# A Thorough Examination on Zero-shot Dense Retrieval

arXiv: https://arxiv.org/abs/2204.12755 · HTML: https://mlsanity.com/paper/2204.12755/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 49 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 28 of 28)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [Large Dual Encoders Are Generalizable Retrievers](https://mlsanity.com/paper/2112.07899/) (2021)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2010.08191/) (2020)
- [Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling](https://mlsanity.com/paper/2104.06967/) (2021)
- [SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine](https://mlsanity.com/paper/1704.05179/) (2017)
- [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension](https://mlsanity.com/paper/1910.09753/) (2019)
- [RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking](https://mlsanity.com/paper/2110.07367/) (2021)
- [Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://mlsanity.com/paper/2010.08240/) (2020)
- [GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval](https://mlsanity.com/paper/2112.07577/) (2021)
- [Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?](https://mlsanity.com/paper/2110.06918/) (2021)
- [PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval](https://mlsanity.com/paper/2108.06027/) (2021)
- [The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes](https://mlsanity.com/paper/2012.14210/) (2020)
- [Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models](https://mlsanity.com/paper/2201.10582/) (2022)
- [LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval](https://mlsanity.com/paper/2203.06169/) (2022)
- [Neural Passage Retrieval with Improved Negative Contrast](https://mlsanity.com/paper/2010.12523/) (2020)

## Built on by (most cited first, 21 of 21)

- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022)
- [OpenTab: Advancing Large Language Models as Open-domain Table Reasoners](https://mlsanity.com/paper/2402.14361/) (2024)
- [In Defense of Cross-Encoders for Zero-Shot Retrieval](https://mlsanity.com/paper/2212.06121/) (2022)
- [COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning](https://mlsanity.com/paper/2210.15212/) (2022)
- [No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval](https://mlsanity.com/paper/2206.02873/) (2022)
- [Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2407.06992/) (2024)
- [Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey](https://mlsanity.com/paper/2208.03197/) (2022)
- [BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval](https://mlsanity.com/paper/2305.11052/) (2023)
- [Selecting which Dense Retriever to use for Zero-Shot Search](https://mlsanity.com/paper/2309.09403/) (2023)
- [Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models](https://mlsanity.com/paper/2404.08695/) (2024)
- [Zero-Shot Retrieval with Search Agents and Hybrid Environments](https://mlsanity.com/paper/2209.15469/) (2022)
- [Retrieval-based Disentangled Representation Learning with Natural Language Supervision](https://mlsanity.com/paper/2212.07699/) (2022)
- [RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering](https://mlsanity.com/paper/2502.13361/) (2025)
- [A Compressive Memory-based Retrieval Approach for Event Argument Extraction](https://mlsanity.com/paper/2409.09322/) (2024)
- [DREditor: An Time-efficient Approach for Building a Domain-specific Dense Retrieval Model](https://mlsanity.com/paper/2401.12540/) (2024)
- [Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings](https://mlsanity.com/paper/2506.08592/) (2025)
- [Revisiting Sparse Retrieval for Few-shot Entity Linking](https://mlsanity.com/paper/2310.12444/) (2023)
- [Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders](https://mlsanity.com/paper/2311.09765/) (2023)
- [A Representation Sharpening Framework for Zero Shot Dense Retrieval](https://mlsanity.com/paper/2511.05684/) (2025)
- [Generate to Understand for Representation](https://mlsanity.com/paper/2306.10056/) (2023)
- [Domain Adaptation of Multilingual Semantic Search -- Literature Review](https://mlsanity.com/paper/2402.02932/) (2024)

## Abstract (arXiv)

Recent years have witnessed the significant advance in dense retrieval (DR) based on powerful pre-trained language models (PLM). DR models have achieved excellent performance in several benchmark datasets, while they are shown to be not as competitive as traditional sparse retrieval models (e.g., BM25) in a zero-shot retrieval setting. However, in the related literature, there still lacks a detailed and comprehensive study on zero-shot retrieval. In this paper, we present the first thorough examination of the zero-shot capability of DR models. We aim to identify the key factors and analyze how they affect zero-shot retrieval performance. In particular, we discuss the effect of several key factors related to source training set, analyze the potential bias from the target dataset, and review and compare existing zero-shot DR models. Our findings provide important evidence to better understand and develop zero-shot DR models.