Search papers, labs, and topics across Lattice.
Korea University
5
0
6
DART boosts reasoning accuracy by up to 22.5 points while slashing thinking token usage by over 50%, all without requiring labeled training data.
ACOER reduces token generation by over 60% while boosting accuracy, solving the reward collapse problem that plagues traditional efficiency training methods.
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.
