1 citations · 1 across the 4 of their papers we have counts for
5 papers
From Neurons to Semantics: Evaluating Cross-Linguistic Alignment Capabilities of Large Language Models via Neurons Alignment
Chongxuan Huang, Yongshi Ye, Biao Fu +2
Large language models (LLMs) have demonstrated remarkable multilingual capabilities, however, how to evaluate cross-lingual alignment remains underexplored. Existing alignment benc…
From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization
Xinjie Chen, Minpeng Liao, Guoxin Chen +4
Reinforcement learning with verifiable rewards (RLVR) has recently advanced the reasoning capabilities of large language models (LLMs). While prior work has emphasized algorithmic…
LLMs Can Achieve High-quality Simultaneous Machine Translation as Efficiently as Offline
Biao Fu, Minpeng Liao, Kai Fan +4
When the complete source sentence is provided, Large Language Models (LLMs) perform excellently in offline machine translation even with a simple prompt "Translate the following se…
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation
Yongshi Ye, Biao Fu, Chongxuan Huang +2
Large language models (LLMs) have demonstrated strong performance in general-purpose machine translation, but their effectiveness in complex, domain-sensitive translation tasks rem…
Efficient and Adaptive Simultaneous Speech Translation with Fully Unidirectional Architecture
Biao Fu, Donglei Yu, Minpeng Liao +4
Simultaneous speech translation (SimulST) produces translations incrementally while processing partial speech input. Although large language models (LLMs) have showcased strong cap…