scenes](https://mlsanity.com/paper/2412.11396/) (2024)
- [Vision-Driven Prompt Optimization for Large Language Models in Multimodal Generative Tasks](https://mlsanity.com/paper/2501.02527/) (2025)
- [Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation](https://mlsanity.com/paper/2501.16467/) (2025)

## Abstract (arXiv)

A ranker plays an indispensable role in the de facto 'retrieval & rerank' pipeline, but its training still lags behind -- learning from moderate negatives or/and serving as an auxiliary module for a retriever. In this work, we first identify two major barriers to a robust ranker, i.e., inherent label noises caused by a well-trained retriever and non-ideal negatives sampled for a high-capable ranker. Thereby, we propose multiple retrievers as negative generators improve the ranker's robustness, where i) involving extensive out-of-distribution label noises renders the ranker against each noise distribution, and ii) diverse hard negatives from a joint distribution are relatively close to the ranker's negative distribution, leading to more challenging thus effective training. To evaluate our robust ranker (dubbed R2anker), we conduct experiments in various settings on the popular passage retrieval benchmark, including BM25-reranking, full-ranking, retriever distillation, etc. The empirical results verify the new state-of-the-art effectiveness of our model.