arXiv:2605.31171v1 Announce Type: cross Abstract: Multilingual Information Retrieval (MLIR) reflects real-world search environments in which queries and relevant documents may appear in different languages within a mixed-language corpus. However, existing embedding models are primarily optimized for Multi-Monolingual retrieval and their performance often degrades in MLIR settings. Moreover, directly applying conventional contrastive learning to MLIR can exacerbate language clustering and expose a trade-off between cross-lingual alignment and embedding uniformity. To address these limitations,
The increasing globalization of information and the prevalence of mixed-language data necessitate more effective multilingual retrieval systems.
Improving Multilingual Information Retrieval directly enhances the capability of AI systems to understand and process diverse global information landscapes, critical for many applications.
Existing embedding models' limitations in true multilingual retrieval are being directly addressed, potentially leading to more robust and accurate cross-lingual search and AI understanding.
- · Global internet users
- · Multinational corporations
- · AI-powered search engines
- · Cross-lingual data analysis platforms
- · Monolingual data platforms
- · Translation-reliant data approaches
Improved performance of AI systems in multilingual settings, leading to better understanding of diverse information.
Reduced language barriers in information access and knowledge sharing, fostering greater global collaboration.
Acceleration of AI model development that is inherently more robust to linguistic diversity, broadens AI application scope.
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Read at arXiv cs.AI