# Towards Best Practices for Training Multilingual Dense Retrieval Models

arXiv: https://arxiv.org/abs/2204.02363 · HTML: https://mlsanity.com/paper/2204.02363/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 73 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 30 of 46)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [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)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://mlsanity.com/paper/2105.13626/) (2021)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://mlsanity.com/paper/2112.01488/) (2021)
- [Large Dual Encoders Are Generalizable Retrievers](https://mlsanity.com/paper/2112.07899/) (2021)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019)
- [Pretrained Transformers for Text Ranking: BERT and Beyond](https://mlsanity.com/paper/2010.06467/) (2020)
- [Simple, Scalable Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1909.08478/) (2019)
- [Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling](https://mlsanity.com/paper/2104.06967/) (2021)
- [Multi-Stage Document Ranking with BERT](https://mlsanity.com/paper/1910.14424/) (2019)
- [Cross-Lingual Ability of Multilingual BERT: An Empirical Study](https://mlsanity.com/paper/1912.07840/) (2019)
- [Emerging Cross-lingual Structure in Pretrained Language Models](https://mlsanity.com/paper/1911.01464/) (2019)

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

- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Zero-Shot Listwise Document Reranking with a Large Language Model](https://mlsanity.com/paper/2305.02156/) (2023)
- [Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages](https://mlsanity.com/paper/2210.09984/) (2022)
- [XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2210.13693/) (2022)
- [Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever](https://mlsanity.com/paper/2408.16672/) (2024)
- [Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval](https://mlsanity.com/paper/2311.05800/) (2023)
- [AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages](https://mlsanity.com/paper/2305.06897/) (2023)
- [ArabicaQA: A Comprehensive Dataset for Arabic Question Answering](https://mlsanity.com/paper/2403.17848/) (2024)
- ["Knowing When You Don't Know": A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation](https://mlsanity.com/paper/2312.11361/) (2023)
- [False Friends Are Not Foes: Investigating Vocabulary Overlap in Multilingual Language Models](https://mlsanity.com/paper/2509.18750/) (2025)
- [Milco: Learned Sparse Retrieval Across Languages via a Multilingual Connector](https://mlsanity.com/paper/2510.00671/) (2025)
- [Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages](https://mlsanity.com/paper/2312.16159/) (2023)
- [Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models](https://mlsanity.com/paper/2407.05502/) (2024)
- [Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval](https://mlsanity.com/paper/2304.01019/) (2023)
- [Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval](https://mlsanity.com/paper/2302.01626/) (2023)
- [On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey](https://mlsanity.com/paper/2507.20783/) (2025)
- [All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG](https://mlsanity.com/paper/2604.20199/) (2026)
- [Synergistic Approach for Simultaneous Optimization of Monolingual, Cross-lingual, and Multilingual Information Retrieval](https://mlsanity.com/paper/2408.10536/) (2024)
- [CLIRudit: Cross-Lingual Information Retrieval of Scientific Documents](https://mlsanity.com/paper/2504.16264/) (2025)
- [Disentangled Contrastive Learning for Zero-Shot Multilingual Dense Retrieval](https://mlsanity.com/paper/2608.02189/) (2026)
- [Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques](https://mlsanity.com/paper/2302.07010/) (2023)
- [Domain Adaptation of Multilingual Semantic Search -- Literature Review](https://mlsanity.com/paper/2402.02932/) (2024)
- [Unsupervised Text Representation Learning via Instruction-Tuning for Zero-Shot Dense Retrieval](https://mlsanity.com/paper/2409.16497/) (2024)
- [Dynamic Injection of Entity Knowledge into Dense Retrievers](https://mlsanity.com/paper/2507.03922/) (2025)
- [AmharicIR+Instr: A Two-Dataset Resource for Neural Retrieval and Instruction Tuning](https://mlsanity.com/paper/2602.09914/) (2026)
- [The Multilingual Curse at the Retrieval Layer: Evidence from Amharic](https://mlsanity.com/paper/2605.24556/) (2026)

## Abstract (arXiv)

Dense retrieval models using a transformer-based bi-encoder design have emerged as an active area of research. In this work, we focus on the task of monolingual retrieval in a variety of typologically diverse languages using one such design. Although recent work with multilingual transformers demonstrates that they exhibit strong cross-lingual generalization capabilities, there remain many open research questions, which we tackle here. Our study is organized as a "best practices" guide for training multilingual dense retrieval models, broken down into three main scenarios: where a multilingual transformer is available, but relevance judgments are not available in the language of interest; where both models and training data are available; and, where training data are available not but models. In considering these scenarios, we gain a better understanding of the role of multi-stage fine-tuning, the strength of cross-lingual transfer under various conditions, the usefulness of out-of-language data, and the advantages of multilingual vs. monolingual transformers. Our recommendations offer a guide for practitioners building search applications, particularly for low-resource languages, and while our work leaves open a number of research questions, we provide a solid foundation for future work.