17 papers
Disentangled Contrastive Learning for Zero-Shot Multilingual Dense Retrieval
Chao Huang, Yufeng Chen, Changhao Guan +3
Multilingual dense retrieval aims to handle queries and documents across different languages based on a unified retriever model. The challenge lies in enabling robust retrieval tra…
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…
Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation
Rui Qi, Fengran Mo, Yufeng Chen +7
Multilingual retrieval-augmented generation (MRAG) requires models to effectively acquire and integrate beneficial external knowledge from multilingual collections. However, most e…
KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
Songming Zhang, Xue Zhang, Tong Zhang +3
Knowledge distillation (KD) is an essential technique to compress large language models (LLMs) into smaller ones. However, despite the distinct roles of the student model and the t…
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…
DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translation
Zhibo Man, Yuanmeng Chen, Yujie Zhang +1
Currently, Large Language Models (LLMs) have achieved remarkable results in machine translation. However, their performance in multi-domain translation (MDT) is less satisfactory,…