Youngjoon Jang

SHIFT effectively eliminates language bias in multilingual information retrieval, enhancing access to semantically relevant documents across diverse languages.

A simple rescaling of the MLM-head can turn unstable training runs into competitive sparse retrieval models, challenging the notion that bigger encoders alone drive performance.

Unlock high-performance sparse retrieval in any language: SemBridge's smart initialization closes the cross-lingual gap without sacrificing precision.