activity
20192026
most citedGCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling

10 citations · 19 across the 25 of their papers we have counts for

collaborators
Showing cs.CLShow all

29 papers · 1 filter

cs.CL2026

CroSearch-R1: Better Leveraging Cross-lingual Knowledge for Retrieval-Augmented Generation

Rui Qi, Fengran Mo, Sijin Lu +3

A multilingual collection may contain useful knowledge in other languages to supplement and correct the facts in the original language for Retrieval-Augmented Generation (RAG). How…

cs.CL2025

SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models

Rui Qi, Zhibo Man, Yufeng Chen +3

Recent developments have enabled Large Language Models (LLMs) to engage in complex reasoning tasks through deep thinking. However, the capacity of reasoning has not been successful…

cs.CL2025

Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning

Xue Zhang, Yunlong Liang, Fandong Meng +5

Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the ``think-then-answer'' paradigm, which enhances both accuracy and inter…

cs.CL2025

CM-Align: Consistency-based Multilingual Alignment for Large Language Models

Xue Zhang, Yunlong Liang, Fandong Meng +4

Current large language models (LLMs) generally show a significant performance gap in alignment between English and other languages. To bridge this gap, existing research typically…

cs.CL2025

Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts

Xue Zhang, Yunlong Liang, Fandong Meng +4

Continually expanding new languages for existing large language models (LLMs) is a promising yet challenging approach to building powerful multilingual LLMs. The biggest challenge…

cs.CL2025

A Dual-Space Framework for General Knowledge Distillation of Large Language Models

Xue Zhang, Songming Zhang, Yunlong Liang +4

Knowledge distillation (KD) is a promising solution to compress large language models (LLMs) by transferring their knowledge to smaller models. During this process, white-box KD me…