2 citations · 5 across the 14 of their papers we have counts for
15 papers
Don't Peek at the Answer: Outcome-Masked Group Relative Policy Optimization for Label-Free RLVR
Yongshi Ye, Liang Zhang, Yidong Chen +2
Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning but typically relies on ground-truth (GT) answers, limiting scalability. Voting-based label-free RLVR r…
PAMT: Process-Aligned Reinforcement Learning for Multi-Domain Machine Translation
Yongshi Ye, Biao Fu, Chongxuan Huang +2
Multi-domain machine translation (MDMT) requires more than fluent generation: it demands domain-sensitive translation decisions such as domain disambiguation, terminology control,…
Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation
Yongshi Ye, Biao Fu, Chongxuan Huang +2
Multi-domain machine translation (MDMT) poses a unique challenge due to varying levels of linguistic complexity across domains. Inspired by human translators' ability to adapt reas…
JURY-RL: Votes Propose, Proofs Dispose for Label-Free RLVR
Xinjie Chen, Biao Fu, Jing Wu +4
Reinforcement learning with verifiable rewards (RLVR) enhances the reasoning of large language models (LLMs), but standard RLVR often depends on human-annotated answers or carefull…
UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory
Yongshi Ye, Hui Jiang, Feihu Jiang +7
Self-evolving memory serves as the trainable parameters for Large Language Models (LLMs)-based agents, where extraction (distilling insights from experience) and management (updati…
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 +5
Simultaneous speech translation (SimulST) produces translations incrementally while processing partial speech input. Although large language models (LLMs) have shown strong capabil…
Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignment
Rui Zhao, Liang Zhang, Biao Fu +3
Sign language translation (SLT) aims to convert continuous sign language videos into textual sentences. As a typical multi-modal task, there exists an inherent modality gap between…
Layer-wise Representation Fusion for Compositional Generalization
Yafang Zheng, Lei Lin, Shuangtao Li +6
Existing neural models are demonstrated to struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compositions of seen components…
Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization
Lei Lin, Shuangtao Li, Yafang Zheng +4
Recent studies have shown that sequence-to-sequence (seq2seq) models struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compo…
Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference
Biao Fu, Minpeng Liao, Kai Fan +4
A popular approach to streaming speech translation is to employ a single offline model with a wait-k policy to support different latency requirements, which is simpler than trainin…
A Token-level Contrastive Framework for Sign Language Translation
Biao Fu, Peigen Ye, Liang Zhang +4
Sign Language Translation (SLT) is a promising technology to bridge the communication gap between the deaf and the hearing people. Recently, researchers have adopted Neural Machine…