# Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models

arXiv: https://arxiv.org/abs/2201.10582 · HTML: https://mlsanity.com/paper/2201.10582/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2022.

## Built on by (most cited first, 26 of 26)

- [A Thorough Examination on Zero-shot Dense Retrieval](https://mlsanity.com/paper/2204.12755/) (2022)
- [Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers](https://mlsanity.com/paper/2207.07087/) (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)
- [PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods](https://mlsanity.com/paper/2402.13350/) (2024)
- [Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.02464/) (2025)
- [Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization](https://mlsanity.com/paper/2510.05038/) (2025)
- [textttMixGR: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity](https://mlsanity.com/paper/2407.10691/) (2024)
- [A Study on the Efficiency and Generalization of Light Hybrid Retrievers](https://mlsanity.com/paper/2210.01371/) (2022)
- [Selecting which Dense Retriever to use for Zero-Shot Search](https://mlsanity.com/paper/2309.09403/) (2023)
- [BLAZE: Cross-Language and Cross-Project Bug Localization via Dynamic Chunking and Hard Example Learning](https://mlsanity.com/paper/2407.17631/) (2024)
- [Zero-Shot Retrieval with Search Agents and Hybrid Environments](https://mlsanity.com/paper/2209.15469/) (2022)
- [HYRR: Hybrid Infused Reranking for Passage Retrieval](https://mlsanity.com/paper/2212.10528/) (2022)
- [Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion](https://mlsanity.com/paper/2506.04760/) (2025)
- [Logic Rules as Explanations for Legal Case Retrieval](https://mlsanity.com/paper/2403.01457/) (2024)
- [Compress, Gather, and Recompute: REFORMing Long-Context Processing in Transformers](https://mlsanity.com/paper/2506.01215/) (2025)
- [When Should Dense Retrievers Be Updated in Evolving Corpora? Detecting Out-of-Distribution Corpora Using GradNormIR](https://mlsanity.com/paper/2506.01877/) (2025)
- [Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain](https://mlsanity.com/paper/2409.01357/) (2024)
- [Content-based 3D Image Retrieval and a ColBERT-inspired Re-ranking for Tumor Flagging and Staging](https://mlsanity.com/paper/2507.17412/) (2025)
- [Document Optimization for Black-Box Retrieval via Reinforcement Learning](https://mlsanity.com/paper/2604.05087/) (2026)
- [Beyond Case Law: Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA](https://mlsanity.com/paper/2604.06173/) (2026)
- [LLM-Oriented Information Retrieval: A Denoising-First Perspective](https://mlsanity.com/paper/2605.00505/) (2026)
- [Domain Adaptation of Multilingual Semantic Search -- Literature Review](https://mlsanity.com/paper/2402.02932/) (2024)
- [MURR: Model Updating with Regularized Replay for Searching a Document Stream](https://mlsanity.com/paper/2504.10250/) (2025)
- [The Effect of Document Selection on Query-focused Text Analysis](https://mlsanity.com/paper/2604.12099/) (2026)
- [Evidence-in-the-Loop: Trace-Driven Optimization for Customer-Service LLM Agents](https://mlsanity.com/paper/2607.18039/) (2026)

## Abstract (arXiv)

The pre-trained language model (eg, BERT) based deep retrieval models achieved superior performance over lexical retrieval models (eg, BM25) in many passage retrieval tasks. However, limited work has been done to generalize a deep retrieval model to other tasks and domains. In this work, we carefully select five datasets, including two in-domain datasets and three out-of-domain datasets with different levels of domain shift, and study the generalization of a deep model in a zero-shot setting. Our findings show that the performance of a deep retrieval model is significantly deteriorated when the target domain is very different from the source domain that the model was trained on. On the contrary, lexical models are more robust across domains. We thus propose a simple yet effective framework to integrate lexical and deep retrieval models. Our experiments demonstrate that these two models are complementary, even when the deep model is weaker in the out-of-domain setting. The hybrid model obtains an average of 20.4% relative gain over the deep retrieval model, and an average of 9.54% over the lexical model in three out-of-domain datasets.