SHIFT: Semantic Harmonization via Index-side Feature Transformation for Multilingual Information Retrieval
arXiv:2606.18801v1 Announce Type: cross Abstract: With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for global information access. MLIR enables users to retrieve semantically relevant documents from multilingual text collections using a single-language query. However, recent multilingual dense retrieval models often exhibit a strong preference for documents in the same language as the query. This leads to severe language bias, where top-ranked results are dominated by documents of specific languages, even wh
The proliferation of massive multilingual corpora and the increasing need for global information access make multilingual information retrieval a critical and actively researched area.
Improving multilingual information retrieval addresses language bias, allowing users to access semantically relevant documents across diverse linguistic datasets, which is crucial for global knowledge synthesis and AI development.
The proposed 'SHIFT' method aims to mitigate language bias in multilingual dense retrieval models, leading to more equitable and comprehensive search results across different languages.
- · Global information users
- · AI developers
- · Multilingual content platforms
- · International research collaborations
- · Monolingual information systems
- · Language-biased search algorithms
Multilingual search engines will provide more balanced and semantically relevant results across various languages.
This improvement could foster greater cross-cultural understanding and accelerate research by breaking down linguistic barriers to information.
Reduced language bias in information retrieval might inadvertently influence the development of more linguistically diverse and culturally nuanced AI models.
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Read at arXiv cs.AI