Bridging Language Gaps: Advances In Cross-lingual Information Retrieval With Multilingual Llms

Abstract

Cross-lingual information retrieval (CLIR) addresses the challenge of retrieving relevant documents written in languages different from that of the original query. Research in this area has typically framed the task as monolingual retrieval augmented by translation, treating retrieval methods and cross-lingual capabilities in isolation. Both monolingual and cross-lingual retrieval usually follow a pipeline of query expansion, ranking, re-ranking and, increasingly, question answering. Recent advances, however, have shifted from translation-based methods toward embedding-based approaches and leverage multilingual large language models (LLMs), for which aligning representations across languages remains a central challenge. The emergence of cross-lingual embeddings and multilingual LLMs has introduced a new paradigm, offering improved retrieval performance and enabling answer generation. This survey provides a comprehensive overview of developments from early translation-based methods to s

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keygoworek2025bridging

Related papers

• What Drives Cross-lingual Ranking? Retrieval Approaches With Multilingual Language Models (2025)0.00
• Transforming Llms Into Cross-modal And Cross-lingual Retrieval Systems (2024)4.52
• On Cross-lingual Retrieval With Multilingual Text Encoders (2021)10.35
• Mm-embed: Universal Multimodal Retrieval With Multimodal Llms (2024)0.00
• CL2CM: Improving Cross-lingual Cross-modal Retrieval Via Cross-lingual Knowledge Transfer (2023)8.60
• Clirudit: Cross-lingual Information Retrieval Of Scientific Documents (2025)0.00
• SLQ: Bridging Modalities Via Shared Latent Queries For Retrieval With Frozen Mllms (2026)0.00
• Neuclirbench: A Modern Evaluation Collection For Monolingual, Cross-language, And Multilingual Information Retrieval (2025)0.00