MIMO: Multilingual Information Retrieval via Monolingual Objectives
Read the original on arXiv AI →MIMO: Multilingual Information Retrieval via Monolingual Objectives proposes a two‑stage framework that first aligns a student model to a stable English semantic space using knowledge distillation, then jointly optimizes distillation and cross‑lingual contrastive learning to improve retrieval discrimination while preserving alignment. The approach addresses language clustering and the trade‑off between cross‑lingual alignment and embedding uniformity, outperforming existing cross‑lingual training baselines on both multilingual and multi‑monolingual benchmarks. MIMO also remains competitive with larger off‑the‑shelf models and its alignment‑uniformity analysis clarifies the distinct roles of the two loss components. whyItMatters":"The study demonstrates a practical method to enhance multilingual information retrieval performance by balancing alignment and uniformity, which is crucial for real‑world search environments where queries and documents span multiple languages."
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.