# Generative Query Expansion with Multilingual LLMs for Cross-Lingual Information Retrieval

arXiv: https://arxiv.org/abs/2511.19325 · HTML: https://mlsanity.com/paper/2511.19325/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 59 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 25 of 25)

- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [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)
- [MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering](https://mlsanity.com/paper/2007.15207/) (2020)
- [Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves](https://mlsanity.com/paper/2311.04205/) (2023)
- [XOR QA: Cross-lingual Open-Retrieval Question Answering](https://mlsanity.com/paper/2010.11856/) (2020)
- [On the Theoretical Limitations of Embedding-Based Retrieval](https://mlsanity.com/paper/2508.21038/) (2025)
- [A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias](https://mlsanity.com/paper/2404.00929/) (2024)
- [Query Expansion by Prompting Large Language Models](https://mlsanity.com/paper/2305.03653/) (2023)
- [Information Retrieval: Recent Advances and Beyond](https://mlsanity.com/paper/2301.08801/) (2023)
- [LAReQA: Language-agnostic answer retrieval from a multilingual pool](https://mlsanity.com/paper/2004.05484/) (2020)
- [Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval](https://mlsanity.com/paper/2311.05800/) (2023)
- [Generative Relevance Feedback with Large Language Models](https://mlsanity.com/paper/2304.13157/) (2023)
- [Exploring the Best Practices of Query Expansion with Large Language Models](https://mlsanity.com/paper/2401.06311/) (2024)
- [LLM-based Query Expansion Fails for Unfamiliar and Ambiguous Queries](https://mlsanity.com/paper/2505.12694/) (2025)
- [XRAG: Cross-lingual Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.10089/) (2025)
- [Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion](https://mlsanity.com/paper/2506.04760/) (2025)
- [Query Expansion in the Age of Pre-trained and Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2509.07794/) (2025)
- [Beyond the limitation of a single query: Train your LLM for query expansion with Reinforcement Learning](https://mlsanity.com/paper/2510.10009/) (2025)
- [Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling](https://mlsanity.com/paper/2403.03516/) (2024)
- [Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment](https://mlsanity.com/paper/2507.11042/) (2025)

## Abstract (arXiv)

Query expansion is the reformulation of a user query by adding semantically related information, and is an essential component of monolingual and cross-lingual information retrieval used to ensure that relevant documents are not missed. Recently, multilingual large language models (mLLMs) have shifted query expansion from semantic augmentation with synonyms and related words to pseudo-document generation. Pseudo-documents both introduce additional relevant terms and bridge the gap between short queries and long documents, which is particularly beneficial in dense retrieval. This study evaluates recent mLLMs and fine-tuned variants across several generative expansion strategies to identify factors that drive cross-lingual retrieval performance. Results show that query length largely determines which prompting technique is effective, and that more elaborate prompts often do not yield further gains. Substantial linguistic disparities persist: cross-lingual query expansion can produce the largest improvements for languages with the weakest baselines, yet retrieval is especially poor between languages written in different scripts. Fine-tuning is found to lead to performance gains only when the training and test data are of similar format. These outcomes underline the need for more balanced multilingual and cross-lingual training and evaluation resources.