Cross-Lingual Retrieval-Augmented Generation for Multilingual Knowledge Discovery
Keywords:
cross-lingual retrieval; retrieval-augmented generation; multilingual knowledge discovery; semantic alignment; socio-technical systems; AI governanceAbstract
Cross-lingual retrieval-augmented generation has emerged as a promising approach for multilingual knowledge discovery because it combines the broad linguistic coverage of multilingual language models with the evidentiary grounding of external retrieval systems. Yet the movement from monolingual retrieval-augmented generation to cross-lingual operation is not a simple matter of translating queries or documents. It requires coordinated decisions about semantic alignment, index design, fusion strategies, latency budgets, storage infrastructure, evaluation protocols, and governance. This paper examines cross-lingual retrieval-augmented generation as a socio-technical system rather than a single model architecture. It analyzes how multilingual encoders, dense and sparse retrieval methods, cross-lingual transfer, and attention-based fusion shape the reliability and interpretability of generated knowledge. It also considers deployment in resource-constrained and high-stakes environments, where sustainability, robustness, and fairness are inseparable from technical performance. The discussion highlights structural trade-offs between recall and precision, between language coverage and computational cost, and between centralized control and local linguistic autonomy. It further explores policy implications related to language equity, data sovereignty, privacy, and accountability. The paper argues that multilingual knowledge discovery depends less on a single breakthrough model than on layered infrastructural choices that determine whose knowledge is retrievable, whose language is treated as first-class, and whose errors are visible. A research agenda is proposed around federated indexing, language-aware governance, robust cross-lingual evaluation, and sustainable deployment.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
