# Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task

arXiv: https://arxiv.org/abs/2504.03616 · HTML: https://mlsanity.com/paper/2504.03616/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation.
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Placed in

- Retrieval-augmented generation / [Generation and context integration](https://mlsanity.com/rag/generation-and-context-integration/)

## Builds on (most cited first, 17 of 17)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [Teaching language models to support answers with verified quotes](https://mlsanity.com/paper/2203.11147/) (2022)
- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering](https://mlsanity.com/paper/2007.15207/) (2020)
- [XOR QA: Cross-lingual Open-Retrieval Question Answering](https://mlsanity.com/paper/2010.11856/) (2020)
- [Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers](https://mlsanity.com/paper/2404.07220/) (2024)
- [An Open Dataset and Model for Language Identification](https://mlsanity.com/paper/2305.13820/) (2023)
- [Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages](https://mlsanity.com/paper/2210.09984/) (2022)
- [Retrieval-augmented generation in multilingual settings](https://mlsanity.com/paper/2407.01463/) (2024)
- [MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2410.13716/) (2024)

## Built on by (most cited first, 12 of 12)

- [CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Models](https://mlsanity.com/paper/2505.19864/) (2025)
- [Linguistic Nepotism: Trading-off Quality for Language Preference in Multilingual RAG](https://mlsanity.com/paper/2509.13930/) (2025)
- [Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations](https://mlsanity.com/paper/2504.04771/) (2025)
- [The Cross-Lingual Cost: Retrieval Biases in RAG over Arabic-English Corpora](https://mlsanity.com/paper/2507.07543/) (2025)
- [Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications](https://mlsanity.com/paper/2506.20815/) (2025)
- [Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion](https://mlsanity.com/paper/2601.02956/) (2026)
- [Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.14896/) (2026)
- [CroSearch-R1: Better Leveraging Cross-lingual Knowledge for Retrieval-Augmented Generation](https://mlsanity.com/paper/2604.25182/) (2026)
- [Retrieval or Representation? Reassessing Benchmark Gaps in Multilingual and Visually Rich RAG](https://mlsanity.com/paper/2603.04238/) (2026)
- [X-MADAM-RAG: Diagnosing and Handling Chinese-English Evidence Conflict in Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.12903/) (2026)
- [Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG](https://mlsanity.com/paper/2607.02966/) (2026)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)

## Abstract (arXiv)

Retrieval-augmented generation (RAG) has become a cornerstone of contemporary NLP, enhancing large language models (LLMs) by allowing them to access richer factual contexts through in-context retrieval. While effective in monolingual settings, especially in English, its use in multilingual tasks remains unexplored. This paper investigates the effectiveness of RAG across multiple languages by proposing novel approaches for multilingual open-domain question-answering. We evaluate the performance of various multilingual RAG strategies, including question-translation (tRAG), which translates questions into English before retrieval, and Multilingual RAG (MultiRAG), where retrieval occurs directly across multiple languages. Our findings reveal that tRAG, while useful, suffers from limited coverage. In contrast, MultiRAG improves efficiency by enabling multilingual retrieval but introduces inconsistencies due to cross-lingual variations in the retrieved content. To address these issues, we propose Crosslingual RAG (CrossRAG), a method that translates retrieved documents into a common language (e.g., English) before generating the response. Our experiments show that CrossRAG significantly enhances performance on knowledge-intensive tasks, benefiting both high-resource and low-resource languages.